Key takeaways

  • Voluminous data in organizations stored as structured or unstructured data requires AI in a data analysis process to discover useful information a human cannot detect.
  • Multiple AI techniques and statistical/mathematical methods are used in a data analysis process to extract the exact data a business can use for decision-making.
  • Using AI and data analytics for decision-making does not mean a business will always make the right decision.

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.

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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.

Expert Tip

Other valuable data analysis processes include exploratory data analysis and inferential analysis. Many different statistical methods can be applied to any analysis technique to help a business make the best decisions in the present or future.

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.

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)

AI data analytics combines AI and the data analytics process to collect, prepare, analyze, and make decisions using advanced AI technologies like ML, MLP, and data visualization.

Coding is not explicitly required with available resources like automated Machine Learning (AutoML) and pre-built models. Still, if you train ML models, you may be required to learn Python, R, or C++, which are all programming languages.

Yes, Julius AI, Tableau, Power BI, and Databrick are examples of AI tools that can automate data cleaning, perform predictive analysis, customize data visualizations, perform text analysis, and combine different data warehouses and lakes into a single cloud-based platform, known as a data lakehouse.

An AI analyst uses data analysis and AI to clean and prepare data for analysis using a variety of AI techniques and statistical or mathematical methods. AI analysts focus on uncovering hidden patterns or correlations in large data sets that can provide better insight into the business decision-makers use to make better decisions for the organization.

Don Hall Avatar
Don Hall
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Don Hall is a contributing writer to Technology Advice and Enterprise Storage Forum, where he covers data storage technology, storage hardware and software, and data networking. He worked for more than two decades as an IT Supervisor for the federal government and as IT Operations Supervisor for an IT Military Command managing programmers, cybersecurity staff, and infrastructure and networking personnel. Previously he worked as an application programmer. Don earned a B.S. in Business Information Systems from San Diego State University and has certificates in Technical Communication and web development with an emphasis in Java/Open Source. He has also had an active CompTIA Security + (ce) since 2011, and a Network +(ce) since 2015.