Data-Driven Decision Making

The age of data is right now, and the opportunities stretch across nearly all industries.

Some businesses get lucky for a while, but inevitably the organization grows to the point where a data-driven strategy becomes necessary to remain competitive, and often to even function and stay profitable.

Benefits of Data-Driven Decisions

Data analytics has proven itself to be an extremely effective strategy.

Greater Objectivity

More Reliable

Higher Scalability

Less Bias

Why Data-Driven Decision Making?

Data-driven decision making is all about the minimization of bias. Every gut-feeling decision maker bases their decision solely off of their own personal set of experiences.

The issue with this is that this set of experiences may not be reflective of everyone’s experience; i.e., that individual’s decision-making processes are heavily biased towards their own experiences.

So what if there were a way to consider everyone’s experiences at one time when making decisions, not just the experiences of the decider?

This is the essence of Big Data-driven analytics.

In this field, we leverage massive datasets ―even large enough, ideally, to include everyone’s experiences― to arrive at optimally accurate, bias-free decisions.

76%

of executives at top-performing companies believe data strategy to be essential

42%

of executives at industry laggards believe the same

0%

of industry laggards believed they had an advantage over their peers in terms of data.

Different Forms Of Data-Driven Decision Making

There are two main forms of data analytics-driven, actionable-insight generation: predictive and diagnostic.

Predictive analytics, as the name would suggest, deals with using known information to predict unknown information.

To accomplish this feat, data scientists employ a special set of algorithmic techniques called machine learning; machine learning involves teaching a computer how to extrapolate upon incomplete data, so that the prediction-making process can be automated.

Diagnostic analytics involves determining the cause of an observed pattern in the data. Using historic or current data to create dashboards and find trends is an example of this.