As education institutions continue their digital transformation journey, the importance of institutional data analytics is becoming clear. To keep up with the ever-evolving industry, institutions need to be utilizing their available data resources. This is crucial for operational efficiency, productive student services and informed strategic decisions.
As presented in the EDUCAUSE Top 10 IT Issues 2023, data and analytics are “indispensable” to HEIs.
”For Higher Education institutions, harnessing the power of analytics – using data to inform and proactively guide institutional actions and decisions – is no longer an optional ‘extra’.EDUCAUSEWhy IT Matters to Higher Education, Review
A Modern Framework for Institutional Analytics
Last month in EDUCAUSE’s ‘Why IT Matters to Higher Education Review,’ a team from Ithaca College in New York revealed a modern framework for Institutional Data Analytics. This framework includes 8 guiding principles and 8 key competencies needed to utilise data for actionable insights in HEIs.
Let’s take a look at what they tell us.
The 8 Guiding Principles
1. Data should be timely
What it means: The first principle highlights the importance of real-time, accurate data. To make effective use of data insights, institutions need to act on data that is up-to-date and relevant.
2. Data should be consistent
What it means: Institutions should standardise the definitions, measurements, and KPIs used in relation to data. In addition, these should be commonly understood. This will help reduce data illiteracy and improve data impact among stakeholders.
3. Data should be trusted
What it means: With the right steps in place to consistently and continually ensure data validity and accuracy, stakeholders should trust in the insights provided.
4. Data should be relevant
What it means: Standardizing measurements and metrics, as well as forming a data analytics strategy, will help HEIs produce the right insights into the most critical areas.
5. Data should be interactive
What it means: Data should be presented in such a way that is accessible and allows users to explore its insights in greater detail. User-friendly, dynamic data dashboards and drilled-down reporting capabilities are important.
6. Data should be connected
What it means: A major issue for institutional data analytics is siloed data, contained in various systems. Data should be brought together in such a way that it is useful, actionable and able to inform strategic decisions.
7. Data should be accessible
What it means: The relevant data should be easily accessible by the relevant stakeholders. Additionally, this should be in a way that is compliant with privacy ethics. This is why interactive data dashboards and detailed reports for actionable insights are important. Effective visual representations are critical for unleashing the power of data. Users should then be able to easily act on data insights, by enabling interventions and having clear next steps.
8. Data should be actionable
What it means: Finally, this point is highlighted in the EDUCAUSE piece as the most important principle. In combination of the other principles above, data should create opportunities. This means opportunities to enable effective interventions and inform student retention strategies, as well as improve campus efficiency and provide insight into the student journey.
The 8 Competencies
The article in EDUCAUSE’s review also outlines 8 key competencies. Institutions need to achieve these to fully leverage actionable data insights.
Key Competencies, EDUCAUSE – ‘A Modern Framework for Institutional Analytics’
Some of the essential aspects include:
- Establishing a modern data environment to store data, connect various systems, and remove data silos.
- Building an institutional data governance framework in addition to standardising goals and priorities for data analytics.
- Creating a community and culture of data among all stakeholders and users. This should breakdown data related barriers and improve data literacy.
- Utilising the right technology, systems and tools to support your institutional data analytics program.
The key takeaway from this framework and it’s principles and competencies: Institutions need data analytics to maximise their potential and the potential of their resources. Saying that, the authors encourage institutions not to be afraid of the process and to approach it incrementally.
Once HEIs have begun their digital transformation, modern cloud-based, data-driven systems can help them to implement and achieve their data goals. Ultimately, whether your institutional data analytics goal revolves around student retention, student experience, campus efficiency or equitable access, there is a data-driven system that can support it.