The Types of Market Data Analytics and Their Uses?

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Pillars of the contemporary market data analytics paradigm are covered by the word “analytics,” To understand better your data and how you may utilize it to achieve your company goals, each plays a role, Using and interpreting data gets increasingly complex as businesses amass a greater volume of information, Market Data Analytics is a wide phrase that may indicate various things based on where you are on the data analytics maturity curve, Data without analytics is nothing.

The Types of Market Data Analytics and Their Uses


Modern Market Data Analytics

Descriptive, diagnostic, predictive, and prescriptive analytics are the four main types of modern market data analytics. 

Analytical tools are available, but how can you determine what to use them for, when, and why?

Data analytics may help your company make better decisions and achieve its business goals.

Suppose you understand what, why, when, where, and how. There are many different types of analytics. 

And this blog will explain how they all play a part in your organization’s ability to analyze data.

Descriptive Analytics

The “What happened?” inquiry is answered by descriptive analytics of modern market data analytics. 

Customer reporting and analysis are concentrated on historical occurrences using this form of analytics, making it the most popular. 

It aids businesses in gaining an understanding of items like:

● Please tell me about our company’s sales volume.

● What was the total output of our team?

● This quarter, how many customers did you lose?

Descriptive Analytics Core Competencies

Developing foundational skills in descriptive market data analytics is critical before moving up the maturity curve for data analytics. 

The following are examples of core competencies:

● The principles of data modeling and the use of basic star schema practices.

● The ability to effectively communicate data via appropriate visualizations is all covered in this course.

A corporation may easily start using descriptive analytics to assess overall performance since Data is readily accessible to construct reports and apps.

Diagnostic Analytics

Diagnostic Analytics


Descriptive analytics of market data analytics employs past data to answer questions. Instead of asking “what,” diagnostic analytics asks “why.”

Diagnostic analytics is perhaps the most undervalued phase in the analytics maturity paradigm. Anecdotally.

I observe most clients skip over the “why did it happen” part. It helps firms address issues like:

● Why did our company’s revenues fall last quarter?

● Why is customer turnover rising?

● Why is a certain product basket surpassing the previous year’s sales?

Predictive Analytics

Advanced analytics, known as predictive analytics, uses machine learning to predict what will happen based on past data. 

To develop prediction models, descriptive and diagnostic analytics rely heavily on historical data. 

Use cases that may benefit from predictive analytics include:

● Preventing breakdowns and malfunctions in machinery.

● Assessing the creditworthiness of a customer and spot signs of fraud.

● Signs of customer unhappiness may be used to predict and prevent client turnover.

Prescriptive Analytics

Prescriptive Analytics


Predictive analytics is the fourth pillar. It is a kind of guided analytics that prescribes or directs you towards a certain action. 

It is a mix of descriptive and predictive analytics. Existing scenarios are utilized to direct the user’s decision or action.

Oil and gas, clinical healthcare, banking, and insurance are just a few industries that apply prescriptive analytics. Prescriptive analytics may help:

Price changes are based on customer demand and external circumstances.

Identifying people in need of more training.

Conclusion

With market data analytics, you can move quickly and make better choices. Ultimately, you can enhance your performance and increase your company’s worth. Moreover, you will no longer be working in the dark or wasting time manually calculating data on spreadsheets.

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References:

sigmacomputing

analytics8

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