data-informed strategy

Data-Driven vs. Data-Informed: What’s the Difference?

A major goal for businesses in 2023 is to gather and analyze collected data to make smarter business decisions. Countless technology articles have been written about the untapped value of data analysis and how a data-driven philosophy is the right move going forward. While this may be the case in some situations, there are distinct caveats of a data-driven model that may lead businesses down the wrong path when strictly looking at “the data”. 

An alternative to a data-driven approach is known as data-informed. This hybrid methodology eliminates many data-driven caveats and may be the perfect fit for those who are seeking a broader picture when it comes to decision making. Let’s look how data-driven and data-informed approaches work along with how they differ.

By Andrew Froehlich

What Does It Mean To Be Data-Driven?

The term “data-driven” has been a buzzword for the past several years. The concept is to collect internal and customer-facing data collected from various sources including in-house corporate apps, customer relationship management, customer experience, and customer data platforms. Once collected, data is organized and cured, then placed into one or more business intelligence (BI) platforms. These platforms use artificial intelligence to analyze the data with the output being auto-generated business decisions that the organization should act upon.

While business intelligence has been an option for businesses for decades, the ability to collect so much data and analyze it with the latest in AI is a major leap forward and can produce meaningful insights that may never have been previously considered. Businesses that attest to data-driven philosophies also point out that decision making can be streamlined, making it possible to make faster decisions that can lead to competitive advantages.

What Does It Mean To Be Data-Informed?

Instead of relying purely on data to make decisions, a data-informed approach uses these same systems and processes to analyze data — yet does not base business decisions solely on the analysis output. Instead, the data is used as a guide or measuring stick, combined with human-derived information coming from business leaders and decision-makers.

Those that prefer a data-informed approach to business decisions cite the fact that while data is indeed useful from an analysis purpose, it often misses the mark. To “right the ship”, the analyzed data is combined with executive experience and external analysis of business and market vertical trends that humans continue to excel at.

Which Approach Is Right for You?

Choosing between being data-driven versus data-informed largely boils down to determining whether the business is looking to make decisions in a stable or unstable business market. If the organization is considered stable and unlikely to make major changes to their internal or external operations, being data-driven makes sense as it produces far faster results that can be acted upon. Additionally, because AI is performing the bulk of the analysis, it allows the business to instead focus on implementing those insights to the best of their ability. Combined, these benefits can give them an edge when it comes to speed and quality of execution.

But as we all know, most businesses are in a constant state of flux and often pivot their business models and goals to more profitable markets. In situations like these, using data analysis can help provide insights in how to better execute how they’ve operated in the past. What’s missing, however, is the visibility of how markets or business practices are changing over time. It’s here where executive experience comes into play as they are adept at watching various trends and understanding the right time to act upon them with a relatively high degree of accuracy. Thus, in most situations, being data-informed is a more appropriate approach despite the slower pace when executing on those insights.

This article originally appeared in Information Week.

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