Why Is Data Driven Decision Making So Important?

Data driven decision making
Data driven business decisions make or break companies.
We read about it everywhere. Once we implement the right tools, so they say, and figure out how to analyse the data correctly, that knowledge will translate to better business decisions. It sounds great in theory. But in practice, that’s not the case in this part of the world. The data we need are not readily available and we are not helping either. Data collection is not given the importance it deserves in this part of the world. Even if you have the best data in the world, decisions are sometimes made in spite of that data, or with what is often described as going with a gut feeling.
For a lot of entrepreneurs, their decisions are driven by their gut or instincts, not by data. It’s to some extent fine to rely on instinct when your company’s just getting off the ground, and its core agenda is being conceptualised. In the long run, however, such an approach narrows your horizons and compromises your ability to spot potential market opportunities.
Before we go over the whys, let’s clarify what we are talking about.
Data driven decision making is a way of working that values the business decisions backed up by verified and analysed data. Such governance is possible the quality of data gathered is ensured. It used to be a long and difficult process to collect, extract, format and analyse the data requiring full-time experts, which of course impacted the time required to take action and the quality of these decisions. But today, the development and democratization of business intelligence software allows anyone without a heavy technical background to analyze and gain insights from their data, requiring much lower support from the IT department to produce the reports that will later need to be analyzed, accelerating then the decision process (although we are lagging behind in this aspect). From there, was born data science: when hacking skills, statistics and expertise meet. This fairly new profession involves sifting large amounts of raw data to make intelligent decisions.
MIT Sloan School of Management professors Andrew McAfee and Erik Brynjolfsson explain in a Wall Street Journal article that they performed a study with the MIT Center for Digital Business. They found that among the companies surveyed, the ones that were mostly data-driven had 4% higher productivity and 6% higher profits than the average.
Companies that approach decision making collaboratively tend to treat information as a real asset more than in companies with other approaches. That way, they tend to identify business opportunities and predict future trends more easily and generate more revenue with data.
There are numerous reasons that organizations should have a data-driven culture:
  • Data allows you to challenge all assumptions.
  • Identify business opportunities and predict future trends.
  • Continuous organizational improvement by making smarter decisions.
  • Increase efficiency of resource allocation by narrowing focus.
  • Generate more revenue
  • Empower individuals to confidently manage themselves.

Data vs. Opinions

When it comes to making decisions, people have their own distinct suggestions. The more time they spend at a job, the more opinionated they become. In such situations, I take my cue from James Barksdale, former CEO of Netscape, who famously said “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” To put it succinctly, I’m the CEO. Opinions are essential, and as your employees become more mature at their jobs, they will definitely form them. Without the numbers to back them up, however, those opinions are suppositions, not actionable data-based insights.

Data Curation Should Become a Habit

Entrepreneurs, Government and institutions should focus on building robust data-collection processes within their organisations from the get-go. If they don’t do this from the start, they won’t amass enough data, and if they don’t have sufficient data to analyse they won’t be able to extract useful insights; they will be left feeling like the organisation has no use for data.
However, if they methodically collected data from the start, they’d soon have enough to work with. They’d start analysing it to gain insights; the more useful those insights proved, the more they would start believing in the power of analytics, and the more likely they would be to use it.
It’s a cycle; if you don’t invest (in data), you won’t be able to enjoy the returns (insights), and you will become even less likely to consider it in the future. If you DO invest, you will see its value, and you will focus more on it.

In conclusion

A lot of the time, young organisations spend a lot of time mining data but end up with no useful insights. That’s because they don’t have a fixed end goal in mind prior to starting data collection and analysis.

One good rule of thumb is to always have a clear analytical objective. What is it you want to achieve?

Do you a) want to assess an opportunity or b) diagnose a particular business problem?

The more clarity you have with respect to your end objective, the more focused and rewarding your analysis will be. On the other hand, the more ambiguous your objective, the less focused your analysis and the less likely it is to yield good insights.

References: DatapineEntrepreneurEYStudios

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