Why you need to “do more with more”

BrandPost By Bryan Kirschner
Sep 17, 2020
Technology Industry

The winning way of working for a data-driven enterprisernrn

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Credit: istock

By Bryan Kirschner, Vice President, Strategy at DataStax

The operating model of an organization is the bridge between its strategic intent and effective execution. It includes organizational structure, accountabilities, governance, and ways of working alongside people, process, and technology.

Design principles for the operating model should crisply reflect how a company has chosen to compete and win. For example, “optimizing for rapid integration” would reflect a choice to drive growth through acquisitions.

If you are choosing to compete and win as a data-driven enterprise, your operating model probably needs to change in some obvious ways. Your technology architecture must be optimized for real-time data processing. Your people need to skill up on turning that capability into reactive apps using the modern stack.

But there are some equally important but no-so-obvious ways as well. Data-driven enterprises get compounding returns from the scale of data, the scope of data, or both. They must optimize their operating models to muster both sufficient boldness and patience to do what it takes to achieve them.

We’ve seen this play out in the technology sector.

It took Netflix until 2013 to achieve the scale needed to create data-driven original content. Now, with the expanded scope for experimentation that this affords them, they are able to turn the flywheel of insight into both what viewers like among content that already exists and where there are “binegable” gaps to fill even faster.

In 2009, Microsoft’s partnership with Yahoo was the price it had to pay for the scale needed to keep its Bing search engine competitive with Google.

Both cases point to the winning way of working for a data-driven enterprise: “doing more with more.”

The connective tissue of your organizational structure, accountabilities, and governance were probably not optimized to create data volume and velocity in order to exploit data abundance.

Consider how an opportunity for a data-driven enterprise might stress the legacy operating model. Delivering a new value proposition combining data produced by two different lines of business may be possible only if one of them achieves a scale it would not achieve organically. And then an advantageous third-party tie-up may be possible–but only if the new offer is first past the post needed to reach a compelling scale before the competition.

The worst-case scenario is implicit or explicit pressure to focus time and attention first and foremost on “doing more with less.”

The good news is that you can detect the misalignment of incentives by the questions teams are putting at the head of the queue.

If they are prioritizing “Who negotiates data access between business units?”, “How do we charge third parties so we can monetize the data immediately?”, “How do I justify storage costs?”, or “How often should the data committee meet?” they are actively creating execution risk for the data-driven enterprise strategy.

Instead, teams should be diving into questions of “How could we exceed our competition’s NPS by 20% through data products?”, “How can the data products increase our average customer transaction volume?”, “What might stop us from delivering data features at the speed of app features?”, and “Who are your data product owners?”.

If they answer these questions about velocity and value, what follows will be the right answers to questions about cost and control. But if they start with the latter, at best they’ll get to the former slowly–and maybe not at all.

Read about how data-driven enterprises can make data most valuable for consumers here.

About Bryan Kirschner:
Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.