Key takeaways
What is data analytics?
Data Analytics processes analyze data executed in a data analysis operation or Business Intelligence (BI) project. While data analysis is a process that collects, cleans, transforms, and models the data into an easily understood visual representation, data analytics uses the processed data to produce actionable insights, helping businesses make informed data-driven decisions. Therefore, data analytics uses data analysis to convert raw structured, semi-structured, and unstructured data into actionable insights, allowing businesses to improve decision-making and promote business growth.
Data analytics uses Artificial Intelligence (AI) tools like Machine Learning (ML) and Neural network techniques, including various statistical methods in the data analysis processes to generate meaningful and accurate information for data-driven decision-making. Data analysis is a subset of data analytics.
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The steps of data analytics
The steps of data analytics are the steps in the data analysis process. Before actionable information is derived from raw data, the structured, unstructured, or semi-structured data must go through a multi-step process to become meaningful, actionable information used to make data-driven decisions. The multi-step process begins with identifying the problem and asking the right questions.
Read more: Data Analysis Methods & Techniques for Business
Identify the issue or situation and ask questions
The first step is to ask questions about a specific objective and what the business wants to accomplish. This question is crucial because it can skew the remaining steps in the data analytics process if it doesn’t address the particular objective. Therefore, individuals involved must clearly understand the problem or situation to formulate questions.
Using the data mining techniques and methods is one of the primary steps in data analytics, and the following is a walkthrough of the proper steps.
Prepare and collect data
The data mining process collects and prepares relevant data to meet the objective identified. The data collection methods or techniques can also be qualitative (non-numerical) or quantitative (numerical). Qualitative collection methods are surveys, interviews, and observations.
Extracting data from multiple sources such as databases, spreadsheets, or text files requires AI techniques or statistical methods like regression or clustering.
Expert Tip
In contrast, AI techniques are descriptive or predictive analytics modeling. Hence, the data collection method chosen needs to be able to address the objective you are trying to accomplish.
In some cases, big data is a large data set too large to use traditional collection methods and requires specialized collection software, like MongoDB, KNIME, or Apache Flink. Once the data is collected, it may need to be prepared, if required. Data can be normalized, consolidated into raw data sets, or receive new attributes or dimensions, which are a few examples of data preparation.
Clean and process the data
Data cleansing is an essential step in data analytics and occurs in the data mining process. In this step, you’ll check the data for inconsistencies and errors that must be removed or corrected. The cleansing of the data validates its quality and reliability. This step ensures the data will generate meaningful results during the data analysis process.
Analyze the data
After cleansing the data, you’ll use AI tools and mathematical or statistical techniques to find insightful information, such as trends, patterns, and relationships. Programming software like R and Python are used in the data analysis process. R is a statistical programming language that can help with data cleaning, analysis, and visualization.
Python is a general-purpose language for various tasks, such as data manipulation and machine learning. In addition to using mathematical techniques, you can also use Machine Learning (ML) algorithms, Natural Language Processing, or Deep Learning, which are sub-categories under the domain of AI that can help find insightful information.
Read more: What is Data Analysis?
Share the results
The analyzed data are the results or findings that need to be shared with interested stakeholders. Interested parties must easily interpret the results; therefore, the best way to show results is to display them visually with a chart, graph, or other visual representations. Visually displayed data helps the audience better understand complex data while clearly showing the results.
Data storytelling is another way to share results. Storytelling analysis converts complex data analysis into an easily digestible story for audiences that typically don’t interpret analyzed data. Storytelling uses a narrative form that is easy to understand and can help a non-technical audience better understand the findings of analyzed data.
Act or report on results
The final step is to act on the findings by making a data-driven decision for a business project or data analysis process. The finding can also be a report that accurately updates a specific business operation or situation and is compared against an established Key Performance Indicator (KPI).
Read more: Advanced Analytics Guide: Definition, Benefits, & Techniques
What are the types of data analytics?
Multiple business data analytics methods and techniques help businesses. The most common are descriptive, diagnostic, predictive, and prescriptive, which are used in the data analysis process. Yet, other statistical methods also play an essential role in data analytics, including emerging technology like generative BI that integrates with analytics and expedites analyzed results. Generative BI can automatically create dashboards, find data anomalies, and make predictions quickly while also providing self-service analytics that non-technical users can easily understand.
Generative BI enhances these data analysis techniques and methods to help extract information from databases, identify trends and patterns, optimize marketing campaigns, and improve operational efficiency. It is a no-code solution that uses AI tools to provide proactive rather than reactive insights.
Business data analytics helps businesses reduce costs, better understand their customers, boost revenues, improve security, and provide better problem-solving strategies.
Some of the most helpful business data analytics tools used in the data analysis processes are the following:
Quantitative analysis
Quantitative data focuses on numerical data and uses measurements, mathematics, and statistical modeling to derive a numeric value based on the inputs. This type of analysis can be used for risk management, credit analysis, inventory management, and financial decisions. Quantitative analysis is objective and uses concrete numbers to remove variability, making the results accurate and reliable.
Qualitative analysis
Qualitative analysis is subjected to interpretation. Qualitative analysis uses interviews, observation, surveys, case studies, and focus groups that can be ambiguous information that is challenging to measure. This type of analysis is ideal for getting input from groups of people that can help businesses understand their perspective. For example, a retail business can use qualitative research to understand a target audience’s purchasing preferences.
Predictive analysis
Predictive analysis attempts to predict future outcomes, using historical data to make projections about the future. Predictive analytics uses artificial intelligence, machine learning, and mathematical and statistical methods to predict the value of something or the outcome of future events, such as projected sales revenue, detecting illness, or weather forecasts.
