4 types of data analytics for improved business decisions

Have you hired any one of the various professional data management consulting companies to create a strategy for your organization? Then you must know about the different types of data analytics as well. Businesses worldwide are recognizing data as immensely important for securing their development. However, the information in its raw form is not going to provide much value to an enterprise. It is through data analytics that raw data is cleansed, transformed, and analyzed to draw valuable insights. In fact, it is nearly impossible to make informed decisions without an analytics tool providing you with the necessary input. In this article, we are discussing the 4 different kinds of analytics that can drive better business decisions.

1. Descriptive Analytics: Understanding The Exact State

Descriptive analytics is the most basic type of analytics which will help you understand the exact state of your business. Let’s say you are a garment retailer and want to know the sales volume for the past week. Descriptive analytics will give you exact sales figures across all categories of products that you sell. This can help you in identifying the clothing items that are in demand. A quick look at the figures tells you that kids’ clothing was the top-performing category last week. This will help you know that the demand may continue in the current week also and you can make plans accordingly. This type of analytics will evaluate historical information to let you know what is happening. However, it is unable to identify the reasons why something happened. You will know whether something right or wrong took place but will not know why it happened.

2. Diagnostic Analytics: Finding The Reason For An Occurrence

When you want to know the reason for an occurrence, you need to use diagnostic analytics. These tools measure historical data with other relevant information to understand why something happened. Diagnostic analytics can help in isolating dependencies and identifying patterns in the available information. It can help in acquiring valuable insight into specific problems faced by your organization. For instance, a garment retailer can find out why it fell short of its monthly sales target by viewing the sales and gross profit figures for all categories. Enterprises which are interested in making data-driven decisions like to combine the different types of data analytics. However, in order to successfully use diverse analytics solutions, organizations must possess detailed information.

3. Predictive Analytics: Forecasting The Trends

Charting the path for the future can be a complex task for businesses. They need access to accurate input in order to create a future plan. Predictive analytics provides them with an effective solution to this issue. These tools can help identify tendencies, clusters, and exceptions by using the feedback provided by descriptive and diagnostic analytics. This can help in forecasting business trends and planning for the road ahead. Predictive analytics enables corporations to take a proactive approach and set new standards rather than playing catch up. For instance, a business can employ the tool to identify clients who are most likely to terminate their subscriptions. They can run marketing campaigns aimed at such clients to discourage them from canceling their membership. This is a far better option than trying to get people to sign up again after they have dropped their subscriptions.

4. Prescriptive Analytics: Identifying Remedial Actions

The power of analytics can be leveraged to identify the corrective measures required to be taken to resolve a future issue. Prescriptive analytics involves the application of state of the art tools and technologies like machine learning to recognize potential problems and the ways to fix them. It requires historical data as well as information from external sources to conduct the evaluations. Prescriptive analytics can also help enterprises spot a promising trend and exploit its full potential. However, the sophisticated nature of the practice requires significant investment and careful implementation. Business owners must assess the exercise’s full value for their enterprises before deciding to use the tools.

Conclusion

Businesses will need access to different types of data analytics to fulfill their diverse requirements. These tools play a vital role in complementing the data management strategy by drawing in-depth insights from the information generated by an organization.

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