Data VALIDATED Decision Making

A couple of recent discussions have compelled me to clarify that Data Driven Decision Making/Management (DDDM) is in no way a competitor to business creativity, and perhaps would be better titled Data Validated Decision Making (DVDM)

Having access to the right data can be a game changer for your business, and while its collection is not necessarily free, it should never be considered as an alternative to other business approaches, only a companion.

Why we collect data

In a business context, data is primarily used to verify a hypothesis. While in some circumstances it is used to present entirely new information (eg, a news article on an unforeseen event), most commonly you have already established some question or series of questions around an idea that impacts your planning, for which you seek data to answer those questions and validate that idea.

Your question could be as simple as, “What types of customers (age, location, etc) are gravitating to our products?”. The data required to answer this question won’t be volunteered, but must be acquired to answer to your question, and verify or disprove an existing hypothesis. Not only to indicate the customer type (the direct answer), but to verify the hypothesis around the question: that our product resonates with certain customers, and we can leverage this in tailoring our product offering.

And in turn, those answers are going to generate a new cycle of questioning as you advance your product strategy. So data is used iteratively, repeatedly, and from a variety or sources as a constant feedback loop into your ideas, hypothesis, and conclusions.

Why data collection and analysis should be a first-order consideration

Planning for the use of data will accelerate and reduce the scope of the iteration cycle, however too often I see plans for a hypothesis to be executed first, then data collected for evaluation. In this case, the feedback loop is far too slow, especially when data may have been able to conclude that the hypothesis was incorrect in the first place.

Inefficient: Ad-hoc data collection
Desired: Ongoing data collection

Strong data availability is not only going to reduce the validation feedback cycle, but also the granularity of the actions you can take. We can start to make minor tweaks in our product offering to only a subset of our customers, satisfy our validation feedback cycle, then expand. This agility will allow you to iterate more freely without the risk of a big change having a major adverse impact, requiring complex sign-offs, etc.

Creativity vs Data

Before the prevalence of data, CEOs and marketing teams were using their intuition and creativity to pursue a market direction or pivot the business, and the most creative and insightful grew very successful companies.

Over time, as technology allows us freer access to data, marketing channels, manufacturing, logistics, and other business tools, the value proposition of that creativity is reducing. Iterative, data-driven companies don’t need a creative genius if they harness data and technology in the correct way, and the creative companies lose their competitive advantage.

Formally, an acclaimed designer could develop and produce unique products desired by consumers, and by the time their competitors caught up, the designer had moved on to the next unique product. Today, the most successful retailers are those which are agile: using data to identify the most desired products and reacting quickly to capitalise on that insight.

To produce the most agile decision models, validation is key.

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