The ‘Who’ And ‘How’ Behind Data-Driven Decision-Making

Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Milen Mahadevan, chief operating officer at 84.51°.

As the explosion of data grows, many are consumed with questions of data quality, connectedness and infrastructure, not to mention what to do with it and how to apply analytics, machine learning or artificial intelligence to make sense of it all.

There are two areas that are critical: who (the talent) and how (the principles for getting the most out of the data).

Yes, data is super important, as is the technology to manage it, but without the talent or right way of using data and analytics, data will never be the game changer many predicted it to be.

Not Enough Data Scientists Or Data Engineers

There has been significant media coverage over the last few years around the shortage of data engineers, data scientists and data-driven decision-makers.

It is likely that you are in a position where you do not have enough talent in your business for your aspirations. You aren’t going to find a silver bullet here for solving that; it’s tough, the market is harder than ever before and will only get more difficult as many more companies find the advantage of using data. Just like evaluating data sources or technologies is not a numbers game, it is about fit, uniqueness and quality. You need to pay as much attention to the talent you are bringing in and the team you are building as the purchase orders you are signing.

Data science and data engineering roles are generally broad and dynamic. Since the technologies change and the expertise needed shifts, you are seeking talent that is flexible and shows the ability to grow, learn and acquire new skills. I wish there was predictability in where really good high-quality talent comes from, but I have found it comes from everywhere and a variety of different backgrounds.

Don’t allow expectations of a specific background to drive decisions. Look for people who have a foundation of knowledge, even in different fields, and bring a diversity of thinking to the table. As you build that team, keep growing the problems that they take on, make them more and more challenging and show the team how they are making a difference to the business so they can see the impact.

As The Saying Goes, Culture Eats Strategy For Breakfast

Many companies have developed analytics or engineering teams to unlock the power of data insights and data-driven decision-making, yet many are not seeing the impact. The organization may not be ready or mature enough or management doesn’t like the answers. The team can end up in a loop, either miracle hunting or just creating reports because they can’t push past through the barriers.

This is a critical part of the leadership role; to drive a culture change, the foundation must be prepared. Companies can invest in data, technology and talent, but if they don’t invest in developing the culture of the organization and driving decisions through data, they will struggle.

Data and analytics should be a strategy for the organization, and it needs to start at the top. Leaders should use simple examples to show how decisions could have been different with the use of data and test markets or segments to show the continuous improvement that is possible. They must take senior management on the journey.

In parallel, leaders must also instill a culture within data teams that leverages data toward results and value creation, continuous learning and collaboration. Since success comes from working across the organization and no single team can make it happen, business alignment is needed.

Data, technology, talent and process are all important, but the last two are not heavily weighted enough in many companies. If you want to make data a competitive advantage, it isn’t about the data but what you do with it. The “who” and “how” become the critical questions to answer.

Follow 84.51° (@8451group) and AdExchanger (@adexchanger) on Twitter.

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