Key takeaways

Artificial intelligence can optimize and improve customer-facing systems or processes for businesses that provide customer service or a support function like a customer relationship management (CRM) system to customers. A call center is specifically created to support customers for a company that requires satisfied customers to be successful. Artificial intelligence enhances the abilities of a customer service or support function to meet or exceed the customer support or service requirement.

What is an AI call center?

An artificial intelligence (AI) call center uses AI technologies like machine language (ML), voice recognition, and natural language processing (NLP) to improve the customers’ experiences by automating and optimizing routine tasks, such as intelligent routing calls to the right agent, answering FAQs, and scheduling appointments.

These routine tasks free up call agents to address outlier issues like identifying the root cause of a recurring problem, following up with a customer to ensure the issue is resolved, or answering specific questions about service, products, or billing. Artificial intelligence reduces wait times and provides faster response times, improving customer satisfaction.

Benefits and challenges of AI in call centers

Benefits of AI

The artificial intelligence resources used in a business function or process typically improve a business’s decision-making using data-driven insight. A call center using AI capabilities improves responsiveness and tailors interactions to meet customer needs. Using AI technologies is beneficial to call centers by enhancing the following functions or processes:

• Predictive analytics: AI can predict customer behavior for a specific season by analyzing the previous year’s historical seasonal data or addressing a potential issue before it becomes problematic through an outreach problem resolution effort.
• Cost reduction: Automating routine tasks reduces the workforce needed in a call center
• Increased efficiency: AI’s ability to handle routine tasks allows call agents to focus on complex issues, reducing resolution times and improving productivity.
• Improved customer service: AI-powered Chatbots and virtual assistants provide immediate assistance and can answer basic questions 24/7.
• Personalized customer experiences: AI can tailor a specific response to customers by analyzing their data and providing a recommendation or suggestion that meets their needs.
• Data-driven insight: AI can analyze historical call records and customer interactions to identify patterns or trends a business can use to make data-driven decisions.

Challenges of AI

The potential benefits of AI are significant, but it does come with a number of steep, but manageable, challenges.

• Natural Language Processing (NLP) misinterpretation: Due to a mispronounced word or slang, NLP can send a customer an incorrect response.
• Lack of human empathy: AI responses cannot detect emotions and are void of emotional reactions to a frustrated customer with a recurring problem.
• Privacy issues: analyzing customer data using AI can potentially be a Personally Identifiable Information (PII) issue requiring human intervention before further action is taken.
• Data Quality Concerns: For AI to be effective, the data must be high-quality data, which can be a challenge to collect and maintain without a robust data governance program.
• AI training and maintenance: AI models may require continual re-training for any new customer service updates, issues, or customer complaints to be effective 24/7.
• Integration difficulty: Implementing AI into an existing call center system may require hiring a contractor specializing in integrating AI technology into an existing business system.

How to use AI in Call Centers

Artificial intelligence is used in a call center to execute routine tasks, such as data entry, call routing, and responding to basic customer inquiries. Call centers use a knowledge database that agents can use to quickly access accurate information about common customer issues, allowing every agent to provide the same up-to-date information consistently.

Artificial intelligence can help keep the knowledge database updated with the most relevant information agents can provide to customers asking the same question. This is done by analyzing customer interactions in real-time, flagging outdated content, and identifying missing or gaps in information. After analyzing the customer interactions, AI can create new content using NLP that generates new knowledge articles based on common customer inquiries that are turned into Frequently Asked Questions (FAQs). 

On top of that, artificial intelligence can also analyze feedback from surveys and post-call interactions; it can also identify which knowledge-based articles need to be updated based on a rating system. It can also be used to perform content quality checks on active knowledge base articles to identify obsolete information that must be updated. An added benefit of having this knowledge base is that it reduces operational and training costs by providing accurate and readily available information for new agents. 

Call agents’ efficiency improves by spending less time searching for information when AI is used. The onboarding process and training for new agents are reduced when AI automatically updates the knowledge database. Faster resolution times combined with customers receiving accurate information improves customer satisfaction. Artificial intelligence can monitor agents’ calls to assess their performance and identify areas for improvement.

Best Practices for Implementing AI in a Call Center 

When deciding on an AI system to integrate into a call center, businesses must clearly define the goals and objectives the AI system will accomplish. There are several AI call center metrics to consider, such as average speed of answer, first contact resolution (FCR), average handle time (AHT), abandonment call rate (ACR), and escalation rate. These are all examples of metrics a business can use to determine the success of an implemented AI system.

To implement a successful AI system, follow these recommended best practices:

• Set clear goals: Depending on what a business wants to accomplish, determine the Key Performance Indicator (KPI) metrics a company will use, such as improving customer satisfaction, reducing ACR or call handling time, or improving agent productivity by evaluating agents’ performance using the AI system.
• Integrate with existing system: Use project management principles with a charter approved by leadership and consider hiring a contractor specialist with AI system integration experience if this skill set is not in-house.
• Data security and privacy: Ensure the AI system complies with relevant regulations and customer information safeguards to prevent breaches.
• Performance monitoring and quality control: Constantly strive for improvement using AI to monitor call quality while providing feedback to agents for improvement that enhances the overall call center system.
• Intelligent routing: Configure AI to route calls to the most qualified agents based on customer needs or the type of call received.
• Predictive analytics: Use AI to analyze historical data to predict future call volume and staff your call center to adequately meet call demands.
• Agent training and support: Routinely provide agent training on the best practices for effectively using the AI system and its capabilities.
• Customer feedback analysis: Provide customer feedback opportunities that can be analyzed to optimize the AI system further, leading to an improved customer experience. 

AI trends in 2024

When appropriately used, artificial intelligence enhances any business function or process, and call centers are no different. Listed here are some of the current AI trends that enhance the abilities of call centers to be more efficient:

• Automated tasks: Automated tasks streamline operations and reduce costs by relieving call agents from doing routine tasks and allowing agents to focus on outlier issues from customers that an AI system cannot handle.
• Personalized responses and support: AI tools can analyze large amounts of customer data using NLP and ML to understand the context of a conversation and provide relevant responses or solutions.
• Omnichannel communication: AI-powered systems integrate a customer’s chats, voice calls, emails, and social media channels into a single platform, allowing customers to switch between communication channels easily.
• Real-time analytics: Help sales agents understand customer behavior in real time.
• Sentiment mapping: AI helps determine the emotional tone of a conversation.
• AI Quality Assurance (QA) systems: Automatically analyze calls between agents and customer interaction using speech recognition to identify trends, evaluate call agents’ performance, and provide real-time feedback.
• Interactive voice response (IVR): An automated telephone system that uses speech recognition and NLP to allow callers to interact with a business through voice commands and provide self-service options using a voice menu without speaking to a live agent.
• Predictive call routing: Uses ML to identify the best call agent to serve a customer.

VoIP cloud systems to consider

Two popular phone-based cloud systems that can enhance call centers with AI functionality are RingCentral and Nextiva.

RingCentral

RingCentral features all the AI tools to automate tasks and help new or current agents receive real-time assistance. RingCentral can extract relevant information from FAQs applicable to a customer’s issue. This product also helps avoid compliance issues by sending reminder policies, standard operating procedures (SOPs) practices, solutions, and other relevant options as pop-up messages during real-time customer conversations when required.

Nextiva

Nextiva’s conversational AI chatbots and sentiment analysis feature allow the chatbot to consider multiple factors before responding to a customer’s complex issue. The customizable scripting feature can tailor call flows based on a customer’s unique situation. The detailed call analytics AI resource can summarize an agent’s performance, call metrics, and customer trends to optimize call center operations. Nextiva’s advanced AI-driven routine uses ML to route calls to the most qualified agent based on a customer issue.

Frequently Asked Questions (FAQ)

The post What is an AI Call Center? A Complete Guide in 2024 appeared first on TechnologyAdvice.

Key takeaways

A call center is a centralized department that receives inbound calls from customers. Its purpose is to resolve issues, provide information on a service or product, and answer questions related to that product or service. This article will explore different types of call centers that a business can use to support its customers.

What is a hosted call center?

A hosted call center is a customer service function entirely managed by a hosted service provider, while the business using the hosted call center pays the service provider to maintain and support the customer service function through a subscription model. The entire hosted call center hardware and software is hosted by a service provider maintained on a cloud-based platform. Businesses using a hosted call center are not responsible for maintenance, support, or upgrades.

The difference between a hosted call center and an on-premises solution

An on-premises hosted call center is physically located at a business’ onsite data center and is managed and maintained by the business using the hosted call center services. Unlike a cloud-based hosted call center, the company using the on-premises services owns and is responsible for the infrastructure, including hardware, software, and maintenance.

Read more: Best VoIP for Small Business

The benefits of hosted call centers

A cloud-based hosted call center offers tangible benefits that immediately improve an organization’s financial standing and business efficiency from a customer service perspective. The benefits include the following:

• Cost-effectiveness: Businesses using a hosted call center eliminate the up-front cost of purchasing the hardware and software associated with a call center and move to a pay-as-you-go subscription plan.
• Easy setup and lower maintenance: Hosted service providers are responsible for system updates and maintenance, which reduces the organization’s Information Technology (IT) burden. 
• Remote workforce support: cloud-based hosted call centers allow agents to work from anywhere provided an internet connection exists.
• Scalability: allows businesses to adjust the number of agents based on call volume quickly without major infrastructure changes.
• Data security: cloud-based hosted call center systems provide robust security enhancements that protect sensitive customer information.
• Integration with other applications: cloud-based hosted call centers can be integrated with customer relationship management (CRM) and other business applications.
• Advanced features: provides enhanced features and functionalities such as automated call routing, real-time monitoring, and call recording.

How hosted call centers work

A cloud-based hosted call center uses Voice over Internet Protocol (VoIP) technology that converts audio into digital data packets and transports them using a Real-time Transport Protocol over the IP network. When the digital data packets reach their intended recipient, they are reassembled by the receiving device (i.e., mobile phone, computer, or any device capable of playing audio) back into the original data packet order. The receiving device contains a Digital-to-Analog Converter (DAC) that converts data packets back into audio (analog sound waves) that a human can understand, allowing for a conversation. 

Read more: 6 Best Cloud Phone Systems

What is the difference between a hosted call center and a hosted contact center?

A hosted call center uses the phone for all customer interactions. In contrast, a hosted contact center handles multiple communication channels like phones, emails, chats, and social media, which is more comprehensive than a hosted call center. Hosted call centers are phone- or voice-focused, while contact centers must oversee and respond to multiple communication channels. Hosted contact centers offer more advanced features and integration options than hosted call centers.

How to decide if you need a hosted contact center solution

Both solutions are viewed as money-saving solutions when you don’t have to invest in infrastructure, maintenance, or personnel. Businesses providing specialized services like IT support, custom solutions, or integrating hardware or software systems to create a unified solution should consider using a hosted call center with expert agents specialized in receiving calls and providing over-the-phone assistance.

Conversely, businesses that generally sell or advertise a product or event should select a hosted contact center that allows them to maximize audience outreach and benefit from multiple forms of communication. They should consider using a hosted contact center to ensure audiences can be reached by email, social media, chats, or phones.

Both solutions allow for a remote workforce, reduced costs, improved customer experiences, and scalability.