Descriptive analysis
The descriptive analysis describes what happened while trying to answer the macro-level questions like Who, What, Where, and When.
Descriptive analysis will use historical data to review and understand what has occurred in the past. For research, descriptive analysis uses statistical techniques like data dispersion and measures of central tendency to identify patterns and trends, summarize data points, and relationships in data.
Diagnostic analysis
Diagnostic analysis reveals why something happened or the root cause of an event. This analysis will use probability theory, regression analysis, clustering analysis, filtering, data drilling, data mining, and time-series analysis to find the why of an event. For example, a business shows two consecutive months of negative revenues, so the descriptive analysis provided this information but not the why.
The diagnostic analysis will evaluate all internal and external data sources using data mining and drilling techniques. After all the relevant data is collected, additional mathematical computations will be run on the accounting information to find out what is different about the last two months to help explain why.
Prescriptive analysis
Prescriptive Analysis is the most advanced type of data analysis, focusing on how to make an event happen. It can be used with any combination of descriptive, diagnostic, or predictive analysis, including all four, to predict a future event a business purposely wants to happen. Prescriptive analysis uses artificial intelligence, machine learning, and any mathematical or statistical calculations that can influence a future outcome beneficial to a business.
Inferential analysis
Inferential analysis uses a small sample size of data from a larger data pool or population to draw conclusions or predictions about the larger population. This type of analysis is a branch of statistics used to provide information about the larger sample size or population by only using a small sample size pulled from the larger data pool.
Statistical analysis
Statistical analysis is straightforward; it collects and analyzes large volumes of data to identify patterns and trends. Statistical analysis takes raw data to find correlations between variables that interested stakeholders can use to make informed decisions. Statistical analysis is helpful in many different businesses, such as health care departments, quality control departments of a company, weather forecasting, or sales tracking for retail organizations.
Text analysis
Text analysis involves machine learning techniques using computers to read and understand human-written text. It helps extract specific text from unstructured text data. Text analysis is also called content analysis since it can classify, sort, and extract information from text documents. Businesses can use content analysis to quickly digest and summarize online documents, which can help improve data-driven decision-making.
Examples of the data analysis process
Data analysis is used in many business industries today to help organizations improve business operations and make better decisions by using analyzed data that is accurate and insightful. The data process steps are the same regardless of the business industry.
These steps are repeatable for any type of business doing data analysis. The analyzed data results can be shared through a narrative report or a visual chart, though a chart is much easier for non-technical personnel to assimilate and digest.
In the same way, the data analysis process does not change much. The same cannot be said for data analytics, as emerging technology like generative BI enhances the data analysis process. The two tools used in data analysis are AI and mathematics, including statistics.
The healthcare industry uses data analysis to predict disease outbreaks, estimate probable diagnoses, and improve patient care, including data-driven decisions about treatment strategies. Marketing and data advertising firms use data analysis to help them identify the latest purchasing trends. Financial and educational institutions, enterprise organizations, and IT businesses all benefit from using data derived from data analysis to protect their proprietary information, reduce potential cybersecurity attacks, and deny fraud attempts.
Read more: What is Descriptive Analytics?
What are the benefits of data analytics?
As Data Analytics continues to improve with emerging technology like generative BI, data analytics becomes more beneficial when companies exploit the advantages of using data analytic tools. Data Analytics benefits businesses with a variety of actionable insights that include:
Lower costs: Data analytics help businesses lower costs by providing actionable reports that are easy to understand. This allows companies to reduce costs based on data-driven information in reports or dashboards.
Improved decision-making: Companies can use analyzed data and data-driven information to enhance decision-making, leading to better efficiencies in overall business operations.
Risk management: Data analytics can reduce risks by detecting abnormal behavior, suspicious patterns, and market trends to prevent financial losses or business reputation.
Improve customer retention and satisfaction: By helping companies respond to customer problems faster, they can improve customer loyalty, better identify customer preferences, and grow their customer base through satisfied customers promoting services or products to family and friends.
Competitive advantage: Businesses using data analytics can make faster and more accurate decisions in real-time about changing markets, supply operations, or external environmental factors like political, social, or demographic concerns.
Streamline operations: Data analytics improves business performance, time management, and company efficiency.
Data analytics recursively improves every aspect of business operations for internal employees, external customers, and business partnerships, making an entire organization more productive and efficient.
What are the challenges of data analytics?
Data quality is always an issue. Poor data can lead to erroneous decisions or misleading insights, leading to revenue loss, an incorrect hospital treatment strategy, or an unpatched cyber security vulnerability. A labor force that lacks the skills to select and understand how to use the correct data analysis tool can hinder the production of the most accurate data for decision-making.
The lack of a data governance board can impact data privacy, improperly validating data before it’s saved, and the uncertainty or looseness of a data management plan makes it extremely difficult to select the correct dataset for data analysis. Forming a data governance program needs to be a priority to prevent any legal, financial, or operational violations from occurring when handling sensitive data. Therefore, a data governance program must be essential to any data analytic solution selected.
Read more: Top Prescriptive Analytics Tools
Choosing the best data analysis software solution for your business
Whatever your industry-specific business does indicates the type of data analytic software solution you need to look for. If you work in a hospital or retail, find a data analytic solution focusing on that business type. If you can’t find a solution that addresses your business needs, you can use our data analytic software guide to narrow your choices.
Your final decision will be based on the 3 to 5 must-have analysis features you know your business will use consistently. Once you have validated the must-have analysis features, involve the staff in using the analysis tools to validate their usefulness.
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