How data analysis supercharges your decision making

The power of data is undisputed. The Economist calls it the new oil. But when we think Data-driven or Data-Informed Decision-Making, we usually think of big data and analytics as a tool only big corporations use to make key decisions.

But it’s not about the size of the data. Even a small amount of info could be considered “big data” if a large amount of information has been extracted from it.  The underlying principle is to make decisions for the future, based on what has worked in the past.

What is your decision-making process?

We might have different processes, but, they are mostly similar and follow this pattern

  1. Identify the decision.
  2. Gather relevant information.
  3. Identify the alternatives.
  4. Weigh the evidence.
  5. Choose among the alternatives.
  6. Take action.
  7. Review your decision.

Data-driven decision-making just tweaks step 2. It uses data to inform your decisions. You gather historical information to analyze trends and make decisions for the future based on what has worked in the past  

Just step 2?

Yes! If done right it makes the other steps a walk in the park. Data-driven decisions leverage insights drawn from the data gathered.

This approach is called by many names in different spheres including big data, data analytics, business intelligence, diagnostic analytics, data analysis, data modelling, online analytical processing and more recently personal analytics. 

Stephen Wolfram, founder and CEO of the software company Wolfram Research, published a blog post back in 2012 about the insights he was able to draw from analysing  20+ years of his personal data. The accumulated data includes emails, keystrokes, phone calls, meetings, events, and walking habits. They all helped him draw meaningful insights.

For instance, his analysis of his email archive helped him understand that without any form of intervention on his part, most issues at work settled themselves before the day was over.


His intervention, a large percentage of the time, would only have brought about a waste of his time and effort. He was able to show that analysing simple data can yield meaningful and powerful insights.

But that’s 20+ years. 

It doesn’t have to be 20+ years, in fact, he attributes a lot of the delay to lack of motivation (he had bigger projects to handle during that period). His product Mathematica and the automated data analysis capabilities, then just released in Wolfram|Alpha Pro gave him the kick he needed.

Simple data like your daily wake-up time accumulated over a week could reveal what really happened that week, as well as a little about your habits. Then, collect that over a month or a year, you’ll begin to see you’re quite predictable. You begin to become less vulnerable to risky decisions going wrong.

In this post, I listed 3 decisions Upnepa helps students make easily. Real-time and historic power supply data sponsor the insights needed for these decisions.

If you are interested in getting a head start, you can begin by collecting data and using simple analytical tools to analyze and draw insights from the data. Checkout Upnepa for instance.

Cheers to a better you!