Read more: Best Call Center Software

How different industries use hosted call center solutions

Various business industries use hosted call centers to improve some aspect of a business department or function. Here are some examples of how different business industries use hosted call centers:

• Retail and e-commerce: Hosted call centers can provide customer support, process orders, and answer customer inquiries.
• Transportation: A call center is a centralized platform that can provide proactive updates on changes or potential delays, resolve customer complaints promptly, coordinate deliveries, and use customer feedback to improve upon provided services.
• Healthcare: A call center can assist with appointment scheduling, billing inquiries, and coordinating referrals to the appropriate specialist.
• Improve customer service: A hosted call center can help businesses promptly respond to customers’ issues or inquiries, including personalized phone support.
• Business-to-consumer (B2C) businesses: A call center can assist with customer interactions that improve the customer service experience, which leads to increased customer satisfaction, loyalty, and retention.
• Finance: A hosted call center can provide 24/7 support, facilitate secure transactions, offer personalized services, and allow agents to upsell or cross-sell based on a customer’s profile.

Overall, hosted call centers reduce cost, scale seamlessly to meet changing demands, and provide analytics and reporting tools that give insight into agent’s performance and customer interactions.

Frequently Asked Questions (FAQ)

The post What is a Hosted Call Center? Benefits & Applications appeared first on TechnologyAdvice.

Key takeaways

Data management is an essential practice that is necessary when businesses want data properly handled throughout an organization. The driving influence of data management is data governance, meaning that data governance sets the policies and guidelines for proper data usage. Data management adheres to governance guidance and policies on how data is handled and utilized in an organization, making it an essential component and pillar of the data governance framework.

What is data management?

Data management is the process of collecting, organizing, and using data securely and efficiently in cost-effective methods, all derived from adhering to data governance practices. Data management employs the technical aspects of data handling, collecting, storing, and transforming, including data access. While data management focuses on how data is handled, data governance focuses on the strategic framework that includes the standards on how data is used and managed responsibly throughout an organization.

Definition and core concepts 

Data management’s goal is to use the data governance principles by stipulating the approved methods used to process, manipulate, and share data within an organization while maintaining the four pillars. Data management is one of the four pillars of data governance. The other pillars of data governance are:

• Data quality means the data is accurate, complete, consistent, reliable, and valid.
• Data protection and compliance ensures data is secure and compliant with regulations and safe from data breaches and any type of malicious attacks. 
• Data stewardship ensures the data is accessible, usable, and secure, including the lifecycle aspects of creating, preparing, using, storing, and properly disposing of obsolete data.

Importance in modern business environments

Data management is critical in many aspects of a business’s daily operations. Data management is vital because the data is used in decision-making, regulatory compliance, and identifying new trends and potential markets to exploit using innovative approaches that can improve revenue streams based on data management practices.

Types of data management

There are several types of data management, but a good data management program begins with data governance, followed by data cleansing, data architecture, data modeling, and the Extract, Transform, and Load (ETL) process, which is also covered under data governance.

Data governance

Establishes the policies and procedures for managing data quality, security, access control, and ensuring compliance with any regulations.

Data cleansing

Data cleansing is a crucial step due to identifying and correcting errors or inconsistencies with collected data, ensuring accuracy.

Read more: Data Cleaning: Definition, Techniques, & Best Practices

Data architecture

It is a visual display of how data flows within a business, including storage and access points.

Data modeling 

Visual diagrams that illustrate the different relationships between data elements and how the data moves through an organizational system.

Extract, Transform, Load (ETL)

The ETL process is an approved data governance process for extracting data from multiple data sources, transforming the data, and loading the data into a target system.

Data integration

An established technical process that combines data from multiple sources to create a consolidated view.

Data warehousing 

A centralized repository for storing large volumes of structured data used for data analytics.

Data security

Security is also an essential requirement covered by the data governance framework because it protects businesses’ proprietary data from unauthorized access, prevents data breaches, and ensures compliance with external or internal regulations.

Benefits of data management 

The data management program is a sub-program under data governance. When data management adheres to a fully functioning data governance program, one of the more tangible benefits is the competitive advantage it provides by allowing a business to make faster and more accurate decisions than its competitors. A good data management program helps improve businesses’ overall operations in several categories:

• Improved decision-making: Allows businesses to accurately analyze data that produces data-driven decisions, which enhances a business’s efficiency.
• Enhanced security: Data management approved processes help protect data from theft, loss, and breaches.
• Improved operational efficiency: Data management streamlines internal operations by identifying and reducing redundancies, which eliminates wasted time with data not related to a specific goal or objective.
• Scalability: Automated data management systems can rapidly handle an increase in a seasonal demand or any increased activity not anticipated without introducing errors in the system due to increased demand.
• Backup and restore: Reduces any downtime by allowing an organization to recover from a data loss quickly and minimizes the risk of losing data permanently.
• Data integration: Facilitates combining data from different sources to present a unified view of data for a general or specific audience.

Challenges of data management 

The primary challenge of data management is addressing poor data quality in a dataset or database. Poor data quality can be incorrect, inaccurate, incomplete, duplicate, or inconsistent, which are the ways data can be identified as poor. Other challenges to data management are:

Data governance

The typical data user will not understand the value of a data governance program because it’s not apparent to a typical user how data is cleansed and integrated into a dataset as value-added data to a business. Therefore, educating users on how data is handled and processed correctly is essential. Still, a broader overview of the data governance program is required to explain the roles of data management and governance. 

The data governance and data management programs are Chief Information Officer (CIO) supported programs, and there are consequences for not adhering to the authorized data management practices. A mandatory annual overview of the data governance program and the complexities associated with governance will ensure users are trained and held accountable for the proper use of data.

Data security

Data security is another topic that needs to be covered in a data governance training program that stresses the approved security protocols and access controls that focus on preventing unauthorized access and data breaches.

Regulatory compliance 

A misstep in regulatory compliance can lead to significant fines when a compliance violation is discovered. A robust governance policy must be strictly enforced to ensure compliance, particularly when data is stored in the cloud.

Data integration

Any time data is consolidated from multiple data sources, the targeted dataset or database is exposed to potential errors. Data cleansing is essential before the combined data is displayed in a unified view.

Best practices of data management

The best practices for data management begin with a data governance framework derived from an organization’s strategic objectives. The objective can be improving customer support, boosting revenue, increasing market share, or improving operational efficiency. The best practices are:

Data governance framework

A data governance framework outlines roles, responsibilities, and processes for effective data management. Establishing data policies and standards that focus on how data is collected, stored, accessed, and used in a business is vital. The standards address guidelines for naming conventions, data formats, and metadata. The policies address compliance, data quality, privacy, security, usability, integrity, retention, and authorized business tools.

Data lifecycle management

Lifecycle management addresses how long data should be archived, retained, and the proper disposal procedures. A data lifecycle management program helps organizations maintain compliance with data retention regulations, manage the risk of retaining irrelevant data, and reduce storage costs.

Master Data Management (MDM)

The MDM ensures the data is consistent and accurate across the organization, and the MDM establishes a centralized repository for master data such as product information, employee records, and other relevant data a business uses.

Data documentation and metadata management

Proper data documentation management ensures all data sources are documented and clearly defined, giving context and meaning to data that users can understand.

Data analytics and reporting

Data analytics is used to identify trends, opportunities, and anomalies typically not identified by humans. Data analytics methods, techniques, and reporting can extract actionable insights from business data used to make informed, data-driven decisions, making the data analysis process vitally important for successful businesses.

Data audits and compliance checks

Regularly scheduled audits of your data management processes and practices can help identify areas that need improvement. These checks ensure compliance regulations are upheld while preventing possible legal or compliance risk exposure.

Data security and privacy

Protecting data is paramount in preventing an organization’s data from being exposed through unauthorized access or a data breach. Ensuring businesses follow privacy regulations like the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA) are mandatory. Annual security training is a must to build a cultural-conscience security organization.

Data integrations 

Data integration can be challenging when moving siloed data from multiple sources to one target system. An automated integration tool can expedite the process by enabling data to flow seamlessly into a target system, eliminating the need for manual entry or transformation. Automated data integration vastly improves data accuracy and consistency, resulting in error reduction and faster results displayed in a unified view.

Continuous data quality assurance

Data quality assurance is essential in continually identifying and correcting errors, duplications, and inconsistencies. Automated data quality assurance software like CloverDX, which is always running, can help keep your target dataset reliable and trustworthy.

The future of data management 

Large and complex datasets, known as big data, use data mining and continue to be used by businesses, making data management and governance critical processes to manage these large datasets. The volume, velocity, and variety of structured, semi-structured, and unstructured data require pre-defined data management processes that ensure data is protected and used correctly by abiding by data management practices.

Recap of key points

Data management is a program under data governance that adheres to guidance provided by approved data governance policies, processes, and roles. Data management is the operational side of data governance, which dictates how data is handled daily. Data governance and management work together to ensure data is valid, accurate, protected, and available.

Future trends in data management

Artificial Intelligence (AI) and Machine Learning (ML) tools will become everyday tools used in data management to help businesses interpret large datasets for improved decision-making. Augmented analytics uses AI and machine automation that will help transform how data is prepared, shared, and generated. Data Fabric is a data management and integration framework that allows businesses to connect and manage data across different systems and applications in real-time. 

Final thoughts on the importance of effective data management

Effective data management optimizes operations, improves decision-making, enhances customer experiences, promotes innovation, and maintains compliance by providing valid, reliable, accessible, and secure data. Data is a valuable asset, and data management is the foundational process that allows data to become valued information businesses use to make informed decisions and optimize business processes.

Frequently Asked Questions (FAQ)

The post What is Data Management? appeared first on TechnologyAdvice.

Key takeaways

Customer Relationship Management (CRM) software helps businesses improve every aspect of a customer’s relationship with a company. The CRM applications focus on sales, marketing, customer service, commerce, and customer data.

What is CRM Analytics?

Customer Relationship Management analytics uses artificial intelligence to analyze the metrics from sales to customer data. The analytics is used to enhance customer service, optimize marketing, improve sales, and monitor staff performances that ultimately increase customer loyalty and retention. Customer Relationship Management analytics also help businesses understand customer behavior, needs, and preferences, which is used to predict future marketing and sales strategies.

What can I track with CRM analytics?

CRM analytics can track Key Performance Indicators (KPIs) for customer interactions, sales transactions, and marketing campaigns. 

Customer interactions

• Customer churn rate: Identifies the percentage of customers who stop doing business with a company over a specific period. Hence, the churn rate identifies potential customers that may leave and how to lower the churn rate and keep customers.
• Past purchase: Tracks customers’ previous purchases that can be useful during promotional sales or when a new product is released. 
• Link click-through rates: Tracks how often links in marketing emails are clicked.
• Follow-ups: Tracks how often sales personnel follow up with customers.
• Calls, emails, and meetings: Track communications statistics to ensure a business is building relationships with customers.
• Inquiries and support requests: Keeps a log of inquiries and support requests about products or concerns with delivery or product performance.
• Customer retention: Tracks the number of customers who remain loyal customers over a given period

Sales transactions

• Sales calls: Tracks the number of calls, length of calls, and the success of sales calls
• Sales forecasting: Uses historical sales data to forecast future sales trends
• Sales rep performance: The performance of sales representatives and the win rate percentage
• Lead drop-out rates: Tracks the rate when leads drop out at different stages of sales
• Stage duration: The duration of each stage in the sales pipeline
• Loss rates and reasons: The reasons for losses and the loss rate percentage
• Purchase and return history: Tracks product purchases and returns
• Sales conversion rate: The percentage of potential customers who complete a desired action

Marketing Campaigns

• Email marketing: Track open rates, click-through rates, and other metrics associated with marketing emails.
• Social media posts: CRM analytics tracks how users interact with social media posts.
• Customer response rates to marketing campaigns: Track the success rate of a marketing campaign.
• Marketing events: Track information about events.
• Video viewership rates: CRM analytics tracks how often potential customers watch and rewatch marketing campaign videos and when viewership begins dropping off.

