The Journey to Data Analytics Maturity


The Journey to Data Analytics Maturity

Delivering better citizen services begins and ends with data. Whether it’s fighting the opioid crisis, creating “smart communities,” or launching other innovative citizen-centric initiatives, data is a critical component that helps our state and local agencies make better decisions that positively impact people’s lives.

But raw data in itself poses no real benefit to an agency. Call it the technological equivalent of white noise; it’s comforting to know that it’s there, but it doesn’t truly serve a purpose until it is made actionable through analytics.

Building an Analytics Center of Excellence

Building a data-driven organization starts with embarking on a journey toward analytical maturity. The concept was originally established by authors Thomas Davenport and Jeanne Harris in their book, Competing on Analytics: The New Science of Winning. We took what they created and developed our own five-step process to help organizations build their data analytics programs, ultimately culminating in establishing an Analytics Center of Excellence (CoE).

This conceptualizes the journey to analytical maturity as a methodical process in which each stage builds off of the other. Organizations begin fresh at stage one, make incremental steps by dabbling in localized analytics in stage two, further their commitments in stage three, and truly broaden their analytical capabilities in stages four and five. They can start small and use incremental successes to affect greater change until the entire organization is analytically mature. Eventually, many agencies will reach the CoE stage, at which point they have dedicated teams of data analysis experts in charge

 of collecting and sharing benchmark data.

The dimensions of analytical maturity

Within each of these stages there are four supporting dimensions of analytical maturity. These dimensions are data, technology, organization, and process:

Each of these dimensions plays a key role in the journey. During the data dimension, organizations begin collecting the required capabilities for data quality, governance, modeling, and management. They then move to the technology dimension, in which they build a scalable and powerful enterprise analytics platform. The organization dimension includes training, managing change, and soliciting (and getting) executive support for the analytics program. During the processes dimension, best practices are defined, made consistent, and shared across the organization.

Each of these dimensions is dependent upon the other, and organizations need to advance their data analytics capabilities within all of these phases if they hope to reach analytical maturity. A mature, data-driven organization has the right combination of data, processes, and leadership, as well as the underlying technology to enable fact-based decision-making.

Getting started

The hardest aspect of embarking on the journey is getting started. For those who need help, we developed the Imagine Innovation Framework to help public sector agencies plan, pilot, and build their data analytics projects. The framework employs number of methodologies, including creative design thinking and lean start-up and agile delivery, to develop a solid foundation for the ultimate creation of a CoE.

Then, the real fun begins. As organizations become more analytically mature, doors and opportunities open up and teams can begin thinking about ways to use their newfound knowledge to greater affect. But it’s important that agencies strive to continuously improve their data analytics efforts and always ask, “What’s next?” What new projects can we tackle? How can we use data even more effectively? How can we continue to improve? How can we become more intelligent, and use that intelligence to help our operations?

Some organizations may never end up reaching the end of the maturity journey, and that’s OK. Even getting to Stage 4 and becoming an “Analytical Organization” is a phenomenal achievement. Ultimately, the goal should be to reach a point where the organization is able to experiment with new technologies, explore new methods of information sharing, and discover new data-driven insights.

The important thing is understanding what the journey to analytics maturity entails, including the signposts to look out for and the pathways to take along the road. Agencies that set out on this road will be able to make more informed decisions that can reduce costs, improve efficiencies, increase citizen engagement, and deliver better services to their citizens.