Toward a data-powered organization

Three key components are necessary for managing change toward a data-powered organization.
(1) Conceptualize a data ecosystem
(2) Invest in the human resource foundation
(3) Cultivate a mindset shift

They are largely independent of the organization’s particular data assets and technologies.

Data ecosystem
Build an ecosystem to adapt to changing conditions and technological advancements, able to incorporate the breadth of data generated by your enterprise.

Doing this takes some brainstorming about what those data are and what that flexibility can mean. For example, there are masses of data – some “dusty” and “dark” in current parlance, i.e., sitting unused in the cupboard – that may have value in improving operational efficiency or fueling research. Ultimately, data are transformed into information that enable actionable insights. So, start with articulating actions you are trying to inform.

In addition, remember that the data ecosystem is not a technology project. Rather, it is a technology-enabled business strategy that is, most importantly, about enabling more efficient and effective behaviors through that data-to-information-to-insights pathway. As such, your data ecosystem must be rooted in robust processes that facilitate that pathway.

Put another way, when building or advancing your data ecosystem, higher education systems and individual institutions meed to pay as much attention to governance, user experience, and communication mechanisms as they are their architecture, visualization software, and build-versus-buy decisions.

Human resource foundation
Your people strategy must be designed to support the agility and continuous improvement of our talent and teams.

I wrote about the importance of investing in people and capacity building in my last post and can’t emphasize it enough. At UNC, the Data and Analytics area that I led went through tremendous change, particularly in my first couple years. It was an intensive and sustained process of (1) understanding the current state, (2) helping stakeholders and my team to think about and articulate needs and expectations, and (3) removing some of the perceived boundaries – empowering ourselves to think and act outside the box – and some real technical and operational barriers to efficient and innovative behavior. 

Actually, as challenging as that process of change can be, the human capital investment supporting a thriving data strategy, like a thriving organization overall, requires more than friendly and stimulating work environment and competitive compensation. In our UNC System, the culture change and continuous improvement and adaptation to new technologies and market forces required us to cultivate team learning. And although I periodically reflect on ways we could have done it better, I believe that the collaborative manner in which our IT and Data and Analytics staff engaged in and routinized team learning is the secret sauce to our accomplishments over most of the last decade. We recruited team members with varied backgrounds and complementary skill sets. We developed a cadence of check-ins and feedback sessions. We encouraged candid input and communicated both explicitly and through our actions the value of everyone at the table. Fostering that dynamic pays dividends well beyond specific projects.

Mindset shift
Capitalizing on the organization’s data assets requires a collective understanding of the value of those assets to the organization and a strategy for benefitting from that value.

It requires a view of data as valuable assets, which in itself can be a mindset shift, particularly in higher education institutions where the idea of monetization isn’t quite as familiar or comfortable as in much of the business world. More than reconceptualizing data, though, the strategy must be part of moving the entire enterprise toward a common set of goals. In other words, data strategy is an integral part of overall strategy. This is more than writing a mission statement or having a strategic plan. It means telling a compelling story about what the future will look like and enabling your team and the organization to participate in writing and editing that story.

To borrow from a series of higher education innovation sessions in which I participated, this means a shift from a “what is” to a “what if” view of the enterprise – a collective ambition. Yuval Harari writes in Sapiens (I’m synthesizing here) that the characteristic, or capacity, which distinguishes our species from others is that ability to think abstractly, to imagine a future state that is different from the present – i.e., to have a vision – and to communicate that vision across time and space so that others can be enrolled to share in that vision. Religion, commerce, political systems… and a successful data strategy – all these rely on a collective conceptualization of “what ifs.”