Imagine closing in on the highest peak on the planet, you are about to summit Mount Everest for the unmatched feeling of being on the top of the world. All the time, training, resolute discipline, mental strength & resources amongst other things you have invested to achieve this herculean feat are about to pay off. Suddenly the weather has changed for the worse or the peak is overcrowded and now you must make a critical decision, to still try and summit the peak or head back down to attempt another time. 

Be it a seasoned mountaineer or a rookie, every person decides differently and some of those decisions can be unforgiving. What is it that makes people take different actions even in these precarious situations? One can never fully understand the human mind but “Human Biases” can be assumed as one of the reasons for the deaths of some of the 300-plus climbers who lost their lives trying to summit Mt. Everest. So what drives us to move ahead when all the information at hand points to the obvious perils? Why can’t we sell a stock after holding it for years and still in losses? Why do we always favour a select few brands while shopping? This points us to the rationality of human decision-making.

Decision Making is imbibed in our everyday life; it is what helps us move ahead every day, every hour, and every minute from what we choose to wear to what we eat. In our busy modern life we have a shortage of time and an overload of information. Given that the human brain makes up 2% of our body weight and uses 20% of our energy resources our instincts have us avoid spending more time on complex decisions as it consumes a lot of energy. In the face of decision complexity our brain, therefore, favours Heuristics. 

Human beings and animals use “Heuristics - unconscious routines to cope with complexities in decisions'' to make decisions quickly and effectively. 

These heuristics are based on our cognitive abilities (brain-based skills we need to carry out any task from the simplest to the most complex, the mechanisms of how we learn, remember, problem-solve, and pay attention). This has an accuracy trade-off, these learning and adaptation in our mind lead to incidental stimuli drifting in the cognitive stream. Known as cognitive biases, these can lead to less optimal outcomes for the decisions we make. These biases also impact the everyday business decisions we make.


Relating to my personal experience, I can say that decision-makers across industries show some of these biases. There is the implied status quo where no one wants to make an uncomfortable change unless forced to do so. The bearer of bad news is always expected to be beheaded, setting a culture of fear. The annual budget set by businesses at the start of the year did not include a single input from the people who are expected to achieve this budget. There are a few other biases linked to this, like overconfidence/prudence biases. 

Data for the revenue projections are reported by individuals leading SBUs. When presenting expected numbers VS achievement it is clearly evident that managers are prudent while reporting their numbers, but the feet on the street are expected to be overconfident in their estimate, which defeats the entire purpose of the reporting exercise. Resulting in distorted information in the management dashboard, which is not a reflection of the real market situation. This brings me to the understanding that organisational culture also plays a major role in habiting biases. Organisations with an inclusive culture and emphasis on evidence-based decision-making can realize the true potential of data analytics.


Once we are aware of some of these common “cognitive biases” we can use the following tools & recommendations in our everyday functioning to mitigate some of them. 

To present an analysis to evoke effective real-time decisions, analysts need a structured approach.

While creating the report:

  • Clearly define the question to be answered
  • Identify the required data – Quantitative / Qualitative
  • Ensure that data quality - Sources are diverse and not based on easy availability, collection, sampling etc
  • Define the parameters – Why analyse in a certain way and not another
  • Statistical tools - Manipulate the data in different ways for different outcomes in different environments
  • Interpretation stage - Look if the question has been objectively answered, what are the limitations of this interpretation?


While presenting the report:

  • Clear presentation with graphics or visualization
  • Have relevant comparables to give a sense of the data
  • Use simulations to predict outcomes in wide-ranging scenarios
  • Include recommendations for actions and implementation where possible

Analysts must work closely with their business counterparts to drive meaningful change. Training is necessary for everyone involved in decision-making to be able to use and interpret data accurately. Some of the mundane decision-making tasks should be automated with adequate monitoring and controls in place. A data-driven culture encouraging curiosity & enablement with access to data across verticals is necessary.

To sum it up, we can mitigate the effects of cognitive biases on decision-making with education, training and practice developing a future framework along the way.