Predictive analytics will be the next logical step for many organisations, as educational institutions become increasingly data-led when making strategic decisions and the results, reports, and insights gained from predictive analytics continue to become more accessible. With more staff and stakeholders gaining access to data, predictive analytics will be integral to different roles across the institution.
Common misconceptions about predictive analytics in the education sector
Examples of predictive analytics are everywhere, and the education sector is no exception. The approach uses historical student data to create models that help forecast future problems or opportunities. Educational institutions are using existing student data to gain insights on current or future student performance.
There is no doubt that the past twelve months has made everyone, regardless of sector, think on their feet and adapt to new ways of living and working. One sector that has shone throughout is Higher Education, switching their focus from centuries of face-to-face teaching to a completely digital provision within days, showing just how adaptable and resilient they are.
Mike Cope spent ten years as CIO at University College London (UCL) before joining Tribal as CTO in 2019. Having seen first-hand the pushback on cloud adoption rates in the education sector for the last decade, Mike discusses why he believes there has been a shift in mindset over the last 12 months.
Migrating services to the cloud in higher education is happening at a faster rate than ever before. According to Tribal’s CTO, Mike Cope, this is primarily because the sector’s appetite for risk has changed as education institutions seek solutions that provide the most up to date capability, for the maximum number of use cases, whilst also providing enough flexibility to adapt to increasingly frequent business changes.