April 24, 2018
In the Digital Age, data touches every dimension of an organization’s operations. Strategic decision-making, productivity, compliance, and marketing all rely upon high-quality data. And as big data, artificial intelligence, and data attribution become central to how organizations differentiate themselves in the marketplace, maintaining efficient control and accuracy is crucial — and sometimes complicated.
If you’re concerned about the integrity of your data, you’re not alone. Institutions of all sizes are struggling to improve data efficiency, quality, and access. Why? Because they realize that good data isn’t tangential — it’s mission-critical.
Understanding terms is fundamental to understanding the responsibilities of successful data management. Data governance refers to the macro-level policies and procedures that guide the availability, usability, integrity, and security of data. Effective data governance programs are typically established and maintained by a governing body, a set of data protocols, and a plan to execute/enforce those protocols.
Data stewardship, on the other hand, is all about the micro-level or day-to-day management of data assets. The goal of stewardship is to provide users with high-quality, accessible data within the parameters of an institution’s governance policy. Though some enterprise-level businesses have created formal data steward positions, I’m unaware of any such positions within higher education. Over the years, I’ve affectionately referred to the countless unsung college and university data heroes as “data champions.” (If you know the people who fill this vital role at your school, buy them a cup of coffee sometime.)
Once you’ve established a framework for clear governance and stewardship, it’s time to tactically examine your data and set necessary KPIs. Here are seven important considerations to keep in mind as you move forward:
I’ll wrap things up with a quote that eloquently captures both the power and potential of reliable data. In his white paper, Big Data in Big Companies, Thomas H. Davenport writes, “It’s important to remember that the primary value of big data comes not from the data in its raw form, but from the processing and analysis of it and the insights, products, and services that emerge from analysis. The sweeping changes in big data technologies and management approaches need to be accompanied by similarly dramatic shifts in how data supports decisions and product/service innovation.”
At Stamats, we help colleges and universities across the country use data to build more strategic and effective recruitment, enrollment, and communication processes. From the advanced predictive models we develop with machine learning-based algorithms to the simplest program demand assessments, we believe sound data practices are the foundation of accurate and interpretable insights. For more information on our services, please call me directly at 319-861-5054 or email email@example.com.