DATA Analytics

What is DATA?

Businesses generate Data! Huge amounts of Data! Data in itself is both a source as well as a product for the business.

The phrase “Data is the new oil” is so apt in the current scenario with how the business are scaling to greater heights through putting their data to work.  The Internet has made inroads into our lives in an unprecedented way, along with the affordability of mobile devices, digital tools, mankind is witnessing never before growth in the generation of data over the years.


As we all know, data is raw, and every piece is not used which means it has to be refined to extract meaningful, usable, and employable information. The question is how do we do this? Yes, this can be made possible with the use of supporting analytical tools and the application of a rational mind.

Analytics simply means a systematic approach to handling data with the application of statistics and computational methods to discover or interpret meaningful information.

For example, Sales made by a medical equipment wholesaler over the last six months is data, which in its raw form is only a number but slicing and dicing this data into separate dimensions such as product, customer, geography, time, period, quantity, selling price – help us in observing interesting facts. These can become the basis for rational decisions for inventory management, price variation, etc.

Remember, Data talks!

The finance domain, especially accounting and auditing domains has witnessed tremendous use in order to make sense of data. The professionals are putting data to work and helping businessmen to make informed decisions rather than following instincts and luck factors.

In the last few years, businesses invested in right tools to learn from their data and monetising the investments already.

Finance professionals should adopt such practices in order to stay in the race, upgrade right skillset and emerge with the right mindset to look at data differently.

Why Finance professionals are the right choice?

Why not! Who else understands data better? We play with numbers all day, help the business grow, manage the money flowing, direct businessmen on the right path on various occasions.

The Big 4s seem to have seized the moment by building in-house analytical tools and are providing SaaS (Software as a Service) platform to clients already. Such as Deloitte’s AI tools such as LeasePoint, Visual Inspection of Assets, An AI initiative Catalyst, and PWC’s AI technology GL.ai and KPMG Ignite offering is designed to enhance business decisions and process on a digital platform.

Analytics is evolving and has branched out to below 4 types majorly,


Descriptive Analytics – This is to understand what is happening in the business.

  • Foundation for all data
  • Summarises past data, like dashboards.
  • Used to track KPIs, monthly reports.

Diagnostic Analytics – Asking what happened and why to dive deeper into the issue.

  • Uses results from descriptive analytics and finds root cause.
  • Involves creating detailed investigations and reports.
  • Creates connections between all data to identify behavior and pattern.


Predictive Analytics – answers the question of what is likely to happen.

  • Uses previous data to make predictions, forecasts the outcomes.
  • Involves the use of statistical tools and skilled manpower.
  • Helps businesses in risk assessment, expansion opportunities, and make logical predictions.

Prescriptive Analytics – Suggests the course of actions to be taken based on the forecast.

  • Combines data from previous analytics and determines the course of action.
  • Consumes a large amount of data for the machines to learn and produce outcomes.
  • Involves the use of concepts such as Machine Learning (ML), Big Data, and Artificial Intelligence (AI)

While descriptive and diagnostic analysis is being used extensively and is a common practice in business, predictive analysis, and prescriptive analytics is where many organizations begin to show signs of difficulty due to the requirement of quality resources and investment. It involves the use of machine learning concepts, statistical models, computer languages such as Python, R, and other tools.

Finance professionals can venture out to this area owing to rich domain understanding and problem-solving skills.

Few tools such as KNIME, Alteryx, POWER BI, are low or no-code tools available in the market to learn and apply analytical models to extract rich data treasure.

In the next articles, I shall cover some of the cost-effective tools in Analytics specifically for Micro and Small Enterprises. Keep checking!

Few terms to read on for the week – Benford Analysis, Linear Regression, Decision Tree.

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