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Business Analytics is a phrase that can mean a lot of things to a lot of people. This article separates out the topic into four categories, and explain how each type can benefit HR. Those four types are Descriptive, Diagnostic, Predictive and Prescriptive. (Some definitions of business analytics focus on three types by eliminating Diagnostic.) Businesses today are inundated with data from a wide range of sources: accounting, manufacturing, websites, CRM, etc. The larger the company, the more data. Too often management can be so overwhelmed with data and analytics that it begins to lose its value. For CEOs, CFOs, and owners, developing processes and investing in solutions to take full advantage of available analytics is key to successfully managing business growth. This is particularly true when preparing for a potential transaction or capital raise, in the short- or long-term. The first step to implementing a system is understanding the range of analytics that exist. Below is an overview of the four basic types and how they are used. 

1. Descriptive Analytics: Descriptive analytics is the simplest form of data analysis, and its primary goal is to summarize and describe data. It helps us understand what has happened in the past, and it’s often the first step in the data analysis process. This type of analytics answers the question “What happened?”Descriptive analytics techniques include:

• Data Aggregation: Combining data from multiple sources to provide a summary.


• Data Mining: Identifying patterns and relationships within a data set.


• Data Visualization: Presenting data in graphical formats to make it easier to understand.


• Data Summarization: Reducing the amount of data while maintaining important information.

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2. Diagnostic Analytics: Diagnostic analytics aims to identify the reasons behind past events or performances. It helps understand what caused a particular outcome and is often used to answer the question, “Why did it happen?”This type of analytics involves:

• Drilling Down: Going deeper into the data to find specific factors that contributed to an outcome.


• Root Cause Analysis: Identifying the fundamental reasons behind a particular issue or success.

Correlation Analysis: Determining if there are relationships between different variables in the data.

3. Predictive Analytics: Predictive analytics is a type of advanced analytics that uses historical data, statistical algorithms, and machine learning techniques to make predictions about the future. It aims to answer the question “What is likely to happen?”This type of analytics involves:

• Regression Analysis: Identifying and measuring the relationships between variables.


• Time Series Analysis: Examining data points collected at regular intervals to forecast future trends.


• Machine Learning Models: Using algorithms to learn from data and make predictions without being explicitly programmed.


• Risk Assessment: Evaluating potential future outcomes and their associated probabilities.

4. Prescriptive Analytics:Prescriptive analytics is the most advanced form of data analysis, and it focuses on providing specific recommendations for action. It goes beyond predicting what will happen and suggests the best course of action to achieve a particular goal. This type of analytics aims to answer the question “What should we do?”Prescriptive analytics involves:

• Optimization Techniques: Finding the best solution among a set of alternatives.


• Simulation Modeling: Creating models to simulate different scenarios and their potential outcomes.


• Decision Support Systems: Tools using data and algorithms to assistdecision-making processes.

Conclusion: The power of data analytics lies in its ability to transform raw data into valuable insights that drive better decision-making. By understanding the different types of data analytics and their applications, businesses can unlock the potential of their data and gain a competitive edge in today’s data-driven world.Whether it’s using descriptive analytics to understand the past, diagnostic analytics to uncover the reasons behind events, predictive analytics to make informed forecasts, or lastly, prescriptive analytics to take proactive action, each type plays a critical role in the data analysis process.

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