CRM analytics aims to gain insight into customer behavior, preferences, and interactions, allowing businesses to make informed decisions about sales, marketing, and customer service to improve customer experience and increase sales revenues.

How are CRM analytics reported

The results of a CRM analytical report are presented in different schemes depending on the CRM platform used. Typically, the best method for sharing a report is through the native features that are included in your CRM platform. Customer relationship management platforms use multiple methods for reporting analytical results. 

A business can use pre-built reports, customizable views with filters, and tailored reports to extract specific information. Using the CRM native reporting options allows users to set access control or present a share button for selected users to access the report. The standard reports provide a variety of predefined reports like sales performance, lead generation, and customer activity.

Data visualization reports are:

• Charts
• Graphs
• Dashboards

Data visualization reports are popular because they allow an audience to easily interpret what is presented and identify trends or patterns quickly. A CRM application like Salesforce will enable you to integrate with collaboration tools for discussion and feedback within the Salesforce CRM platform. Popular CRM applications are Salesforce, Zoho CRM, and Microsoft Dynamics 365.

What CRM options do analytics best?

Every CRM solution is known for doing something better than its competitors, and Salesforce is known for its CRM analytics. The Salesforce Einstein solution performs the best analytics because of its enhanced AI capabilities that automatically analyze large datasets, which can extract predictive insights and automate actions without detailed data preparation or complex modeling. 

Salesforce solutions can personalize recommendations that allow users to discover hidden patterns and trends and make data-driven decisions quickly and effortlessly. Salesforce’s key analytical features are:

• Automated insights: The Einstein version identifies specific data patterns that can provide vital insight without manual intervention
• Predictive Analytics: Executes machine language algorithms to predict customer churn, conversion rates, and recommend proactive strategies for the future
• Seamless integration: Integration is built into the Salesforce platform that allows users to access the AI-provided insight in their created workflows
• Data-Ready design: Minimizes the extensive data preparation process and removes the complexities of the analytical method for non-technical users

Overall, the Salesforce Einstein solution reduces manual work, improves sales efficiency, and improves decision-making using real-time analytics.

CRM analytics software recommendations

Customer Relationship Management solutions often have partners that strengthen some aspect of a business function. Salesforce, Microsoft Dynamics 365, and Zoho CRM have business partners that either enhance the CRM solution or train users on using these CRM applications.

Slack

Slack is a messaging application used to help teams communicate and collaborate better, whether users are on the go or in the office. Slack allows you to chat, share audio, and video clips, including huddles that enable you to communicate in real-time. Slack can create channels that can be public or private and organize work for projects, teams, or an office. Slack can be integrated with other services like Google Drive or Office 365. Slack was purchased by Salesforce in 2021.

Experlogix

Experlogix is used by Microsoft Dynamics 365 to improve the sales process and streamline order fulfillment. Experlogix collates the order information into one place, reduces manual errors, and shortens the sales cycles. Experlogix digital commerce integrates with Dynamics 365 to help customers with ordering 24/7. Experlogix improves the customer experience through its improved self-service shopping process.

Spark

Zoho CRM uses Spark to teach developers how to use development tools in a four-day online course. Spark provides instruction on the Zoho Social feature for scheduling and publishing content to social media channels and how this feature integrates with the Zoho CRM application. Spark offers several training courses for users and data administrators, too. Zoho certifications are provided once a user completes an assessment exam.

CMR analytics trends

Customer Management Relationship solutions are about maintaining customer communication, and omnichannel integration is one of the new trends that helps businesses improve customer communication. Omnichannel integration provides a seamless shopping experience across all sale channels: physical stores, applications, websites, and social media. The omnichannel integration provides a unified customer experience across any sales channel a customer is using. 

This trend personalizes each customer’s experience through tailored messages and offers based on customer data and behavior across each sales channel. A customer’s shopping journey becomes seamless between online and offline interactions, whether browsing products online or picking them up at a store. The seamless omnichannel experience improves and increases customer engagement, which increases customer retention. Other CRM trends are:

• AI-driven insights: This trend predicts customer behavior, improves sales strategies, and personalizes communication across different omnichannel
• Internet of Things (IoT)integration: IoT data can be integrated with CRM data to help businesses understand customer’s behavior and preferences
• Low-code/no-code solution: Self-service options in CRM solutions offer no-code features for customers
• Automation: Improves business efficiency and allows sales teams to focus on non-automated issues or tasks
• Mobility accessibility: This trend is growing and allowing all users to access CRM systems via mobile devices
• Content Delivery Platforms (CDPs): A CDP aggregates and analyzes data from internal CRM and Enterprise Resource Planning (ERP) systems for better performance management and improved financial results
• CRM à la carte: Divide CRM solutions into usable components so businesses only pay for what they use

Frequently Asked Questions (FAQ)

The post Everything You Need to Know About CRM Analytics in 2024 appeared first on TechnologyAdvice.

Read more: Business Intelligence vs. Data Analytics

What is data cleaning?

Data cleaning is a necessary step that must occur before the data is executed in a data analysis process or business intelligence operation. Data cleaning involves looking for erroneous, inaccurate, or incomplete data that needs to be removed, corrected, or updated. Data cleaning consists of using AI tools and a manual review conducted by specific personnel to remove different types of incorrect or missing before any data can be processed in a business intelligence or data analysis process.

Read more: What is Data Analysis? A Guide to the Data Analysis Process

The importance of data quality

Using bad or poor data in a BI or data analysis process can lead to incorrect analysis, business operation errors, and bad business strategies. Addressing bad data before it’s executed in a data analysis process saves businesses money by reducing the expense of fixing bad data results after the data is processed, including the added cost of interrupting business operations to correct the results of bad data. 

The cost of fixing poor data increases if it is not corrected in the data cleaning process. Cleaning bad data in the data cleaning process costs approximately one dollar. The cost increases tenfold if not corrected in the data cleaning phase, and if the bad data is processed and used, the cost of correcting a problem resulting from bad data increases to $100.

Data can be improperly formatted, contain spelling errors, duplicate records, missing values, integration errors, or outlier information that skews data. These types of data errors must be cleaned through a data cleansing process before data analysis processing. The emerging role of artificial intelligence (AI) and automation tools contribute significantly to identifying and correcting various errors in the data cleaning process, which enhances its overall efficiency.

Read more: Best Data Quality Software Guide

Understanding data cleaning

Data cleaning or washing is a critical step in the data processing phase because it boosts data consistency, correctness, and usability, making the data valuable after analysis. Ensuring the data is thoroughly cleaned can be challenging for businesses due to the varying formats and standards used. Data can come from different sources, which can be problematic in the data cleansing process.

For example, Lexical, grammatical, and misspelling errors can be challenging for businesses to correct, even when using advanced AI tools. Additionally, when integrity constraints are not applied to a data column in a table, the column can accept any value. 

Embedded analytics data from an application populates a database table, providing the latest information for business uses without the need for querying. However, if an embedded value is populating a data column with no integrity constraints, then the software application populating the data column could populate the data column with incorrect information. 

This is possible if a software application is updated and the embedded analytics data is incorrectly modified, sending erroneous data to the data column.

Outdated data that is not routinely updated can damage a business’s financials or reputation. Data quality issues can cause a company to lose up to 20% of its expected revenues. Without proper data hygiene, the saved data can contain misspellings, punctuation errors, improper parsing, and duplicate records. A lack of standardized naming conventions can also cause a business to lose expected revenues. To combat these data challenges, companies must continuously clean collected data to maintain data integrity and accuracy.

Read more: Common Data Quality Issues & How to Solve Them

How to clean data: A step-by-step guide

Data cleaning occurs after the data collection process is complete. Not all the collected data will be used, and it will most likely contain duplicates, erroneous values, missing values, and unformatted data that must be cleaned up before it is used in a business intelligence or data analysis process.

The emergence of AI tools minimizes the need to rely entirely on a manual data-cleaning process. However, there will be a requirement to manually check any AI tools used to ensure they identify and correct any discrepancies it’s expected to correct, which includes a final manual validation check that all errors are removed.

Step 1: Remove irrelevant data

Identify Unnecessary Data: Begin by reviewing your dataset to identify and remove any data that does not contribute to your analysis objectives. This could include columns or rows that are not relevant to the specific business questions or analysis goals you are addressing.

Criteria for Relevance: Establish clear criteria for what constitutes relevant data based on the purpose of your analysis. For instance, if you are analyzing customer data, fields like ‘customer ID’ and ‘purchase history’ might be relevant, while ‘middle name’ might not be.

Automation: Use AI tools to automate the identification of irrelevant data, but ensure you manually review the results to avoid excluding potentially useful information inadvertently.

Step 2: Deduplicate Redundant Data

Identify Duplicates: Scan your dataset for duplicate records, which are common in large datasets and can skew analysis results. Duplicates often occur when data is collected from multiple sources or entered manually multiple times.

Removal Process: Use automated tools to flag duplicate entries by comparing key identifiers like unique IDs or other distinguishing attributes. Once identified, remove or consolidate these duplicates.

Manual Review: After using AI tools, manually check a sample of the flagged duplicates to confirm accuracy and ensure that no unique data points are incorrectly removed.

Step 3: Repair Structural Errors

Identify Structural Issues: Look for inconsistencies in data structure, such as inconsistent naming conventions, formatting errors, or misplaced data. Common structural issues include typos, incorrect capitalization, and different date formats.

Correct Structural Errors: Use data-cleaning software or AI tools to standardize the structure across your dataset. For example, ensure that all dates follow a consistent format (e.g., YYYY-MM-DD) and that categorical data uses standardized labels.

Manual Inspection: Review the corrections made by the AI tools, especially in areas where human judgment is required to determine the correct format or structure.

Step 4: Address Missing Data

Identify Missing Data: Identify any gaps or missing values within your dataset, which could lead to biased or inaccurate analysis results. Missing data might be represented as blanks, ‘NaN’, or special characters like ‘?’.

Handling Strategies: Depending on the extent of the missing data, decide whether to remove the affected rows, fill them with a calculated value (e.g., mean, median), or use advanced techniques like imputation via AI models.

AI Assistance: Leverage AI tools that can intelligently predict and fill in missing data based on patterns within the dataset. However, ensure a final manual check is conducted to confirm the appropriateness of the AI-generated values.

Step 5: Filter Out Data Outliers

Detect Outliers: Outliers are data points that significantly deviate from the norm and can distort your analysis. Use statistical methods or AI algorithms to identify these anomalies.

Decide on Handling: Determine whether the outliers are errors that need correction, values that should be excluded, or important data points that should be kept (e.g., a significant sales spike).

