Accidental Entrepreneur: Data Driven Decision Making Process for Professional Success

  • Data Driven
  • Knowledge Drives
  • Goal Driven
  • Results Drives
  • Process: First step in gathering and executing a successful data driven project is to have a clear end to end process established from the beginning. It provides clear direction and purpose for collecting/analyzing the data. This doesn’t mean that the process doesn’t change as time goes by and new information is gathered. I have made many adjustments/modifications, but starting without a plan can be very disastrous. Therefore have a clear process and discuss with stakeholders to align on the next steps in the process.
  • Tools: Dataset sizes have significantly changed over time. As I mentioned in my early career I had to collect and analyze the dataset from billions of transistor designs. This is where the tools and automation come into play. When I joined my first job at Rockwell, they had the data collected in the computers but the analytical tool was not there. I had to go through thousands of pages of printed pages to identify issues/anomalies and report to my senior manager. Rather than just following the orders from my superiors, I asked them for the digital data and used scripting to analyze them and report them in simple Excel spreadsheet format. These were simple scripting tools (In 1990’s it was Csh and Perl and now Java and Python) and visualization using spreadsheets such as Excel.
  • Analysis: Once the process and tools are in place, the next step is to understand the purpose of the data collection and what kind of results we are looking for. During this analysis process, the first step is to take a small sample of data to validate the tools and scripts. Lately AI/ML people define this as a training sample set. Once the basic algorithms and analysis are validated, the automated batch scripts with minimal human intervention can process billions of datasets in days rather than in months by people.
  • Visualization/Presentation: Most important part of any data analysis and decision making is to present the results in a consumable manner. This is key for communicating the results of all your hard work to others and convincing/educating others to make useful decisions. There are many ways to visualize this data and present it to others. I use a simple scheme of stating the facts, providing some parallels from prior experiences or similar common life situations. Basic idea here is for others to understand the points you are making and be able to follow your conclusions.
  • Refinement: Finally, no situation is static as well as there are experiences from others that can provide different perspectives. I use the presentation process and discussions with experts to continually refine the analysis process in addition to re-validating the conclusions.



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Ruban Kanapathippillai

Ruban Kanapathippillai

Entrepreneur, Founder of multiple successful startups, Mentor/coach, Angel investor (Sandhill Angels) and Positive thinker