Sunday, February 7, 2021

Use of data & machine learning techniques in every day business decisions


    In this high-tech era, we are increasing our data generation by leaps and bounds. Even a small business generates enough data, to analyze and use findings in important business decisions. 

    Be it a small shop or large manufacturing unit, a home based start-up or well established corporate; every business has trends. Every business has some ups and downs based on some factors. 

    There are lot of factors affecting business based on type of business, some of  the most common are:

  • Time of year/month
  • Season
  • Inclement weather
  • Pandemics
  • Political events
  • Social events
  • Innovations
  • Changes & updates in competitors

    Every business has data, however question is how to use it ? Even though it seems very straight forward, it is not. Without proper planning, domain knowledge & data science skills;  data will not help much.

    We can use data to answer range of questions, varies per type of business and status of the business. 

  • Predict sales for next quarter or month (Time series with ARIMA)
  • Predict change in sales due to a current event
  • Find replacement options to balance partial business lost
  • Quality control analysis
  • Plan workforce 
  • Evaluate operation's bottlenecks
  • A/B testing
  • Pre-product launch analysis
  • Market status - stock predictions
  • Survey or feedback analysis
  • Product & program performance analysis
  • Cost-benefit analysis
  • Supply chain analysis or inventory control
  • Logistic analysis


Success of any analytic project depends on:

    👉 Analytic view
    Just like we need to do cost-benefit analysis before we start any project, we should have clear picture of how and what we want from an analytic project. Analytical vision is the foundation of success as it helps us to see KPIs and its usability. 

👉 Domain knowledge 
    We should have basic understanding of the business we are planning project for. It helps us to consider available resources and possible data sources.

👉 Usable Data
    Raw data is not ready for analysis. We should have some data engineering skills to scrap data, retrieve data from data lakes & data warehouses or even to create one. 
    Data cleaning and feature engineering takes almost 60-80% of the time in any data science project. So, if we have dataset in the format we want to use, half of the job is done!

👉 Data science tools & skills 
    This is equally important, still I kept it last. We do not need them until we have analytic vision, domain knowledge and usable data. 

    If we don't have first three, there might be no data science project at all! In other words, we use data science skills and tools once we have meaningful project and usable data.

    Or we can pick up a tool or two while working on project. Once we know what we want, there are lots of resources available online.  

    Just like once we have an address - the destination, there are lots of ways to reach there. We can use GPS, use maps & ask people, public transportation or simply take a cab.

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