Review Outliers: While AI can automate outlier detection, it’s important to manually review these points to ensure that valid data isn’t mistakenly removed.

Step 6: Validate That the Data Is Correct

Final Validation: Once the cleaning process is complete, conduct a thorough validation of the dataset to ensure all errors have been addressed. This includes running checks to confirm data consistency, accuracy, and completeness.

Manual Spot Checks: Perform random spot checks on the cleaned data to ensure the AI tools have correctly identified and rectified all issues. This step is crucial for maintaining data integrity.

Document the Cleaning Process: Keep detailed records of the cleaning steps you’ve taken, including any decisions made during the process. This documentation is important for transparency and reproducibility in future analyses.

Machine learning is the primary AI tool for identifying and correcting errors in a dataset. The ML algorithm can handle missing or inconsistent data, remove duplicates, and address outlier data saved in the dataset, provided it has learned to identify these errors during the ML algorithm testing phase by using either the supervised, unsupervised, or reinforcement learning process. The popularity of AI tools makes the data cleaning process more efficient, allowing businesses to focus on other aspects of the data analysis process.

Consider leveraging AI tools for data cleaning

Building on the strengths of machine learning in data cleaning, businesses can take a more proactive approach by leveraging AI tools to automate much of the grunt work involved. Implementing machine learning models not only helps in identifying and correcting data errors but also streamlines the entire cleaning process, from detecting inconsistencies to removing duplicates and spotting outliers. As these models learn and adapt from training data, they become more efficient over time, handling even complex cleaning tasks with greater accuracy.

To maximize the effectiveness of machine learning in data cleaning, it’s crucial to choose the right approach. Whether you opt for supervised, unsupervised, or reinforcement learning depends on the specific characteristics of your dataset and the type of errors you’re looking to resolve. Each method offers unique advantages, allowing for tailored solutions to your data challenges.

With AI taking over much of the data cleaning burden, your team can redirect its focus toward higher-level, strategic data analysis. This not only increases operational efficiency but also ensures that your cleaned data is more reliable, setting the stage for deeper insights and more informed business decisions.

Techniques and best practices for data cleaning

Data washing or cleaning has changed dramatically with the availability of AI tools. The traditional data cleansing method uses an interactive system like a spreadsheet that requires users to define rules and create specific algorithms to enforce the rules. The second method uses a systematic approach to remove duplicate data and data anomalies, ending in a human validation check. 

With the challenges of cleaning big data, these traditional methods are impractical. Today, businesses use Extract, Transform, and Load (ETL) tools that extract data from one source and transform it into another form. The transformation step is the data cleaning process that removes errors and inconsistencies and detects missing information. After the transformation process is completed, the data is moved into a target dataset.

The ETL process cleans the data using association rules, which are if-then statements, statistical methods for error detection and established pattern-based data. With the emergence of AI tools, businesses save time with better results, though a human is still required to review the cleansed data.

The emerging role of Artificial Intelligence (AI) in data cleansing

Artificial Intelligence helps data cleaning by automating and speeding up the data cleansing process. Machine Learning (ML) is a subfield of AI. The ML algorithm uses computational methods to learn from the datasets it processes, and the ML algorithm will gradually improve its performance as it processes more sample datasets presented to the ML algorithm. The more sample data the ML code is exposed to, the better it becomes at identifying anomalies. 

The ML algorithm uses supervised learning, which trains the algorithm based on sample input and output datasets labeled by humans. The second option is unsupervised learning, which allows the algorithm to find structure as it processes input datasets without human intervention. Reinforcement learning (RL) is another ML algorithm technique that uses trial and error to teach ML how to make decisions. Machine learning builds a model from sample data that allows the ML algorithm to automate decision-making based on the inputted dataset processed. 

After ML algorithms have learned from sample datasets, the algorithm can correct the data using data imputation or interpolation methods to fill in missing values or labels. Imputation replaces missing data with an estimated value, and interpolation estimates the value of a data column by using a statistical method involving the values of other variables to guess the missing values. Both methods are used in ML to substitute missing values in a dataset. Data deduplication and consolidation methods are used to eliminate redundant data in a dataset.

Natural Language Processing (NLP) is another subfield of AI. It analyzes text and speech data. This AI tool can be used on text documents, speech transcripts, social media posts, and customer reviews. Natural Language Processing can extract data using an NLP model that can summarize a text, auto-correct a document, or be used as a virtual assistant.

In addition to the available AI tools used in BI and data analysis, mathematical and statistical equations complement the AI tools. These equations verify the AI results fall within an expected standard deviation. For example, numeric values that fall outside the expected standard deviation can be considered outliers and excluded from the dataset.

Read more: Data Analysis Methods and Techniques

When is a manual data cleaning process required?

Though manual data cleaning processes are still required, they are minimized. Manual data cleaning is needed when a business wants the data to be at least 98% accurate. The manual data cleaning effort focuses on correcting typos, standardizing formats, and removing outdated or duplicate data from the dataset. In business industries like healthcare or finance, manual data cleaning can enhance patient safety or help financial institutions minimize compliance risks. Manual data washing is essential when every record matters, and you want your dataset or database to be as perfect as possible.

Data validation and quality checks

A convenient method for ensuring data columns or fields contain valid data is to implement integrity constraints on the database table’s data column that the user must adhere to before the data is saved in a field. The integrity constraint is a set of rules for each data column that ensures the quality of information entered in a database is correct. The constraints include numeric values, alpha characters, a date format, or a field that must be a specific length before the data is saved in the field or data column. However, misspellings can be challenging to identify. 

The integrity constraints will minimize some errors found during the data cleansing phase. A quality check performed by a human can validate correct spelling, outdated information, or outlier data still in the database. Quality checks can be routine or done before the data cleaning process occurs.

Data Profiling

Data profiling analyzes, examines, and summarizes information about source data to provide an understanding of the data structure, its interrelationships with other data, and data quality issues. This helps companies maintain data quality, reduce errors, and focus on recurring problematic data issues. The summary overview that data profiling provides is an initial step in formulating a data governance framework.

Normalization and standardization

Database normalization is a database design principle that helps you create database tables that are structurally organized to avoid redundancy and maintain the integrity of the database. A well-designed database will contain primary and foreign keys. The primary key is a unique value in a database table. A foreign key is a data column or field associated with a primary key in another table for cross-referencing the two tables. 

A well-designed database table will be normalized to first (1NF), second (2NF), and third (3NF) normal forms. There are four, five, and six normal forms, but the third normal form is the furthest we will explore. The first normal form removes data redundancy from the database table. 

Figure 1 contains redundant data, so the database table is not normalized to the 1^st NF.

Unnormalized database tables cause insertion, deletion, and update anomalies. The insert anomaly will continually populate the table with unnecessary redundant data and overpopulate the database. The deletion anomaly can possibly unnecessarily delete the professor’s information if all the student information is removed. A related database table is lost when student data is deleted, and the database is not normalized to the 1st NF.

The last issue is an update anomaly. If another professor replaces Professor Perry, every record will be updated with the new professor’s information. Data redundancy requires extra space if not normalized, including the problems we just covered with insertions and deletions. To solve this problem, we must create two database tables, as shown in Figure 2. 

The primary key is in a red font, and the foreign key uses a green font. The two database tables are now connected with the primary and foreign keys, and any professor information that changes will only require updating the professor table. These two databases are now considered to be in the first normal form.

The second normal form addresses removing partial dependency. A table must also be in 1NF to be in second normal form. A primary key is a unique value that retrieves one specific record from a database table, and the retrieved data columns or fields are functionally dependent on the primary key. For example, a unique student ID number can retrieve name, address, and other personal information. An essential concept of the second normal form is when the functional dependency relies on the primary key to retrieve specific data entirely dependent on the primary key. 

In the Figure 3 tables, the three tables will help illustrate what partial dependency is.

Subject Table

Score Table

The primary key retrieves functionally dependent information in the student and subject tables. The score_ID is not a good primary key because it only represents one test, and some students have taken more than one test. The score table uses a composite key comprising two or more columns as a primary key. The composite primary key can pull up a student’s tests. Since this is a score table, professors’ information is unnecessary, as it is partially dependent on subject_ID and has nothing to do with the student_ID. 

Removing the professor column shown in Figure 3 puts the Figure 4 score table in second normal form, and adding a data column for the professor data in the subject table shown in Figure 4 also makes the table functionally dependent on the foreign key.

Subject Table

Subject Table

To illustrate transitive dependency, the score table adds two data columns, as shown in Figure 5.

The two data columns or fields added in Figure 5 will demonstrate how transitive dependency impacts a database table. The composite primary key can retrieve all the data columns except total points. The total points column depends on the test_name field, making it transitive-dependent and not associated with the composite primary key. The test_name field grades practical and presentation tests differently with a Pass or Fail rather than a standard multiple-choice test based on points. Removing the Test_name and Total_points fields from the score table and creating a test table will put the database tables in third normal form. See Figure 6 tables.

Score Table

Test Table

Data standardization is vital to the data cleansing process because it converts the structure of different datasets into a standard format after the data sources are collected and cleansed before being loaded into a target system for data processing. Using a standardized format makes it easier for the computer to process the data, improving its quality, accuracy, and reliability. Standardized data also makes it easier for businesses to compare and analyze the data to gain insight that improves overall business operations.

Establishing a data governance framework

A data governance framework should be the foundation of an effective and coherent data management program that establishes rules and procedures for proper data collection strategies, storage requirements, data quality, security, and compliance. Using a data enrichment tool as part of the governance framework can help businesses address outdated information, fill in missing information, and add more context to existing data.

Read more: 4 Data Enrichment Tools for Lead Generation

The four pillars of data governance ensure all stored data is usable, accessible, and protected, including reducing errors, inconsistencies, and discrepancies. Data governance also includes managing data catalogs, the central repositories that capture and organize metadata. The data catalog provides a comprehensive inventory of an organization’s data assets. Data governance has specific roles that delineate responsibilities.

Data users are essential to help the organization accomplish its business goals by properly using the data. Building a data-conscious business culture must start with upper management and flow down through the organization through regular training, strategically placed posters promoting data governance and a comprehensive introduction of a data governance training program for new hires like the cybersecurity training program. Like cybersecurity training, data governance should be an annual training requirement.

Comprehensive data management software recommendations

There are aggregate BI solutions that perform the full spectrum of data analysis actions, like cleansing, analyzing, and interpreting data, allowing a business to make a data-informed decision. These comprehensive BI solutions also include data governance features that enable you to manage your data from inception to the proper disposal of obsolete data, allowing businesses to manage the entire data lifecycle.

IBM InfoSphere

IBM InfoSphere Master Data Management solution provides a tool that all businesses can use to manage data proactively with different deployment models and accelerate insights for quick decision-making.

Talend

Talend’s modern data management solution provides an end-to-end platform with data integration, data integrity and governance, and application and Application Programming Interface (API) integration.

Tibco

Tibco’s data management platform provides a master solution that allows users to manage, govern, and share data with peers. Tibco’s management solution features hierarchy management, role-specific applications, and data authoring.

