I can’t remember the first time I heard the term “Center of Excellence”, but I remember immediately falling in love with it. It was really just a rebranding of the “Competency Center” concept, but I was enamored with how aspirational it sounded. Why strive for mere “competency” when you can be “excellent”?
Sadly, I think this very root sometimes leads people astray as to the true purpose of a Center of Excellence – whether it pertains to data, as I’ll discuss here, or marketing, innovation, people management, or any other cross-functional concern. There is so much focus on the “Center” part, and not nearly enough on “Excellence.” Don’t get me wrong, the “Center” components — who makes up the group, how often they meet, what is and isn’t in scope — are critical. But “Excellence” — holding yourselves accountable to achieving the highest possible standard — is the real objective, and what will ultimately demonstrate real business value for the CoE’s initiates.
In the world of data, for example, a high-functioning Data Center of Excellence is not merely a steering committee. Rather, it’s a whole-of-organization effort that includes leaders and individual contributors from across the organization, representing both the business and the technical side. At SQA Group, we believe there are six pillars that make up the critical components:
- Adoption & Tools
These are the six areas that the DCoE helps to drive, as well as the six ways the DCoE helps an organization become more data-driven and data empowered.
Learn More: Data Center of Excellence
Let’s look at each of these pillars and their role in driving data excellence:
- Strategy is the foundation of your organization’s approach to data. It details not only what your data initiatives will entail, but how they will align with your business strategy and objectives.
- Governance is a set of practices to ensure your data strategy is executed successfully even as it evolves. It includes defining the roles that individuals will play in bringing the strategy to life; setting standards, controls, and policies for how data is handled by both systems and personnel; and balancing resources to ensure timely completion of data initiatives.
- Measurement focuses on the ways that data is applied to provide insight into the business and guide decision making. This includes Key Performance Indicators (KPIs), Objectives and Key Results (OKRs), and other metrics.
- Adoption considers the technology currently deployed in your company, and how widely used it is throughout the organization. Usership can be described in two ways – the degree to which persons in the organization use the tools as deployed, and the degree to which the tools’ features are made available.
- Stewardship is the practice of ensuring a high standard of data quality by appointing individuals in the organization to own parts of the data as “stewards”. Stewards protect against data becoming inaccurate, incomplete, invalid, inconsistent, redundant, and stale.
- Literacy, as defined by Rahul Bhargava and Catherine D’Ignazio, is the ability to read, work with, analyze, and argue with data. Everyone in the organization must achieve a base minimum level of data literacy so that they can effectively apply data to do their job.
So why is a Data Center of Excellence critical? Why can’t you just get away with building a data strategy and putting it into practice? As important as a data strategy is that it might struggle to be widely adopted/incorporated throughout the organization without someone, or “someones” in this case, driving it. If you look at the data strategy as a roadmap towards leveraging data for maximum benefit, the Data Center of Excellence is the car that gets you there. Consider just a few of the factors that a static data strategy might not effectively address:
- Strategy evolves. The data strategy isn’t implemented overnight. Business objectives, technological advancements, and real-world conditions can change on a dime very quickly. The DCoE adds a nimbleness to the strategy by providing a means to shift focus if externalities demand it.
- Priorities must be balanced. The best data strategies are driven by business need. But when you get down into specific use cases, competing interests emerge. If sales and marketing disagree on how customer data should be handled, who wins? Is the answer always one or the other, or does it depend on where the data originated or what it’s intended use is? Governance and Stewardship exist to reconcile these differences while ensuring data remains consistent.
- Technology sometimes goes unused. Data strategies include implementation of certain tools to empower business users to get the most out of their data. But in the U.S., $30 billion is wasted on unused technology every year. This is where Adoption comes into play – making sure that users are getting the most out of the technology that they have installed, that companies have implemented all useful modules they have at their disposal, and only then identifying where new tools and technologies should be deployed.
The Data Center of Excellence turns the data strategy into a living, breathing document. It puts words into action. It balances organizational priorities. And it ensures that decision-making around how data is to be used is handled in a democratic fashion, rather than unilaterally by a single business leader or by IT.
If you’ve built a data strategy already, don’t let it whither on the vine. And if you’re still working on building the data strategy, now is a great time to get the Data Center of Excellence in place so you can hit the ground running when it’s time to execute.
Ready to bring your data up to a higher standard? Click here to learn more about how we help companies like yours spin up a Data Center of Excellence to become more data empowered.