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Mistakes To Avoid When Using Data-Driven Decision Making

Even though your organisation can grasp the understanding of how data-driven decision making works by also collecting real-world data as examples, it would help to have the don’ts in this learning curve to prevent any unnecessary damage or losses.

An organisation once produced an internal report indicating that selling one of its divisions to another company was not going to be a smart business decision. Instead of waiting for proper research, they proceeded with their intuitions and bailed on the idea. As it turned out, they forgot that they had commissioned research to support this decision-making process and the research came back proving that the report was wrong. This mistake cost the company over one billion dollars.

On that note, a study found that 58 per cent of the companies they surveyed did not use data and information, but instead based half of their regular business decisions on intuition or experience. The study also found that companies often only use 50 per cent of available information when it came to decision making.

The data analysis tool empowers organisations by helping them gain optimum usage out of their data through visualisation and easy shareability of generated insights using real-time dashboards that will make better business decisions faster. None of these initiatives matter if decision-makers choose to rely on their intuitions instead of research.

The bigger question is: how do you make sure that your decisions are based on facts instead of feelings? What can you take away from business leaders who choose to ignore data-driven decision making? Here are some key points that will ensure consistency in data-driven decision making within your organisation which are focused on bringing excellent results and on your goals.

Quality – It is important that you leverage on is data quality, a set qualitative or quantitative variables that should be perfectly suited to address its use in operations, decision making, and planning. Good data quality management — from acquiring and dispositioning to maintenance and distribution throughout your organisation — is essential, as well. There’s no point in collecting good data if it isn’t well managed and exploited for optimal use.
Relying too much on past experiences – Simply said, you cannot move forward if you are looking backward. In other words, experiences could prove to be detrimental to businesses due to missed opportunities. Companies hire business leaders with X amount of experience, even though the landscape around them is constantly changing: rendering their business tricks useless over time. Just like technology, decision making must not completely rely on the past – it should also adapt to newer insights.
Data over intuition ­– If you have a gut feeling about a business decision, get proper research done to verify this decision. If research proves it wrong, you should go with what the numbers depict – even if your boss says otherwise. Because “that’s a fact”.
Too quick to judge – Cognitive biases are decisions made based on the little information you have, or based on your experiences that may not necessarily be relevant to the situation you are in. Companies make decisions based on cognitive biases every day, so much so that it’s an unsaid norm to ignore solid data and proceed with business decisions based on assumptions. For example:
a) Confirmation bias
The tendency of favouring information that confirms existing beliefs.

b) Cognitive inertia
Difficulty adapting to new surroundings, sticking to old beliefs despite being proven otherwise by data.

c) Group Think
Conforming with the majority, regardless of evidence or motives to support.

d) Optimism Bias
Decision-making based on the assumption that the future will be better than the past.


In other words, project leaders must understand the different types of biased behaviours so that they can be managed.

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