Understanding Your Data Maturity

First steps in data strategy  |  April 12th, 2017

Businesses today are experiencing rapid change, inside and outside of Silicon Valley. Even coffee isn’t immune—just last year, Starbucks crossed the threshold of more than 20% of transactions being done in their mobile application. Data is the currency of digital transformation, and data capabilities are the battlefield influencing market share and profitability.

No two situations are the same, but at SVDS we have found one truism: making a data transformation successful requires much more than simply getting the technology right. Across a variety of industries and operations, we consistently find the influence of systems and people to be deciding factors. This is one of the reasons why we developed our Data Maturity Model—a tool for understanding how well your data capabilities create value for your business, across people, process, and systems.

Data maturity in practice

Data maturity is a useful tool for measuring the progress being made against your transformation. Recently, we were working with a multi-billion dollar industrial device company that was just beginning their Internet of Things (IoT) transformation: integrating software services with their physical devices. The vision was set and small experiments were being run throughout the organization—but the real work of building had not yet begun.

The company’s overarching strategy made a lot of sense—building a higher-margin services business from the key position their devices play in their customers’ workflows. We were asked to help them develop a data strategy and accompanying architecture to make that possible. Fundamentally, they needed a plan to use analytics and device data to create new services their clients would value.

While this company certainly had major technology investments in its future, some of the most urgent things necessary for this transformation to be successful had little to do with technology:

  • New incentives to change required: Like many established product companies, the organization was structured around distinct, mature product lines. With very different P&Ls and incentives, we were told: “Careers are made within the business units, not the company.” There was an inherent skepticism for investing in unproven growth, especially if the effort was performed by a centralized function.
  • Lack of experience with data rights: At this time, a very small percentage of overall revenue was produced through software. To sidestep questions of license management, the default behavior was to “make stuff free so that we do not have to create licenses.”
  • New customer relationships (and approach to product development): Getting the most value from device data meant integrating it with some of the customers’ own data sources to provide greater context to end users. This meant expanding or revisiting customer relationships to embrace data sharing, requiring updates to processes in sales management (i.e., around channel or partnership agreements) and overarching application strategies (e.g., creating standards and cultivating ISV ecosystems).

In working together, we helped the industrial product company identify solutions for incentivizing technical investment. We identified policies required around data ownership, custody, and consent as a precursor to becoming an integrator and reseller in the supply chain of data services. We helped them update their architecture to support these services. It is not uncommon for an SVDS maturity assessment to reveal a broad set of opportunities.

Ultimately, our client was equipped with a broader perspective on what they needed to launch their software business and a roadmap to build all aspects of their data capabilities, ensuring that their transformation would be more successful. A clear roadmap helped them fill in the gaps between here and there, illuminating the concrete steps they needed to achieve their vision. Although transformation does not have a fixed end point, milestones are important—change should be incremental enough that the organization can see progress.

What’s next?

If your company is at the beginning of its data transformation, it is easy to get caught up in all the upcoming technological change. Resist that urge, as honestly evaluating the maturity of your people and processes will help you identify where you need other investments to ensure a successful transformation.