As the world becomes more data-driven, storytelling through data analysis becomes a vital component and aspect of both large and small businesses. It is the reason that organizations continue to hire data analysts. To analyze data, the core components of analytics are divided into the following categories:

• Descriptive
• Diagnostic
• Predictive
• Prescriptive
• Cognitive

Descriptive analytics:

Help answer questions about what has happened based on historical data. Descriptive analytics techniques summarize large semantic models to describe outcomes to stakeholders, An example of descriptive analytics is generating reports to provide a view of an organization’s sales and financial data.

Diagnostic analytics:

Help answer questions about why events happened. Diagnostic analytics techniques supplement basic descriptive analytics, and they use the findings from descriptive analytics to discover the cause of these events. Then, performance indicators are further investigated to discover why these events improved or worsened.

Predictive analytics:

Help answer questions about what will happen in the future. Predictive analytics techniques use historical data to identify trends and determine if they’re likely to recur.

Prescriptive analytics:

Help answer questions about which actions should be taken to achieve a goal or target. By using insights from prescriptive analytics, organizations can make data-driven decisions. This technique allows businesses to make informed decisions in the face of uncertainty.

Cognitive analytics:

Attempt to draw inferences from existing data and patterns, derive conclusions based on existing knowledge bases, and then add these findings to the knowledge base for future inferences, a self-learning feedback loop. Cognitive analytics help you learn what might happen if circumstances change and determine how you might handle these situations.

Role of Data analyst in Retail business:

A retail business uses descriptive analytics to look at patterns of purchases from previous years to determine which products might be popular next year. The company might also look at supporting data to understand why a particular product was popular and if that trend is continuing, which will help them determine whether to continue stocking that product.

A business might determine that a certain product was popular over a specific timeframe. Then, they can use this analysis to determine whether certain marketing efforts or online social activities contributed to the sales increase.

An underlying facet of data analysis is that a business needs to trust its data. As a practice, the data analysis process will capture data from trusted sources and shape it into something consumable, meaningful, and easily understood to help with the decision-making process. Data analysis enables businesses to fully understand their data through data-driven processes and decisions, allowing them to be confident in their decisions.

Summary:

Data analysts in retail businesses are instrumental in transforming raw data into meaningful insights, providing valuable support for strategic planning and operational decisions.