Crucial data cleaning software features

Using business intelligence or data analysis tools without a thorough data cleansing process is a non-starter. Finding the best AI-based data cleansing software can be challenging with today’s various data cleaning applications. The best data cleaning software must have these features to thoroughly clean data expeditiously:

Data profiling and cleansing functionality

A data profile transformation lets a user examine the statistical details of the data structure, content, and integrity of the data. The data profiling feature uses rule-based profiling, including data quality rules, data profiling, and field profiling. This feature allows businesses to retrieve data stored in legacy systems and identify records with errors and inconsistencies while preventing the migration of erroneous data to the target database or data warehouse.

Advanced data quality checks

Data quality checks are rules or objects used in the information flow to monitor and report errors while processing data. These rules and objects are active during the data cleaning and help ensure data integrity.

Data mapping

Data mapping helps correctly map data from data sources to the correct target database during the transformation process. This feature provides a code-free, drag-and-drop graphical user interface that makes the process of mapping matching fields from one database to another database.

Comprehensive connectivity

A data cleansing tool must support the common source data formats and data structures, including XML, JSON, and Electronic Data Interchange (EDI), which allows the electronic exchange of business information between businesses using a standardized format.

Workflow automation

Workflow automation helps automate the entire data-cleaning process. This automation feature profiles incoming data, converts it, validates it, and loads it into a target database.

A data cleansing success story

Human Resource (HR) departments, including HR analytics, are critical to successful business operations. As discussed, data can be prone to errors and inconsistencies due to human error, data integration issues, and system glitches. Human resource departments contain employee records with Personally Identifiable Information (PII), which, if mishandled in any way, can damage a business financially, reputationally, operationally, and legally. IBM’s Cost of Data Breach Report in 2023 stated the average data breach cost was $4.45 million last year.

Using an AI data cleaning tool will improve the efficiency and consistency of the HR department’s data, and using a data cleansing guide that outlines each step in the process will help ensure success. La-Z-Boy understands the value of analytics and successfully used the Domo cloud-based management platform with advanced features like alerts that are triggered when a specific threshold is triggered, which causes a data custodian to perform a required action. Domo’s intuitive graphical dashboard displayed information that was easy to understand and take the appropriate action.

La-Z-Boy’s business intelligence and data manager understands that data analytics information begins with a repeatable data cleansing process. The repeatable process is the following:

• Identify the critical data fields
• Collect the data
• Remove duplicate values
• Resolve empty values
• Standardize the cleaning process using workflow automation
• Review, adapt, and repeat on a daily, weekly or monthly basis

In addition to HR analytics, Domo’s analytics software helps with pricing, SKU performance, warranty, and shipping for more than 29 million furniture variations.

The minutiae of data analysis

Every detail of the data analysis process should be considered critical. BI solutions come with advanced AI data cleansing tools that are only effective if they have been trained to look for specific discrepancies in data. Therefore, no matter how thoroughly you think the AI tool has cleaned the data, manually reviewing the AI-cleansed data is always recommended to ensure it did not miss a unique discrepancy the AI tool was not trained to address.

The data analysis phases before and after data washing are essential. Still, the most critical role is the data cleaning role because if any error is used to make a business decision, the mistake can range from negligible risks to catastrophic damages that can lead to business failure. 

Negligible risks can include a poorly planned marketing campaign, an inability to pay suppliers or customer loss. To produce good data for decision-making, collecting and cleaning the correct data must be prioritized with attention to detail.

The data governance framework begins with validating the data quality before it’s saved in a database or data warehouse. These data integrity checks must be integrated into any application that saves data. Secondly, data governance should be as essential and given as much attention as cybersecurity training.

The post Data Cleaning: Definition, Techniques & Best Practices for 2024 appeared first on TechnologyAdvice.

Key takeaways

AI data analysis involves using advanced tools to enhance the speed and accuracy of processing large volumes of structured, unstructured, and semi-structured data. Traditional methods, such as Excel or programming languages like Python and R, are limited in handling big data, which often requires AI for tasks like data cleansing, pattern recognition, and anomaly detection. By leveraging high-end CPUs and AI, businesses can efficiently extract valuable insights from complex data to make informed, data-driven decisions that manual processes simply can’t match.

AI tools are necessary because they expedite the collection and preparation process, recognize patterns in data more efficiently than humans, and can detect anomalies in data better than any combined traditional approach involving humans examining data sets by hand. When examining and manipulating big data, relying on a human to process big data quickly and accurately is impractical.

What is AI in data analytics?

Artificial Intelligence (AI) is necessary and integral when analyzing large amounts of big data. Artificial intelligence is a category of computer science. Under AI, the broader category, there are sub-categories of AI tools used to help collect, clean, prepare, analyze, and interpret the extracted data. 

The AI sub-categories are Machine Learning (ML), Natural Language Processing (NLP), deep learning, robotics, and image and speech recognition used in a data analysis process or step that uncovers insights for data analysts or decision-makers.

Without AI tools in data analytics, it would be difficult for businesses to extract data from multiple data sources, collate it, and put the collected data in a readable format that a human can easily interpret to make an informed decision.

Practical applications for AI data analysis

Business intelligence (BI) is closely related to data analytics because BI uses data analysis methods and techniques to help businesses make informed decisions across any function. For example, data analysis uses ML algorithms to learn from being exposed to data. Natural language processing is used to understand human language and extract useful insight from different forms of texts, messages, and documents. 

Business intelligence uses a combination of data analytics, AI technology, and strategy that help organizations make better decisions based on data processed through data analytics. Business intelligence is not effective without data analytics. 

Artificial Intelligence is used in data analytics in multiple ways:

1. Connect to different data sources: AI assists the data analysis process by using machine learning algorithms to connect to multiple data sources and identify trends or patterns across multiple datasets, even when data is stored in different formats.
2. Data collection and cleaning: AI technology can automate data collection from various sources, such as structured databases and unstructured data like text files, social media feeds, and images, including cleaning and standardizing the data in preparation for analysis.
3. Finding relationships between data points: AI algorithms can combine related data points from different data sources that may not be apparent when analyzing each dataset separately.
4. Data mapping: AI can identify data values in data field names across different datasets, even if the field names differ.
5. Natural Language Processing (NLP): This AI feature can extract relevant information from text data and integrate it with structured data from other sources.
6. Machine Learning models: By exposing an ML model to different or combined datasets through training, ML can learn how to distinguish the complex relationships between data values in datasets, which would be difficult for traditional data analysis processes to recognize.
7. Improving data quality with automated cleaning: Businesses can use AI to improve data cleansing by automatically correcting inconsistencies, filling in missing values, and removing duplicates. 
8. Creating reports and dashboards: A user can select data to include in a visualization, and AI will automatically format and display in a user-friendly chart
9. Summarizing insights and analysis: Users can ask an AI chatbot specific questions like the best sales month last year or the reason for a sales decrease in the previous month.
10. Creating code and debugging errors: Programming is a time-consuming process, and using an AI code assistant increases productivity and accelerates the software development process by automating repetitive tasks, assisting in debugging, and automating deployment.

Data analytics and AI can help different business functions improve any aspect of a company. Businesses wanting to improve customer management can analyze customer data to make informed business decisions. 

Other business areas that data analytics and AI can improve are:

Supply chain optimization: AI can help identify cost-saving opportunities, predict demand patterns, identify potential disruptions, and improve route planning that optimizes delivery routes.

Improve business operations: AI and data analytics can identify business processes that need improvement or have become obsolete and deemed wasteful.

Pricing: Changing prices based on real-time market demand

Marketing: Preventing guesswork from marketing by analyzing and reviewing real-time data

Business Process Management (BPM): Analyzes large datasets generated by business processes to identify organizational inefficiencies and impediments.

Data analytics and AI are used in many business industries, such as manufacturing, retail or hospitality, and financial or healthcare institutions. Data analytics can help financial businesses detect fraud quickly, provide healthcare organizations with improved patient care, and more accurately target a diagnosis for a rare health condition using comprehensive data analysis. 

Any business-industry concerned with improving business productivity, revenue generation, or customer satisfaction must consider incorporating advanced data analysis tools.

Benefits of using AI in data analytics

The popularity and benefits of using data analytics combined with AI are removing the guessing or estimating in decision-making for managers and analysts. Instead, managers and analysts can make better decisions based on the results of data that has been processed and analyzed, making data-driven decisions.

The four popular types of data analyses are descriptive, diagnostic, predictive, and prescriptive analysis. Each analysis type is designed to answer one question.

Descriptive analysis

Descriptive analysis focuses on what happened. This analysis uses measures of central tendency and dispersion, including histograms, scatter plots, data mining, and reporting to find out what happened.

Diagnostic analysis

Diagnostic analysis wants to know why an event happened. The diagnostic analysis begins with a root cause analysis that defines the problem, collects detailed information like the five Ws (who, what, when, where, and why), and brainstorms the most likely cause. The What-if analysis is also used, and the purpose of this analysis is to change variables to identify the conditions that most likely explain why an event occurred. Correlation analysis, data mining, and drill-down analysis are methods and techniques used to determine why an event happened.

Predictive Analysis

This analysis focuses on future events and uses simulation techniques, regression analysis, and forecasting methods to predict what will happen. Predictive analysis uses machine learning, artificial intelligence, statistical models, and data mining to predict the likelihood of a future event.

Prescriptive analysis

Prescriptive analysis is the most advanced analysis because its goal is to make a particular outcome occur in the future by taking specific actions in the present to achieve a desired future outcome. Prescriptive analysis uses advanced algorithms, data mining techniques, machine learning, heuristics, and statistical methods to help determine what a business must do now to generate a desired outcome in the future. Prescriptive analysis uses complex modeling and descriptive and predictive analyses to help management make the correct decisions in the present that will most likely influence the future outcome the business is trying to make happen.

The AI techniques and statistical methods help businesses improve decision-making, reduce costs, process information faster, and provide the scalability and flexibility needed to meet impromptu demands quickly.

Advanced data analytics helps businesses solve complex problems, derive meaning from unstructured data, reduce risks, improve customer satisfaction, enhance operational efficiency, and better forecast future events.

Risks of using AI in data analytics

Bias in analyzed data is just as bad as corrupted data used to make business decisions. Therefore, when an AI algorithm is being trained, the data analyst needs to ensure the data is unbiased and reflects the current state of a business. Businesses using data analytics need to ensure their data analysts are thoroughly trained to prevent biased data from being used to make a business decision. Bias data can be challenging to detect, so it’s a risk that must be eliminated. 

Other risks of using AI in data analytics are:

Business data manipulation: If a bad actor uses AI, the person can manipulate the algorithm that produces misinformation for a business and its employees.

Data privacy and security breaches: Big data can contain personally identifiable information (PII) about employees and organizations that can be exposed through a security breach committed by a bad actor, who can also be an actual employee.

Job displacement and reduction: These AI-enhanced tools may replace employees without retraining.

Accountability: Who is held accountable when AI and data analytics derive a decision with real-world consequences that lead to an ethical issue involving personnel and potential job losses?

Lack of transparency: An AI algorithm can be complicated to understand, which makes it difficult to explain how it reached a specific conclusion.

Data analytics and AI are not sensitive to ethical issues involving PII, compliance regulations, and personnel assignments, so it’s always essential for a human to review the results of a data analytic-derived decision before it’s executed in a business.

A step-by-step guide to using AI in analytics

The steps in a data analysis process will most likely be the same whether a statistical method, AI tool, or technique is used in an analytical process. However, in today’s business environments, using AI tools is necessary because big data is becoming more prevalent. 

