How Mature Are Your Data Capabilities?

When technology isn't enough  |  April 27th, 2017

In a previous post on data maturity, we discussed a company that was just embarking on a transformation: launching a new services business and building data capabilities to support that business. But what if you’re not starting from the beginning? What if you’ve already been embracing new technology, conducting pilots, and launching new analytical platforms? Recently, we were working with a Fortune 500 industrial company in the midst of developing software services to improve product R&D and enrich the customer experience.

Their goal was to use data to empower decision makers across every part of the organization to make robust, data-driven choices. The company had great talent, technical vision, and infrastructure. Still, they weren’t generating the progress they would have liked at a rate they would have expected. What was wrong?

We were asked to perform an assessment of their capabilities and architecture to help them become truly data-driven.

Understanding their overall data maturity shined a light on areas requiring attention to get the most of their technology investments:

  • Missing links between projects and metrics: The initiative’s overall success was being measured by a single metric that they could only begin tracking in 2020—at the completion of the transformation. This led to significant uncertainty within project teams building new capabilities and platforms. Many teams were unsure where their analytical work fit in the big into the larger efforts and, more importantly, whether they were contributing to the overall success of the transformation.
  • Lack of cross-functional teams: The analytical infrastructure built by the engineering team was impressive, but was sorely underutilized. The data scientists had not been trained to use it and did not know how to access it. We heard from an analytics manager: “Seventy percent of my team’s time is spent on writing UDFs and Pig scripts to access data!” Creating teams that facilitated collaboration between engineers and data scientists was an opportunity for quick productivity gains with expensive talent.
  • Siloed business functions: Teams felt a lack of clear objectives that stemmed from communication and information sharing issues with other teams. For example, one team integral to product development described their view of the future as “a dusty window.” Business units on the consumption side of application development experienced very uneven usage of analytical tools. Strengthening these partnerships was crucial as the overarching project’s success relied specifically on strong analytical capabilities throughout the entire organization.

In working together, we helped the industrial company link their data and analytical capabilities with their ultimate business objectives, allowing them to create the right metrics to truly understand their progress. We helped them improve their devops capabilities and better integrate their engineering and data science teams. Collectively, this helped them break down technical and organizational siloes that were hampering progress.

Understanding the uneven maturity of their capabilities across people, process, and systems gave them the answers they needed to the question on everyone’s minds: How can we see real results faster? A view of their current state of maturity along with a clear target for where they needed to be set a baseline and a way to measure progress.

Frustrated with the progress you’re seeing from your data and analytics investments? You don’t have to be. Understanding where your capabilities are strong and where they’re lacking gives you a great lens for directing and prioritizing investments. Doubling down on your strengths is often a good strategy, but not if immature capabilities are holding you back.