As an organization, you are certainly planning or rolling out an enterprise-wide data strategy. You probably have done anything from setting up a lab, building data lakes, acquiring analytics and process mining platforms to hiring/contracting the appropriate staffing for it. You may even have appointed a “Chief Data and Analytics Officer”

You already know that it takes time and effort to bridge the gap between what that data strategy is designed to achieve and the direct needs of the business leaders and process owners to exploit data insights to optimize and transform operations today. Many of our “strategic” solutions are slow to deliver and are more “brittle” than we were led to believe.

This gap, perhaps “chasm”, is discussed in many blogs and scientific research papers. Depending on the position of the author, the summarized solution is given by applying more technology (the analytics, mining and ERP vendors), more technique (the data scientists and statisticians), more structure and organization (the analysts and the scientific community) and more resources (the service providers).

The reality is more fundamental. We have invested for years in building application and data architectures, landscapes, services and ecosystems to support our business processes, and these are generating the promised “deluge of data”.

What we are also promised is that all this data would be directly consumable as insights by business leaders and operations, in a self service style.

This is far from reality today.

I ran a poll on LinkedIn (an experiment and a “first” for me) to determine what business operations, shared services, finance, process and technology leaders felt was the biggest barrier to making data strategy a reality. Approximately half the respondents felt that culture and behaviors (or “behaviors”, depending on where you went to school) are the biggest inhibitor. This should give us some cause for caution in large scale, enterprise-wide data programs. Maybe we need some early wins first to change the behaviors?

A recent article in MIT Sloan Management Review “To Succeed With Data Science, First Build the ‘Bridge’” by Roger Hoerl, Diego Kuonen and Tom Redman describes the need for a new organizational structure to better align data teams with business operations, and communicate between the two big groups of experts. But this is not what the “Agile” experience has taught us.

If the “Bridge” connects two disciplines, then “Business Science” is a common agile discipline employed by leadership, operations and data teams.

There is a big barrier to successful data strategy and data science and the authors address it with recommendations to develop better communication, processes, and trust among all stakeholders.

However, as with Agile, the answer is to combine the disciplines into rapid response teams rather than a “value chain”

You can read the referenced above Sloan Management Review article here

You can also read the related Harvard Business Review (HBR) article “How CEOs Can Lead a Data-Driven Culture” by Thomas Davenport and Nitin Mittal here

What is your experience?

We will be sharing more of our “business science” thinking as it becomes clear that we need a new, more agile, responsive, business-led approach.

Thanks for reading . . . .