The power of Data Analytics

Demand for Data Analysts

Types of Data Analytics

Data analytics is categorized broadly into four types:

• Descriptive Analytics

  Using descriptive analytics, organisations may determine which product categories they should concentrate on by learning about events that occurred over a specified time period, such as the number of views on a website for a specific product. Descriptive analytics works with unprocessed data gathered from many data sources to extract and provide insightful information. These outcomes, however, typically indicate whether something is right or wrong without providing a clear explanation. Descriptive analytics are therefore combined with other forms of analytics in highly data-driven businesses.

• Diagnostic Analytics

  Diagnostic analytics explains the reasons behind the happenings or past performances. It gives in-depth insights into a specific problem, like exploring whether it was the cold weather or poor marketing strategies that affected the sales of cold drinks, or retailers trying to find the reasons behind why they missed their net profit target.

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  Predictive Analytics

  With the use of predictive analytics, you may find out what will happen in the near future, such as the weather or sales for a specific quarter. In order to predict future trends and identify clusters or exceptions using machine or deep learning, it makes use of the findings from descriptive and diagnostic analytics. Although a more sophisticated type of analytics, its accuracy is based on the calibre of the data gathered and the market's stability.

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• Prescriptive Analytics

  Prescriptive analytics is used to recommend what needs to be done or it reveals a course of action best suited. For example, a retailer is selling 200 units of a given product and using prescriptive analysis it is advised to increase production to 210 units keeping in view the rise in demand. The same can be leveraged by any group or industry and is considered the most valuable kind of analysis.

Roles of a Data Analyst

A data analyst's primary duty is to improve a process' performance and efficiency by finding patterns and insights in the data using a variety of approaches, including analysis, mining, management, and visual display.

Sorting data from unstructured data sources, such as text, sensor data, photos, large databases, etc., is what data mining entails. At this stage, the main task is to extract, process, and load the data into an appropriate format for analysis and storage.

In order to access the findings of data mining, databases must be designed, managed, and created using SQL.

In order to forecast or identify trends, statistical analysis entails extracting insights from data and building statistical models. Python, statistics, and machine learning are utilised.

As the final step in the process, data presentation involves sharing insights from the data with stakeholders through a visual presentation that tells the data's story. This step also aids management in taking the proper actions to enhance an organization's performance.