As this year’s EDUCAUSE Annual Conference approaches, a growing number of higher education professionals are focused on how they can turn their data into analytical insights that increase efficiencies, lower costs and improve the education environment for both students and faculty. Among higher education institutions, some have yet to develop an enterprise data strategy because they’re unsure where to begin—but they’re eager to try. Others are so anxious to reap the benefits of analytics, they’ve rushed headlong into analytics projects without the data management tools to ensure a successful effort. The interest is easy to understand; the potential of predictive analytics in higher education is substantial. But unless organizations begin with a solid data management foundation of data quality and data governance, they can’t begin to use analytics to generate accurate, meaningful insights.
Leading up to the EDUCAUSE Annual Conference 2019, EDUCAUSE published a three-part blog series highlighting key takeaways from the 2019 Enterprise Summit co-hosted by EDUCAUSE, the Association for Institutional Research (AIR) and the National Association of College and University Business Officers (NACUBO). This series demonstrated a keen understanding of the data management capabilities that are required to effectively leverage analytics, and the key components needed to foster a data-driven culture. The series explained the potential of analytics in higher education, why it’s time to start leveraging institutional data, and the importance of building a data management foundation (part one). The second article addressed the impact of analytics on professionals working in higher education (part two). The final installment explained critical parts of a data management foundation, such as governance, collaboration and communication to unlock a successful analytics future (part three).
Collectively, the blog series painted a clear picture of how analytics can be leveraged to improve enrollment numbers, increase student success, develop innovative teaching approaches and enhance research efficiency. It also explained how data governance, data understanding, data quality, collaboration and communication are critical components that must be in place to ensure analytics can accurately predict behaviors and trends, optimize processes and achieve objectives. But to be successful, these data management efforts must be an enterprise undertaking, and ideally integrated to build a data-centric culture and ensure valuable analytics outcomes.
Higher education institutions have amassed huge quantities of data, but it is data quality, not quantity, that turns raw data into actionable business intelligence. If data used for analytics is inaccurate, incomplete or inconsistent, what do you think the results will be? Any “insights” will also be flawed and questionable, breeding data distrust among data users. For higher education institutions to create effective predictive models or successfully leverage advanced analytics, they require accurate, consistent and reliable data. And that means ensuring data quality across the entire data supply chain.
Enterprise data quality needs to build user trust, ensure data accuracy and measure data quality for every critical data asset, and the best way to do accomplish this is to integrate data quality into an enterprise framework of data governance.
Today’s data governance is far different than the governance of just a decade ago. It used to be that data governance was concerned mainly with setting policies and processes related to data use, as well as ensuring regulatory compliance. But data governance is now about the people, processes and technologies that enable higher education institutions to pull value from their data. Data governance creates educated data consumers who understand data, know where to find it and are empowered to use it. As the EDUCAUSE article series emphasized, collaboration and communication complement this data literacy to help build a data-driven culture to achieve objectives and generate ROI.
But data quality is vital to these efforts. Bad data causes data distrust, and if users don’t trust data, they certainly won’t use it. With quality-powered data governance, institutions can assure data integrity by utilizing balancing and reconciliation techniques as data moves between different systems and processes, and implement data quality checks and validations to verify that the data is complete, consistent and accurate. Finally, data quality scoring reassures users that data is fit for purpose and of sufficient quality for analysis. It is through an iterative process of quality monitoring, improvement and measuring that quality-powered data governance fuels analytics with the right data for the right project.
“Advanced analytics” can be a daunting concept, bringing to mind highly-skilled IT professionals and data scientists working tirelessly to mine insights from masses of data. While this perception can be true, the fact is, the democratization of data is delivering the possibilities of data analytics to a wide scope of new data users with a broad range of responsibilities, priorities and objectives. Among these administrators, researchers, professors and other employees, some may be focused on streamlining admissions. Others are concerned with ensuring each student receives the correct amount of financial aid each semester to pay for expenses such as books, housing, tuition and meals. Still others are tasked with managing disclosures, reporting and other compliance requirements. But common among all of these roles is a desire to be less reliant on limited IT resources who may not be able to generate results in a timely manner.
Self-service data analytics automates data blending and cleansing, and empowers more users to quickly and easily profile, aggregate, correlate and transform data to deliver actionable insights across the organization. These tools are far superior to traditional “data preparation,” which might involve manually cleaning up and combining data in spreadsheets, and can easily take months to complete before analysis can even begin.
Self-service analytics tools bring a level of speed and automation that isn’t possible with many traditional IT data preparation tools, including ETL. And integrating analytics with data quality and data governance gives users an integrated, agile data consumer experience that enables them to quickly uncover insights to improve processes, programs and the student experience.
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