Higher Education
Data Quality and Assurance are Vital in the Education Sector
The ability to track performance, mange student records, track inventory and manage complex systems, is needed - both to ensure data is accurate and secure, but also to provide the necessary insights for the organization to make informed decisions and operate efficiently. Improving data collection and data quality in the education sector can improve decision making.
At a granular level, student data comes from disparate sources, such as exam results, parent feedback, library cards and incident reports. Each is vital for the smooth running of your systems, but manual processes result in errors and duplication, and in data being inputted in different ways. The right data management in the education sector provides helpful insights into student performance, allowing you to undertake thorough data analysis of exam results and scores.
Delivering Accurate Data in The Education Sector
To run an effective education system, systems and processes are implemented to test data and undergo data analysis to reduce variance, which can damage data quality in the long run.
Typically within higher education, many legacy systems will feed an on-premises ERP system such as SAP Hana (see section on ERP Migration). But with many of these older ERPs no longer supported, and with the increasing demand for composable systems frameworks, with multiple inputs and outputs, manual processes of data cleansing, transformation, migration and testing, are quite simply no longer fit for purpose if in fact they ever were).
Data quality assurance in the education sector can produce a single version of the truth, so your institution can run better than before. Complete data certainty can shine a light on what works and what doesn't when it comes to teaching your students, giving you a chance to create a bespoke learning environment that is monitored and measured.
Typically, the education sector can experience a number of problems in data quality assurance.
These include:
1. Lack of quality data consolidated in a single source
2. Unable to track performance systems crucial to the learning process
3. Poor testing services for new programs
4. Lack of accountability
5. Lack of trust in the information and reporting
6. Delayed and challenging decision-making, frustrated by uncertainty around data truth.
The Rapid Changes in the Data Landscape
Even before students experienced their first online lesson, the education sector needed to adapt faster than ever before - rapidly adopting new technologies and processes, training staff at every level to use new systems and technologies, and onboarding students at the same time. But integrating these new systems with the legacy frameworks that underpin most educational institutions, is not without risk.
Data breaches, insecurities, duplication, miscommunication and amplification of errors in existing data frameworks, all become much more obvious.
Whilst many institutions have moved back to in-person learning models, many of the systems and frameworks remain - especially in light of uncertainty about the future.
In fact, only four things are certain:
1. Peoples' modality and expectations of work and study have changed forever. Remote and hybrid educational models are here to stay.
2. Legacy systems that were not fit for purpose before, are now creaking at the seams. data breaches and security issues are more likely than ever before.
3. Manual processes are no longer fit for purpose. When there are errors and duplication across a large but unknown number of records, manual processes that check, for example, 20% of students records, leave 80% of the data inaccurate and unchecked.
4. Automated process, including automated testing and in some instances, machine learning and augmented intelligence, can save 50% or more in time and cost, compared to manual data processing.
"IDS are able to scale what clients need. There's a level of confidence with IDS and that's down to James and the team.
- Kaljit Virdee, Senior Project Manager, University of Leeds
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How to Increase Effectiveness of Software Testing in Higher Education
Higher Education & Data Quality at a Glance
Here are ten of the most common challenges we see when working with clients in the education sector:
1. Expensive and time-consuming manual data assurance processes
2. Lack of capability due to under investment over time
3. Lack of resource, especially skilled resource
4. Lots of legacy systems
5. Requirement for digital transformation
6. Rapid move to remote learning
7. Lack of security - universities struggling with risk management and governance
8. Resistance to change
9. Forced migration from unsupported legacy ERP systems
10. Lack of single-point of truth for decision-making.
If your project or institution happens to suffer from any of these, find out what iData can do for you.
