Is Your Decision-Making Process Holding You Back?

In today's business environment, the decision-making process is often a critical point of failure, holding back organizations from realizing their full potential. When the issue lies not in the lack of data but in how it's used – or not used – in decision-making, how can data leaders spotlight this gap and guide the organization toward more effective practices?

To get started, here are a two very real examples:?

A team composed of Product and Marketing was at a crossroads, trying to decide whether to invest in Campaign A, with a 46% conversion rate and 21,000 conversions, or Campaign B, with a 38% rate but 74,000 conversions.

A team composed of Product, Engineering, and Operations struggled to decide how to turn around a sudden 4% drop in sales that occurred at the same time new features were released and Operations reported a 16% increase in customer support calls.

Data informed decisions are when people act as though the data will tell them what to do. As shown in these two cases.?

Data-driven decisions are made when the appropriate analysis is done to quickly understand what the data means and how it backs up recommendations that are in line with their business goal or objective.

The Cost of Ineffective Decision-Making

We have all experienced ineffective decision-making. Endless meetings without resolutions, deferred or avoided decisions, poor choices, and a general level of frustration with other decision-makers. These are telltale signs of this systemic issue. The cost? A staggering estimated $250 million annually in lost opportunities and 500,000 hours wasted in meetings, and as much as 3% of profits.

Data-Informed vs. Data-Driven

A problem we most often see is that the decision-making process and the data analytics process are treated as separate. Or, at best, the data analytics process is sandwiched between a business problem and a presentation of the data.?

When the data team starts to engage with business stakeholders or teams, we encourage them to ask, “How will the data be used?” and, “What is the problem you are trying to solve?”.

Another best practice is to ask? “What decisions do you need to make with the data and how will you make them, and when?”.?

This leads to an understanding of who will make the decisions, aka who is impacted by them. It gives the data team the chance to see who should be involved from the start. If the right people are not involved, we often see the data used for cross-functional finger pointing. This leads to conflict or resistence to solutions, and the data team can get caught in the crossfire!

If we can get people and the data-driven decision-making process in sync, we can unblock the human barriers to getting value from our data.

Becoming data-driven in Three Steps....Continue to Read