• Rizwan Khan

How to go from no data analytics to a data-driven organization?

Updated: Nov 24

A data-driven organization is an organization that can collect and refine data and finds ways to use the minded information to drive growth and profitability. Companies like Netflix, Google, Coca-Cola, and Uber are using business intelligence to assist in making logical decisions with a high probability of success. If you are not one of the CEOs of these giants with a stockpile of cash and resources, how do you build these capabilities? Good news, you can with the right approach. Following are the key steps to start a successful data analytics program in an organization irrelevant of the maturity level of the organization.  

  1. Scope

  2. Prioritize the critical business areas to analyze and improve

  3. Determine the initial business questions to investigate

  4. Educate stakeholders about the benefits of business analytics

  5. Plan the effort: define success and create timelines and outcomes.

  6. Build internal rapport and congruence

  7. Collect

  8. Inventory data sources and decide how much to include.

  9. Establish a Master Data Management policy

  10. Create a dictionary that defines your standard business terms

  11. Combine and integrate key data sources in the central data mart

  12. Clean

  13. Define acceptable standards for data cleanliness

  14. Correct duplicate, missing, and inconsistent data.

  15. Standardize procedures to reduce future data discrepancies

  16. Analyze

  17. Teach “The Analytical Mindset” to shift the culture

  18. Visualize your data with interactive dashboards

  19. Forecast outcomes with Predictive analytics

  20. Discover patterns and correlations through data mining

  21. Ask new questions with data discovery.

  22. Iterate through the process to refine

  23. Communicate

  24. Demonstrate results to communicate value

  25. Ensure the level of detail is appropriate for the audience

  26. Describe the visualizations with stories

  27. Encourage questions and new hypotheses.

  28. Tech “A Conversation with Data” to speed adoption

  29. Prescriptive Analytics

  30. Understand the difference between AI and ML

  31. Prioritize the main driver(s) of value

  32. Select the areas to build cognitive capabilities

  33. Evaluate your internal capabilities

  34. Consider consulting a domain specialist to build models for your business.

  35. Iterate to improve the models and ways to measure improvements

No matter where the organization is on its data analytics journey. The above approach will add value at any stage of this journey. We have seen improvements in all business areas by taking a systematic approach to decision-making using clean data with the right tools. Some of the most valued areas of improvement include employee productivity, marketing activities, product innovations, and personalized customer experiences.


The ideas mentioned above are meant as information to ease your organizational processes. However, if you would like a more detailed overview, do not hesitate to reach out to me at rizwan.khan@mac.com.


I have years of experience building Technology and providing Technology Due Diligence as a CTO, and I am available for fruitful discussions.


#CEO #technology #CIO #Interim #technologyleadership #educationtechnology #leadership

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