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You may hear the term, “data-driven decision making,” which in today’s business landscape sounds like it could be the best way to make informed, timely decisions. But this term could also be a bit misleading as it competes with the similar concept of “analytics-driven decision-making.”

So what is data and analytics-driven decision making? And what’s the difference?

Data-driven decision making refers to the process of making business decisions with an emphasis on a quantitative approach, which uses number crunching and data processing to yield results as number-based information. The implications of a chosen decision can be calculated through predictive models and presented as predictive data, but it’s more along the lines of numbers in, and numbers out and the outcome is only affected by the inputted variables.

In general, analytics is defined as the discovery and communication of meaningful patterns in data. So analytics-driven decision making takes decisions one step beyond, and into qualitative analysis. This next step combines quantitative and qualitative data and adds a layer of analysis, which provides an additional level of consideration and shows varying factors that influence the decision-making approach.

The result of analytics-driven decisions? A different way to make decisions, which can be more effective.

As an example, analytics-driven can be demonstrated as the difference between using the collected data of a consumer sale – type of products, transaction amount, etc. - to make merchandise purchasing decisions, and alternatively using the checkout data combined with consumer behaviors - what consumers left in their carts and didn’t buy - to make merchandising decisions. Although a key metric in measurement could be the type of products and transaction total, it doesn’t provide the full picture of what is happening and why.

By focusing on an analytics-driven decision-making process, companies can focus on the what and why, and can sustain a competitive advantage through understanding how to effectively use and integrate technology, business processes, data, and metrics.  But, many companies face a challenge in understanding how this works or leave it out of a function of the business, causing only certain aspects to be analytics-driven and leaving some employees beyond the reach of real-time accessible data.

That absolutely poses a challenge since the accessibility of analytics function should cut across all businesses and become part of the overall leadership. It’s difficult for employees in any function, who have the analytics they need, to ignore the facts and go with a gut instinct as their justification. This is where better, more informed, and strategic decision making happens.

So what is the solution to initiating the shift to becoming analytics-driven? It’s really all in having the right tools and the right training. 

For a company to use analytics effectively and integrate into the functions of the business, knowledgeable people and effective tools need to be deployed. For many companies, this leads to needing external support and finding ways to innovate the business intelligence, enterprise performance management, and governance, risk, and compliance functions.

While launching an analytics-driven initiative can seem like a lot, it can actually be implemented fairly quickly through planning with a team that is knowledgeable about best practices, ways to migrate data and create a seamless deployment, and ensure optimization of business performance and minimizing risk.

Read more about Services for Analytics and how these services can lead to an analytic decision-driven organization with successful outcomes.