The Importance Of Data Analysis

“Data is what you need to do analytics. Information is what you need to do business,” said renowned theologian John Owen. Analysis of data from business and market is key to staying in business and ahead of the curve.

Inferring from experience of working and communicating with various organisations over the last two decades, one thing which stands out is the fact that I have not come across a company which undermines the importance of data analytics.

The common phrases are: ‘We have some extensive data analytics performed monthly’, ‘our top management looks at the numbers’, ‘we are on top of our analysis’, ‘we have dedicated resources to collect information’, ‘it’s our CFO’s prime responsibility’.

But the big questions here are: Why do we analyse the data? What is it that we are trying to achieve? Is it because someone told us MIS will help give a clear picture of our business or just because everyone says its mandatory for any business to do so?

In an upside-down business world, no problem can be solved by looking at it in the same way as we started. To think differently, we need our fuel, which is data, but the correct one to read it like information.

Which is the correct data for my business which will help me make the right business decision? This is the big question which needs to be addressed.

Be it Nepal or India, generally MIS or data analysis, revolves around analysing the conventional yardstick of financial numbers, ie. profit and loss, cash flow, debt ratios and a few more financial ratios.

These numbers are important, they demonstrate what we have achieved but not what we can achieve or should have achieved. Recent study by MicroStrategy, reflected how companies worldwide are using data to:

  • Boost process and cost efficiency – 60%
  • Drive strategy and change – 57%
  • Monitor and improve financial performance – 52%

To achieve the cost efficiencies like these global companies, we can use multiple methods for data analysis, the four primary ones are:

Descriptive – It aims to answer the question of what happened. Through interpretation of historical data, old patterns and trends can be seen.
Exploratory and Diagnostic – Aim is to explore and diagnose the historical data, any other input variable to pinpoint the exact issue or challenge faced.
Predictive – To plan a future course of action, we should be able to forecast the future outcomes. This is done through advanced statistical tools. Helps us stay ahead of the competition.
Prescriptive – Most effective ways in research, it’s a combination of predictive and existing data using visual aids like graphs, etc in the key business areas like marketing, sales, logistics, etc.

As simple as it sounds, when we are analysing varied data points from different sources in different formats, the tools to do so becomes a focal point. BI tools can be stacked onto existing financial and operational software’s using SQL queries, Rstudio, python, etc. Making it easy to analyse, monitor and report the findings within competitive timelines. These tools use over hundreds of statistical tools like pareto, 80-20, cohort analysis, regression, text analysis, porter analysis, fraud detection methods, etc to present data in the format we need depending on the business decision we wish to analyse.

Over the years, we as a business, have concentrated only on financial numbers. Here is an indicative list of performance indicators for a few segments that should be explored, which will have a direct impact on cost and process optimisation:

  • Production related
  • Planned production vs actual production
  • Plant capacity vs achieved capacity
  • Delay in scheduled production and reason
  • Production rejections vs budgeted vs industry standard
  • Machine break down – planned vs unplanned vs repeated
  • Break down reason analysis
  • Manpower time estimation for actual production vs actual manpower hours available
  • Order execution time cycle
  • Inventory ordering to receipting time cycle
  • Shop floor damages
  • Human Resources
  • Employee hiring TAT
  • Attrition rate and cost impact
  • Learning curve cycle
  • Employee productivity index vs global index
  • Employee morale and happiness
  • Overtime cost vs benefit analysis
  • Training vs efficiency mapping
  • Manager wise attrition rate
  • Supply chain
  • Perfect order index for error free deliveries and damages
  • Customer delivery commitment vs actual delivery cycle
  • Cash to collection cycle
  • Customer order to cycle
  • Warranty claim estimated vs actual
  • Route optimisation savings
  • Freight reduction saving
  • Customer grievance escalations
  • Brand value engagement
  • Innovation scale

Analytics has seen a long journey in a relatively short span of time. Data analytics can assist organisations in multiple facets of business and decision making and be the game changer for the future and scalability. But to maximise the benefits we need to implement the right tools, metrics and technology. The future years will see companies who have used the most relevant tools and at the right time and efficiency succeed and go beyond.

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Tulsi Khemka

Tulsi Khemka is a CA with 18+ years of experience in the space of risk, systems and security having worked with corporates in India, Nepal, US and UK.

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