The following steps are the best practices for a data analysis process:

1. Identifying a problem or the specific goal a business wants to attain without waiting for perfect data.
2. Based on the problem or the goal, what types of data needs to be collected?
3. Collect the data using a combination of collection techniques or methods, including data mining, quantitative, or qualitative processes.
4. Clean the collected data by filling in missing values, removing duplicates, correcting outliers and errors, standardizing data formats, converting data types, and checking if the data adheres to predefined rules or constraints.
5. Constantly adhere to the established data governance guidelines throughout the process.
6. Analyze the data using AI tools like ML, NLP, or deep learning techniques and mathematical or statistical methods to extract insightful information businesses can use for decision-making.
7. Strive for Continuous Process Improvement (CPI)
8. Review the extracted information for any ethical issues that may arise if a business moves forward with a decision based on the extracted data.
9. Share the results with any stakeholders or interested parties.

Future AI data analysis trends

Artificial intelligence advancements like generative (AI) can create new content such as images, music, audio, videos, and text that can increase the productivity of any business. Grammarly, ChatGPT, and DALL-E are examples of generative AI that can be a writing assistant, create images when responding to a prompt, or generate text when prompted. 

Other future trends of AI in analytics are:

• Automated storytelling: AI can narrate a data-driven story using a data-documented programmatically
• Conversational analytics: A Chatbot can answer analysis queries through NLP
• Advanced simulations: AI can test thousands of simulations using complex configurations concurrently
• Real-time problem detection: Internet of Things (IoT), edge computing, and live streaming are uncovered before humans realize a problem exists
• Embedded Analytics: Seamlessly embedded models continuously monitor services and products involuntarily or autonomically
• Prescriptive Intelligence: AI-recommended solutions or decisions based on specific scenarios that may possibly produce the desired outcome for a business

Selecting a data analysis application with AI functionality

The operational performance of a data analysis application is a primary metric for selecting a data analysis application. Still, a business must first establish what metrics are needed to measure the performance of an organization. Businesses like manufacturing, financial, healthcare, or retail can begin by looking at business-industry-specific data analysis applications that cater to their needs.

More importantly, businesses must produce a list of the metrics they want to be measured. Companies can measure financial performance, operational efficiency, customer behavior, market trends, maintenance cost, production volume, or revenue growth, including a combination of more than one metric.

Businesses can evaluate a data analysis system with a 30-day trial period or seek out companies already using an analysis system they are interested in for feedback. Hiring a contractor specializing in data analysis applications may be beneficial for any business new to data analysis systems, as they may have a wealth of knowledge you can use to select the best product for your business. 

Frequently Asked Questions (FAQ)

The post How to Use AI in Data Analysis? The Complete Guide appeared first on TechnologyAdvice.

Key takeaways

Data governance is a foundational component of any successful business. It uses information technology (IT) resources to execute internal and external business processes. The IT resources used to perform daily business operations will be useless if the processed data is inaccurate or corrupt. Data governance aims to manage the validity and accuracy of business data from its inception to obsolescence, including the proper disposal of outdated data.

What is data governance?

Data governance consists of a set of processes, procedures, and rules that dictate the proper use of business data throughout its life cycle. The data governance framework ensures business data is accurate, available, usable, secure, and protected for data analysis processes and from unauthorized users gaining access.

Data governance allows authorized users to locate, prepare, use, and share data with other authorized users. It helps businesses safeguard their data, grant access to authorized users, and distribute it as needed.

Read more: Data Governance Can Ease PII Regulation Compliance

Why is data governance important?

Data governance is essential because it outlines the proper actions users must take when using data. It provides approved and authorized processes for users to follow and specifies the authorized technology used throughout the data’s life cycle to ensure it stays within expected parameters. Data governance helps businesses manage and govern proprietary data responsibly and effectively by using approved processes and technology employees use to accomplish daily tasks.

Data governance ensures businesses meet any regulatory compliance for legal obligations. It also improves data quality and efficiency by removing errors or duplicate data from datasets. The data governance framework allows for better decision-making while ensuring the data is secure and protected by preventing data leaks or exposure through an IT vulnerability. Effective data governance ensures consistency and trustworthiness throughout any data’s life cycle.

Read more: What Is Data Mining?

Data governance vs. data management: What’s the difference?

Data governance and data management are integral parts of the data governance framework. Data governance operates strategically by establishing policies, procedures, and strategies for managing data. In contrast, data management is more tactical in how data should be appropriately handled, processed, and used.

Data governance focuses on establishing policies, procedures, and strategies for managing data that involve availability, usability, security, and integrity. Additionally, governance focuses on the people using the data, the technology that manipulates the data, the processes aligned to data roles, and how well the processes support the business’s data strategy.

Data governance establishes the rules, policies, and procedures, including the overall objectives, and data management uses the established specifications from data governance to meet short- and long-term goals and objectives.

Read more: Data Cleaning: Definition, Techniques & Best Practices

Data governance framework

The pillars of the data governance framework are data quality, privacy, compliance, and security. The data governance framework is paramount throughout the data life cycle, as it dictates specific actions from creation to disposal of any data type, including metadata.

The data governance framework is a set of rules, processes, and responsibilities that mandates how businesses manage their data, including accountability to people, processes, and technology.

The purpose of the data governance guide is to ensure data integrity is maintained, allow employees to use data effectively, and protect it from security risks. The data governance framework helps businesses maintain compliance with policies and regulations, promote authorized data sharing, and quantify results.

Benefits and challenges of data governance

Quality decision-making begins with data that truthfully represents an accurate picture of a business’s profits, efficient processes, available product resources, and the ability to promote growth while allowing employees to continue excelling. The data governance guide provides the following benefits:

• Enforce and monitor data policies: Ensures policies, rules, and definitions apply to all business data, including security, accuracy, and quality.
• Increased operational efficiency: Identifies areas of improvement, allowing managers to allocate resources better and adjust workflows as required.
• Regulatory compliance: Ensures businesses comply with regulatory requirements like GDPR, CCPA, and HIPAA.
• Enhanced transparency: Provides a shared understanding across the entire organization.
• Reduced costs: Improves data quality to eliminate or minimize mismanaged data and repeated data mining or data cleansing efforts.
• Improved data management: Establishes structure, policies, and rules for properly managing data throughout its lifecycle.
• Improved data-driven decisions: Ensures data is accurate and trustworthy for better decision-making.
• Enhanced collaboration between teams: Clearly defined data roles and a unified view of data help eliminate silos and encourage better communication.

Challenges of data governance

A data governance program not supported by senior leadership is a significant threat that can eventually lead to failure. A robust data governance program must be supported by all levels of management, including a repeatable data governance training program that holds all employees accountable.

Other challenges include:

• Fragmentation and siloed data: Isolated data must be identified to validate its usefulness.
• Data literacy and culture: All employees need to understand the importance of data to minimize resistance and misuse.
• Limited resources: Budget constraints can hinder acquiring necessary training, tools, and expertise.
• Resistance to change: Some employees may resist adopting a data governance program.
• Poor data quality: Ensuring data accuracy, completeness, and consistency is paramount.
• Data security: Understanding data states helps in applying the best security tools.
• Data accountability: Employees must understand proper data usage; data stewardship teams can help.
• Data integration: Maintaining data trustworthiness after integration is challenging.
• Data compliance: Staying aware of changing compliance regulations is essential.

Read more: Common Data Quality Issues & How to Solve Them

Data governance best practices

Data governance best practices begin with upper management fully supporting data governance and recognizing that best practices must be implemented to maintain accurate and trustworthy data.

• Managing data access: Only authorized and trained users should access data. Data owners must ensure data remains trustworthy.
• Establishing roles and responsibilities: Data stewards and owners maintain consistent data management standards. Administrators oversee data integrity and analytics.
• Develop and enforce data quality standards: Regular checks ensure data maintains established quality standards.
• Stakeholder involvement: Involving stakeholders from different departments ensures organizational alignment.
• Implement data security: Updated security methods like multi-factor authentication prevent unauthorized access.
• Automation: Tools that perform data quality assessments without constant human intervention are essential.
• Adopt a continuous improvement mentality: Continuously challenge the data governance program to adapt to changes.
• Document data: Keep all data documentation, such as metadata and data dictionaries, updated.

Read more: Data Analysis Methods.

Data governance products and solutions

Data governance tools help staff connect data through people, processes, and technology to achieve better business results.

Alation

Alation is a data intelligence platform that helps businesses understand what data exists and how it’s used. It assists in managing risks, compliance, and data privacy issues and aids in cloud migration by identifying dependencies.

SAP Master Data Governance (SAP MDG)

SAP MDG allows businesses to define, enforce, monitor, and improve master data across multiple IT domains. It enhances data quality by adding views of master data associated with relevant information from different applications, ensuring consistency and accuracy.

Collibra

Collibra is a data governance platform that protects and manages data while maximizing its value. It uses a centralized platform for defining, implementing, and tracking data policies and standards, enhancing decision-making and ensuring regulatory compliance.

Frequently Asked Questions (FAQs)

The post What Is Data Governance? appeared first on TechnologyAdvice.

Key takeaways

The Voice over Internet Protocol (VoIP) uses the Session Initiation Protocol (SIP), a universal standard acting as a common language that different devices such as smartphones, computers, tablets, and VoIP phones use to initiate and communicate with other devices. SIP manages and oversees the online initiation, modification, and termination of voice and video session calls.

What is the VoIP protocol?

The Session Initiation Protocol (SIP) is an application layer protocol known as the standard for signaling and controlling VoIP communication sessions. The SIP became a standard by the Internet Engineering Task Force (IETF) in 1999. The Session Initiation Protocol is considered a subprotocol that enables Voice over Internet Protocol communications designed to emulate the functionality of how traditional phones operate. 

The SIP is an essential protocol for VoIP phone services because it removes the need to retrain users when they move from a traditional phone to a VoIP device. The Session Initiation Protocol works with different protocols but works with each one differently.

Read more: VoIP vs. Landline: The Pros and Cons for Business

Most popular VoIP protocols?

The protocols used on an active VoIP call are SIP, Real-Time Transport Protocol (RTP), Session Description Protocol (SDP), and Medial Gateway Control Protocol (MGCP). The other relevant protocols are User Datagram Protocol (UDP), H.323, Secure RTP (SRTP), and RTP Control Protocol (RTCP). 

Each protocol used in a VoIP phone communication session does the following:

Session Initiation Protocol (SIP) 

Initiates the call by sending messages like call setup, ringing, and call disconnect to facilitate the call between calling participants.

Real-time Transport Protocol (RTP) 

After the call is established, RTP carries the digital voice packets across the network while ensuring the timely delivery of each packet for smooth audio transmission.

Session Description Protocol (SDP)

This protocol uses a text-based format to describe multimedia session parameters and sends information about the session when multimedia data is transferred across a network.

Medial Gateway Control Protocol (MGCP) 

The MGCP is a communication channel between the call control device (Call Agent) and the Media Gateway to manage the call and send commands to the gateway to control routing, codec selection, volume changes, and call termination. The call agent monitors and reports the status of the call.

User Datagram Protocol (UDP) 

The UDP is responsible for rapidly transmitting voice data packets between the caller and receiver and prioritizes speed over guaranteed delivery. The UDP sends packets as quickly as possible without waiting for delivery confirmation. The UDP does not establish a dedicated connection before sending data packets, as it is primarily focused on faster transmission. 

