Higher education is a prominent industry that requires high quality data to improve its efficiency. Sadly, it suffers from bad data quality by not keeping up with technological advancements.
The data collected by universities is used for various purposes including research, admissions, and forecasting decisions. But, when the data is inaccurate or out of date, it can lead to consequences like overpaying for tuition, ill-preparation for digital transformations or rejecting qualified students.
Why Data Quality is Crucial for Higher Education Institutions
Productivity - Poor data entry errors can reduce productivity as staff must perform a manual workaround, or end up submitting data quality issues into the downstream system.
Staff members might be pressured for deadlines and make ad-hoc corrections rather than fixing the problem systematically. But this can backfire if a data breach is unintentionally committed when attempting to make quick fixes.
Reputation - Bad data quality costs businesses time and money, as well as the university’s reputation. A single bad experience can quickly go viral on social media or the OFS, costing universities potential opportunities for new employees and student applicants.
Decision-Making - The better the data in higher education, the better decisions will be made.
Decisions about the selection behavior of prospective students, the retention behavior of current students and the satisfaction rate of alumni is critical for higher education institutions. Accurate data, free of any data quality issues, will help increase effective decision-making to better serve institutions’ stakeholders.
Communication - Universities will want to communicate the necessary information to the right staff or students at the right time, while minimizing response time.
Incorrect contact information with prospective students, faculty, alumni, and organizations can make it difficult for a university to reach out, and stay connected with those they need to communicate with.
Data Entry Errors
Universities often rely on student input for this information, but data entry errors quickly arise as students are often not trained in the nuances of data entry, or simply unbothered to complete required fields correctly.
This can be dangerous for a university because it could lead to decisions being made based on bad data quality or no data at all. Universities should take steps to ensure that they have good quality control over their data input to avoid poor data quality, causing lasting damage to operations.
Lack of Data Responsibility
As universities are increasingly relying on data to drive decision-making, they have a responsibility to ensure their data is accurate. But most institutions lack resources, expertise, or time to properly manage data quality problems.
The main solutions that the university can use to tackle these problems are: investing in software, hiring more staff specialized in fixing data quality issues, or outsourcing work to a third party organization, like IDS.
Universities spend a lot of time and money on manual data cleansing, that would be better spent on other mission-critical aspects.
Array of Potential Data Sources
The major data quality challenges facing higher education institutions is that it’s difficult to manage and maintain different data types. Data in higher education comes from so many different sources. The multitude of data sources, such as student surveys, school surveys and administrative records, leads to discrepancies in the information collected.
The lack of standardization across these sources leads to further data quality problems later on - like inconsistent reporting and inconsistent results.
Universities need to have a single point of data truth and a centralized system for collecting and storing this data. It needs to be accessible by all departments, but also secured so that it cannot be tampered with.
Inaccurate Contact Information
Nowadays, there’s a vast amount of data in higher education to keep up with regarding the needs of their students and faculty staff.
Data entry errors by students causes data quality issues from typos or a lack of understanding on how to input certain information correctly. These errors can be costly as they may lead to incorrect decisions being made about students’ academic progress or financial aid eligibility.
This has led many universities to turn to technology solutions like automated data quality management tools, like iData, to improve poor data quality and the accuracy of records and databases. In addition, these systems can also help reduce the time spent on manual input by eliminating errors caused by human error such as blank entries, typos or entering the wrong date or address.
One of the overlooked data quality challenges for universities is dealing with obsolete data. Runner finds if a student’s contact details are updated, but aren’t registered in the institution’s systems, this is when data quality problems begin to surface.
The main reason that obsolete, poor data quality persists in higher education is because of the lack of resources to update old data. There are also some universities who have outdated IT systems, making it difficult to update their database with accurate information.
The problem with obsolete data in higher education is that it leads to inaccurate student forecasting. Outdated information can lead to misleading predictions that are not accurate and can be misleading. For example, the institution might then end up investing resources into areas that the student will never pursue or even be interested in pursuing, damaging their overall experiences.
These data quality issues are not just prominent in higher education. They persist across industries from financial services to healthcare and affect every company that collects and uses data for strategic advantage.
The surest path to assured data in higher education is collaboration and automation. Setting data governance practices is crucial if universities want to be successful in their data strategies and provide the best experiences to all stakeholders.
IDS have the experience and expertise to provide consultation on effective data governance processes to cope with data quality challenges. With the offer of an automated support software, iData, this makes up for any resource deficits your institution might be experiencing and provide insights about your data to make better decisions for your business.
Get in touch to find out more and take back control of your data.