Data-Driven Decision Making


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


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When it comes to organizational decision making, data analytics has proven itself to be an extremely effective strategy; its benefits include greater objectivity, reliability, and scalability, as well as a dramatic decrease in bias, when compared with conventional “gut-feeling” processes.

Humans are not as good as they think they are at guessing or “instinct” when it comes to business.

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.


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.

And the benefits of data-driven decision making aren’t just purely theoretical, either.

Empirical studies such as this one find that 76% of executives at top-performing companies believe data strategy to be essential, compared with only 42% of executives at industry laggards; additionally, out of all executives in the financial laggard group ―17% of all executives in the study― none 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.

For reasons aforementioned, a machine learning-based approach is greatly advantageous compared to gut-feeling analytics ―in terms of both accuracy and automation― when it comes to predictive decision making.

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.

Data-Driven Decision Making For Your Organization

A prominent principal at Deloitte Consulting is quoted as stating,

“Many of my clients are clearly aware of the importance of data, but they don’t know where to start in terms of where they should focus to get the most value, as well as how to translate the data into actionable insight.”

If your organization relates to such clients, reach out to us at Boxplot.

We’ve gained a track record for enabling clients to pursue data-driven decision making, regardless of their industry and previous level of data experience. Whether you already know exactly the insights you want, or if you just want an open consultation with a data expert, contact us today by visiting this page.

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