Basic Overview of Data Analytics

In this blog, I will give you a basic overview of Data Analytics and try to answer some of the questions like – What is data analytics, How Data analytics is important and helping many sectors to grow ?

Lets go ahead.

What is Data Analytics?

You can look at a more formal definition here. But in simpler words it can be defined as –

Data Analytics is the process/science to pull out important insights from raw data by molding in such a way which gives the best insight which helps to take business decision.It helps to optimize the business performance.

Why is it important?

Data Analytics is important because it helps –

  • In optimizing the performance of the business.
  • In reducing unnecessary cost in business.
  • To forecast the business challenge in future by predicting revenue through historic data available.
  • To read the trend of business and can help to change the strategies in business required time to time.

A company can use data analytics to understand customer trends as well as their satisfaction level regarding their product which eventually leads to better product and service.

Types of Data Analytics

Data Analytics is broken down into 4 types ;

  • Descriptive Analytics:
    This is the most common and basic type of data analysis. It usually answers what has happened by summarizing the past data.
  • Diagnostic Analytics:
    After knowing what has happened, now its time to know know why it happened and that’s what we do in diagnostic analytics. This analysis takes insights from Descriptive analysis and drill to further deep into the data to find pattern or behavior in the data. Did the summer weather affect sales of ice-creams or beverages?
  • Predictive Analytics:
    As its name suggest, it predicts what is likely to happen to near future by using the data summarize in above 2 analytics.
    It can be done by creating some statistical models as per business requirement.How much sales/revenue will generate of ice-cream/beverage in near future? It is also important to understand forecasting of something is only an estimate, it depends on the quality of data and model building techniques.
  • Prescriptive Analytics:
    After knowing ‘what happened’, ‘why happened’ and ‘what is likely to happen in future’, now its time to suggest a course of action and this is what has been done in prescriptive analytics.
    If a sales of ice-cream/beverage is going to increase then company should increase the workforce for that quarter and rent an additional machinery to meet the demand of product.

Uses of Data Analytics?

Many companies or group of companies offering similar service or product (which we call sectors) are using extensively data analytics. Travel and tourism industry using data analytics to meet the demand of customers as it has quick turnaround time. Healthcare sector process structured and unstructured data to understand the patter of customer to make quick decision. In Retail sector, data analytics is used extensively to understand the trends, identify customer needs and increase profits.

Conclusion

As we have shown, 4 different types of data analytics, these are all interconnected and rely on each other. As we move from Descriptive to Predictive and prescriptive, someone requires much more technical skills (how to build models) to have those answers ready which need to be answer in the business.

I hope you enjoy reading this blog. I am waiting for your valuable feedback and share the topics on which you want me to post a blog.


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1 Response

  1. July 1, 2020

    […] starting any data analysis, the most crucial step is data preparation. Many companies spend millions and billions of dollars […]

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