Secure RTP (SRTP)

The SRTP protects voice over IP calls to prevent eavesdropping by encrypting the RTP packets to prevent unauthorized replaying or reordering of data packets. 

RTP Control Protocol (RTCP)

The RTCP gathers statistics on quality during an active VoIP call. The RTP carries the media streams, and the RTCP uses the RTC-collected data to monitor transmission statistics and quality of service (QoS). 

Less common VoIP protocols

H.323

The SIP and H.323 were created to manage voice or video communication over an IP network, but the H.323 was not designed to handle instant messaging. Even though both protocols were created in the mid to late 1990s, the SIP is more advantageous because it can manage voice, video, and messaging. Additionally, the Session Initiation Protocol is a more flexible and scalable protocol that uses a modular architecture that can handle call setup and management processes more efficiently than H.323. 

The H.323 is a connection-oriented protocol requiring a connection before exchanging data. In contrast, the SIP is a connectionless protocol that focuses on faster communication and does not require setup time.

XMPP and Jungle

These two protocols are primarily used for peer-to-peer (P2P) transmissions, focusing on instant messaging. Jingle improves instant messaging with VoIP and videoconferencing features.

Inter-Asterisk Exchange (IAX)

Used as a reliable option for VoIP termination for service providers using Asterisk-based solutions. Inter-Asterisk Exchange uses a single UDP port for both media traffic and signaling that reduces the need for sophisticated Network Address Translation (NAT) and firewall configurations

Skinny Client Control Protocol (SCCP)

Skinny Client Control Protocol is a Cisco-proprietary protocol that controls signaling and available features on Cisco IP phones, endpoint devices, or embedded systems.

What are the key features of VoIP protocols?

VoIP phone systems have many key features that would be an additional cost if added to a traditional phone line. 

The key features of various VoIP protocols include:

• Call forwarding automatically forwards calls to one or more numbers or extensions, allowing the recipient to take calls at any location.
• Call recording records audio of a phone call or a text transcript of a call, which can happen automatically or on demand.
• Call routing answers calls automatically and distributes the calls to the correct queue, allowing a person or group to answer the call.
• Caller ID displays the calling number that a business can use to prioritize important calls.
• Call queuing prevents calls from being terminated at the busy signal or sent to voicemail.
• HD voices provide very clear audio quality.
• Auto attendant is a menu system that answers calls and routes them to the correct person or department.
• Video conferencing is a VoIP feature that allows virtual meetings using audio and video for two-way communication.
• Call screening helps a business or user decide whether to accept or reject the incoming call, and this feature uses caller ID.
• Voicemail transcription transcribed an audio voicemail and sends it to the user’s email.
• Call pulling moves a phone conversation from one device to another, allowing the salesperson or agent to be more mobile by using cellular networks when required.
• Call analytics is used to track incoming call volume, call time, hold time, and call performance, including customer behavior and agent performance. 
• Auto dialers automatically dial numbers from a contact list, which allows call center agents to quickly connect with potential clients without the agent wasting time dialing numbers.
• Call monitoring is used by management to listen to or participate in a call without the customer being aware that a manager is on the call to provide guidance.
• Call notify use pre-defined criteria about an expected call and notify a user by email that the scheduled call is occurring or has occurred.

Frequently Asked Questions (FAQs)

The post What Protocol Is Used To Initiate VoIP? appeared first on TechnologyAdvice.

Key takeaways

Voice over Internet Protocol (VoIP) is a modern telephony technology that uses the same resources as computers to communicate using Information Technology (IT) equipment. Modern telephony technology allows users to communicate audibly using VoIP devices or computer resources.

What is VoIP? 

Voice over Internet Protocol is a technology that allows users to make voice calls by converting voice into a digital signal and sending it over the internet in data packets using IT equipment. Voice over Internet Protocol uses packet switching that is different from circuit switching used by traditional telephones in homes and business offices. VoIP provides more communication options by supporting audio calls, video calls, voicemail, instant messaging, team chats, email, and text messaging using a VoIP phone or a computer device.

Read more: What is VoIP? A Comprehensive Guide

How reliable is VoIP?

Voice over Internet Protocol phone systems have proven more reliable than traditional or landline phone systems. VoIP phone systems do not rely on physical infrastructure such as telephone wires or a telecommunications facility that connects subscribers’ phones and transfers calls between subscribers. The traditional phone system’s physical infrastructure is more exposed to natural disasters, energy surges, traffic congestion, and wear and tear, making landline systems more vulnerable than VoIP systems.

Redundancy contributes significantly to ensuring VoIP providers are functional by implementing redundant data centers that can reroute calls during any potential outage. Disaster recovery systems can forward calls to employee devices during an ongoing outage or emergency. Unlike traditional phone systems, VoIP doesn’t rely on physical infrastructure exposed to natural disasters like hurricanes or tornadoes. 

Flexibility is another feature of VoIP systems that makes them reliable. Employees with an internet connection can connect anywhere globally and communicate. Finally, VoIP systems are less susceptible to network and hardware outages due to best practices like monitoring frequently, network redundancy, regular network updates, and having a backup plan.

Common VoIP problems

VoIP systems are far better than traditional phone systems due to VoIP’s reliability, cost savings, and the flexibility to make calls from anywhere with an internet connection. At times, businesses will have to troubleshoot VoIP problems, and here are some common issues to address and how to fix them.

Jitter and latency

Jitter results from data packets of an active phone call being sent out or ordered to a recipient, making the conversation hard to understand. See Figure 1. Latency occurs when there is an audio delay, and the speaker’s voice takes a long time to reach the recipient. See Figure 2. To avoid jitter or latency, run a VoIP speed test. Removing these two issues requires jitter below 30 milliseconds (ms) and latency below 150 ms. 

Businesses can avoid these problems by purchasing a new router that supports faster data speeds or reducing local network traffic by limiting the number of concurrent users. You can also ask your Internet Service Provider to upgrade your internet connection. Installing a jitter buffer may cause a slight delay, but the delay is because the buffer may be reordering the data packets in real time. 

Broken or choppy audio

Broken or choppy occurs when the VoIP call is not fully connected, which means certain words are dropped, and voices are cut in and out during the call. You can use the VoIP speed test to confirm that the jitter is below 30 ms. If the jitter is higher than 30 ms, it indicates insufficient bandwidth.

Reducing the number of users or segmenting the network by removing unnecessary traffic from a specific network segment can reduce the jitter. Data-intensive activities like streaming, gaming, or video conferencing can be moved to another network segment to minimize jitter. Adjusting your Quality of Service (QoS) settings will enable you to prioritize specific devices or data types to access the bandwidth for VoIP services over other devices and data types. VoIP phones can use a wireless network to communicate, but if the Wi-Fi is intermittent, consider switching to an Ethernet cable connection to the internet.

Phone echoing

Echoing occurs when one or more voices are loudly repeated during the call, interrupting other recipients from hearing the conversation. 

The common causes of echoing are:

• A recipient has a speakerphone on
• Electromagnetic interference
• Damaged equipment
• Slow internet

To combat echoing, users must turn off speakerphones. If a user is using a headset, the headset will need to be tested by making a call with a headset not used on the call or a call without a headset. If the headset continues making noises on a different call, the headset must be replaced. 

An internet connection or bandwidth issue can be the cause of the echoing. Businesses can check the internet speeds using an internet speed test. If your speed test is insufficient for VoIP traffic, you can contact your internet service provider to explore options for increasing speeds. Electronic magnetic interference occurs when electronic devices are too close together.

No sound after the call connects

When a connection is established and no sound is heard, the most likely cause is data packets going to the wrong IP address or the firewall is blocking inbound data packets. A system administrator may also need to check that the Session Initiation Protocol (SIP) Application Layer Gateway (ALG) is turned off. The SIP ALG inspects VoIP traffic to prevent problems with firewalls, and it’s often turned on as a default setting on routers and broadband hubs. Real-time Protocol (RTP) and User Datagram Protocol (UDP) packets are required for VoIP services.

Also, all recipients’ VoIP extension’s Codec settings should be checked to ensure they use the VoIP codec (G.711, G.722, Opus) that compresses and transmits all audio data during a call.

Phone doesn’t ring on inbound calls

A VoIP phone not ringing when you’re expecting a call from a potential client can lead to losing a potential client. The first item to check is ensuring your VoIP phone is not set to Do Not Disturb and checking your call forwarding setting to ensure the expected call was not redirected. Make sure your VoIP phone is registered with your current VoIP service provider and that the phone is logged into your VoIP application.

Additionally, check for damaged cable connections and ensure the cables are securely connected to the correct devices. Ensure the router is in an open area and not blocked by a door or wall that can hinder the Wi-Fi connectivity. Lastly, check your router to ensure it’s functioning correctly and has enough bandwidth to handle the expected call volume concurrently. Adjust your router’s Quality of Service feature to prioritize specific devices and the associated bandwidth.

Dropped calls

The most likely cause of dropped calls is insufficient bandwidth. Another possible cause of dropped calls is the software may be incompatible with the VoIP hardware and equipment. A software update may be required to ensure compatibility between the software and hardware. Check with your VoIP service provider to ensure your VoIP system has the latest software updates. 

If the problem is still unresolved, disconnect all devices connected to the VoIP network and reconnect one device at a time to see if you can isolate and replace the bad device. Check your UDP timeout and Router timeout settings to verify that they are not causing the dropped calls, and call your VoIP service provider to assist in checking the timeout settings.

One VoIP phone works while another one doesn’t

If one VoIP phone works and another doesn’t, the VoIP phone that doesn’t work may not have its MAC address registered or not correctly configured and needs updating. Make sure the MAC address is registered and the configuration is updated. Take the non-functioning VoIP phone to a working office space with a working network port and test the phone again. Contact your VoIP service provider for firmware or configuration updates if applied updates do not work. If neither update works, the VoIP phone will need to be replaced.

Best practices to implement to avoid VoIP problems

A robust and reliable VoIP phone system starts with implementing the known best practices. A reliable VoIP system begins with selecting a good VoIP service provider who follows the best practices consistently. A vital element of a good VoIP phone system starts with network stability.

Network stability

• Bandwidth requirements: must be adequate to handle VoIP calls with minimal packet loss; therefore, bandwidth must be between 5-25Mbps.
• QoS configuration: This helps prioritize VoIP traffic on routers by adjusting the QoS setting.
• Router selection: Select a router specifically designed for VoIP traffic.

Selecting a hosted VoIP service provider

• Reliability: Select a VoIP provider with a history of high uptime and reliable service.
• Customer Support: Choose a VoIP provider with good 24/7 customer support and available technicians who can troubleshoot.

Read more: What is Hosted VoIP? A Complete Guide

Device management

• Software updates: Updates are applied to VoIP software and firmware that addresses security vulnerabilities and bugs, as necessary.
• Hardware checks: routinely schedule checks for loose cables and damaged connections.
• Device configuration: Verifies VoIP phones are correctly configured.

Security practices

• Strong passwords: The use of complex passwords is mandatory.
• Two-factor authentication: Enabling two-factor authentication is required.
• Encryption: Confirm your VoIP service provider implements end-to-end encryption for secure communication.

Read more: VoIP Security Guide: Tips, Risks & Security

Troubleshooting practices

Run a speed test: Perform a regularly scheduled speed test to address any latency or other potential concerns.

Disable SIP ALG: Turn off this setting to prevent any interference with VoIP traffic.

Check network settings: Review network settings to verify proper routing and firewall configuration settings.

Strategies for Troubleshooting VoIP Problems

Using established best practices can help minimize VoIP problems so that troubleshooting is a rare occurrence. Typically, some components in a VoIP phone system will require troubleshooting steps to identify the problem.

• Checking your network configuration: Ensure your firewall, routers, and switches are configured to support VoIP traffic.
• Internet and VoIP speed test: Running speed tests to check for latency or bandwidth issues as required is a necessary test when performance is noticeably slower or inconsistent.
• Configure QoS: Ensure VoIP traffic is prioritized over other data types on routers and switches.
• Update firmware and software: Phones, routers, and all associated VoIP devices must have the latest version, as required.
• Change the UDP timeout duration: Increase the timeout to at least 60 seconds, allowing for longer calls.
• Disable SIP ALG: Disable the SIP ALG setting to eliminate the effect on VoIP data streams, which is enabled on commercial routers.
• Use a Virtual Private Network (VPN): A VPN separates your VoIP traffic from all other types of traffic, minimizing internet congestion on the VPN.

Frequently Asked Questions (FAQ)

The post VoIP Troubleshooting: 7 Common Problems and How to Fix Them appeared first on TechnologyAdvice.

Key takeaways

The phrase number porting describes when a person or a business wants to move an existing phone number to another communications provider (CP) and keep the same phone number they had with the previous CP. This article will walk you through the steps to successfully move an active phone number to a different CP.

What is VoIP number porting?

When VoIP number porting occurs, business landlines are primarily ported to a VoIP service provider. Businesses that move a phone number from a traditional phone company to a VoIP service provider remove the burden of notifying clients about a new phone number. Businesses save money by eliminating the need to create new marketing and advertising material with updated contact information while allowing clients to contact them despite moving to a VoIP service provider. A mobile or VoIP phone number can also be ported to a VoIP service provider. However, the process is slightly different because landlines are assigned to a Plain Old Telephone Service (POTS) phone and a specific location.

Why do you need to port a VoIP number?

A landline phone ported to a VoIP service provider allows your VoIP phone to use a VoIP port to connect to your computer and the internet. In addition to keeping your phone number, a business’s customers will have additional methods to communicate with the company. Additionally, after phone numbers are ported, the cost savings can be between 40% and 90%, as VoIP service providers are generally always cheaper than landline phone providers. Avoiding printing new marketing material and business cards also generates cost savings. Brand consistency is maintained in online listings, email signatures, and website contact information. 

Porting to a VoIP number offers several additional features at minimal or no cost compared to adding advanced phone features to a landline phone. Advanced phone features come as standard options with a VoIP phone system that can be an additional cost if added to a landline phone. VoIP phones are more flexible and scalable than landline phones because VoIP phones are internet-connected, allowing the VoIP service to use hardware devices capable of making phone calls, too.

How to port to a VoIP number

Before porting a number to a VoIP service provider, there are specific steps to take. The first step is to confirm that your phone number is eligible to be ported to a VoIP provider because some providers do not support specific area codes or locations. Make sure your internet provider can support the selected VoIP provider’s bandwidth requirements and ensure your phone is compatible with the VoIP provider. 

Verify that you are the only one using the phone number being ported. You can check by doing a reverse phone lookup with a service like Whitepages or Spokeo to confirm. After these pre-port requirements are verified, you can start making calls to your current provider and your selected VoIP provider to execute the following steps:

1. Contact your current provider and let your new VoIP provider know you want to port a number.
2. Review and select a VoIP plan and create an account with your VoIP provider.
3. Fill out the necessary forms and provide the following information:
   1. Provide information about the number you are porting and include your account number and the name of the current provider
   2. Sign a Letter of Authorization (LOA) and share the PIN
   3. Ensure the person authorizing the port move is the same person signing the additional paperwork.
4. Provide an accurate billing address, and PO Box addresses are not an option
5. Keep your current landline phone service active until the porting process is complete, and in the meantime, you can use a temporary number to forward your calls until the porting is complete.
6. Pay any fees, which may include a porting fee
7. The porting process can take as long as ten business days, but each VoIP provider has different timelines for completion. Check with your selected provider for an estimated completion date.
8. Close your old service provider account after confirming the number has successfully been ported to the VoIP provider. See Figure 1 to review the steps to port a number to a VoIP provider.

Is porting a landline or mobile phone different from a VoIP phone?

The porting process is the same across landline, mobile, and VoIP numbers, and the difference boils down to how many numbers are being ported. The most notable difference when porting VoIP, landline, or mobile numbers is the time it takes to complete the porting process. Mobile or cellular, VoIP, and landline phones have different timelines for completing a successful port, but companies like Verizon and AT&T have their porting process with established timelines.

 Landline numbers use Local Number Portability (LNP) and can port numbers to VoIP providers the fastest. Mobile numbers use Mobile Number Portability (MNP), which takes 5-10 business days to port number(s). Porting VoIP numbers takes the longest due to making the number accessible across multiple devices, and the completion timeline is similar to mobile numbers.

For any business going through the porting process, you can use call forwarding through Interim Number Portability (INP) or get a temporary number from your new provider. One common delay, regardless of the type of number being ported, is incorrect or missing documentation. For any CP to meet their estimated completion timeline, submitting correctly filled-out documentation will help the provider meet their completion dates.

Requirements for VoIP porting

Specific requirements must be met before a number is ported to a new provider. The account must be in good standing with your current provider without a history of past-due bills. You must be with your current service provider for at least 60 days before you can cancel. As mentioned earlier, your phone must be currently active. You own the phone number exclusively and sign and provide a LOA with a recent phone bill that shows the number you are porting. Again, verify the selected VoIP provider covers the location you plan to work from with VoIP phones

Ensure your phone(s) is compatible with the selected VoIP provider and provide a list of phone numbers along with the porting out PIN information that authorizes the port transfer. Sometimes, the new VoIP provider will contact the current provider on your behalf and get the required information. The porting process can take from 5 days up to 6 weeks, depending on the number of phones and the complexity of the task.

Benefits and challenges of VoIP number porting

There are pros and cons when porting to a VoIP number. An advantage of porting to a VoIP provider is the portability, which keeps teams and customers connected regardless of location. VoIP phone systems are excellent for companies that want to excel at customer service due to the available communication options. Portability allows 24/7 access to files, features, and contacts as long as a good internet connection exists. VoIP phones provide excellent voice quality due to packet switching technology that minimizes jitters in voice quality, connectivity problems, and call drops. Other notable benefits are the following:

• Lower costs: Advanced features come as standard features with a VoIP phone, and it’s easier to upgrade or modify your existing contract to meet any new business needs.
• Integration: VoIP phone systems integrate easily with customer-focused business applications like Customer Relationship Management (CRM) software, text messaging software and email.
• Mobility: A working internet connection will allow an employee to make or receive a call anywhere in the world.
• Advanced phone features: Call management features such as voicemail, call forwarding, call routing, and call parking are advanced features that help businesses distribute calls to the appropriate person.
• Conference calling: Conference calls can be audio or video meetings with screen share functionality over the internet.
• Toll-free numbers: Business VoIP phone system can include toll-free numbers, which is ideal for a startup or small business.
• Flexibility and scalability: VoIP phone systems are flexible and capable of adapting to change quickly, scaling up to meet increasing workloads and scaling down when required.
• Advanced analytics: VoIP systems allow for real-time monitoring and conversation intelligence, allowing management to gain insight into customers’ expectations and sentiments.
• Unified Communications (UCaaS): It helps improve user experience by providing users with different communication methods such as desktop, laptop, tablet or smartphone to communicate seamlessly.
• Toll-free numbers: Some business VoIP systems provide toll-free numbers, while some providers offer free VoIP phone services. 

Challenges of VoIP number porting

On the flip side of the benefits of VoIP number porting, there can be some challenges a business may need to overcome. The most concerning issue is that VoIP services may not be compatible with emergency services. Some VoIP providers may not have an interconnection agreement with your current phone service provider. Other known challenges that may arise are:

• Coverage: A specific VoIP provider may not provide coverage in your area.
• Bandwidth limitations: The VoIP phone service will most likely run on your business network, and the additional bandwidth requirement for VoIP services may impact existing IT resources on the business network, which can degrade the network for all IT services.
• Phone number status: A projected number identified as portable may be inactive, which means it cannot be ported.
• Geographic restrictions: A possibility exists that some phone numbers may be restricted from being ported due to service areas or rate centers due to calling boundaries for specific phone numbers.
• Security: VoIP phone systems may be vulnerable to fraud that allows scammers to hijack lines to make calls that the business must pay for.
• Documentation errors: Incomplete or errors in the required documentation can cause unexpected delays between service providers.
• Network architecture compatibility: A network upgrade may be needed to accommodate the VoIP provider services.

Best practices for VoIP number porting

Change will always occur as IT technology advances to improve business operations, but a backup plan can minimize downtime if an unplanned incident occurs. Best practices are essential if something unanticipated happens, and it begins with a backup plan. Communicating early and regularly with your current and new provider is a crucial best practice. Other best practices are:

• Understand the terms and conditions: Ask as many questions as necessary until you clearly understand the service agreement, pricing, and any potential future upgrades.
• Configure your VoIP account: As soon as your VoIP account is created, log in and set up your account.
• Coordinate porting dates: Coordinate proposed porting dates that minimize disrupting your business operations.
• Comply with porting requirements: Verify all porting requirements are met, including active number, no pending orders, and no outstanding balances.
• Eligible phones: Not all phones are eligible for porting as some phones may have restrictions such as prepaid phone numbers.

Guide to choosing a new VoIP provider

A business looking for a VoIP provider can start by forming a committee from various departments to garner input from every department. Companies must decide on the type of VoIP system needed and the primary features the VoIP system will need to conduct everyday business. What will also influence your business decision is whether you are getting a new VoIP system, replacing an old system, or expanding an existing VoIP system. If VoIP technology is new to an organization, contracted assistance may be required.

The selection process can begin by reviewing VoIP’s customer comments on various websites.

1. Reviewing different website reviews will provide a holistic view of their customer base to assess a VoIP provider’s performance.
2. Select a VoIP system and must-have features:
   1. Hosted PBX – hosted on a business on-site location in a data center
   2. Cloud-based – uses cloud-based technology that allows users to execute mobile VoIP functionality globally with a good internet connection
   3. Unified Communication (UC) – is a complete business communication suite designed for teams working remotely routinely. Unified Communication includes video conferencing, instant messaging, screen sharing, call forwarding, and more advanced features
   4. Communication Platform as a Service (CPaaS) – is more robust than UC, featuring high-tech integrations and advanced functionality
   5. What communications features are needed
3. How many users need VoIP phones
4. Determine the monthly or annual budget requirement
5. What are your customer support requirements
6. Any VoIP provider issues, such as security or latency that are notable
7. Compare your top VoIP providers and what’s uniquely offered, and compare what features are included as standard options
8. Present the findings to the CIO or committee chair for final approval.  See Figure 2.

Frequently Asked Questions (FAQ)

The post Number Porting: Overview, Definition & Process appeared first on TechnologyAdvice.