When was the last time you did a spring clean on your laptop? We all get frustrated when we start running out of space and can be guilty of deleting files haphazardly without looking at what data is actually causing the problem.
While you might be able to sustain this approach on your personal devices, imagine the chaos it would cause in a much bigger environment.
Higher education institutions are a prime example – they’re now dealing with a much a higher rate of internet-connected devices on campus with BYOD, resulting in a deluge of data that their current methods of coping can’t handle.
Higher education institutions have data flooding in from all directions via online applications, software-based and online classroom exercises and testing, social media, blogs, and student surveys.
There is also a surge from public data – with online benchmarking of students, professors and curriculum performance becoming increasingly popular. All of this data is placing strain on existing IT infrastructures, causing CIOs to demand new architecture and CEOs to ask why.
Established software solutions, which have been around since the 70s, are not engineered to cope with the data demands. That’s why many higher education institutions have yet to harness big data – they lack the tools to take advantage of it and this problem will only get worse as data volumes continue to mount.
Institutions that fail to embrace big data as an opportunity will to be left behind by those that do as the power of data-informed strategic decisions fuels them forward in the league tables.
So how can high education institutions stop fire-fighting and get to the point where they can actually apply big data insights?
1. Start small
You need to find the areas in your operations that are the most data-intensive – such as applications or grading – and identify one problem that can be easily solved with analytics. The momentum from a quick victory will allow you to address more problems throughout the institution.
2. Identifying the desired outcome
To be successful on your analytics journey, you should begin with the end in mind. Are you aiming to improve student relationships? Improve recruitment? Find new sources of revenue for your institution?
Before you invest time and money into a big data analytics initiative, first determine your desired outcome, then build your strategy and data architecture to help you meet your goal.
3. Foster a data culture
For too long, higher education institutions haven’t had the resources to effectively capture and store the data, much less analyse it. With the advent of next-generation technologies like Hadoop, this is no longer an issue.
These days, it may be more expensive to discard data than it is to keep it. It’s imperative to create a culture that respects data and makes data analytics an integral part of every business decision.
4. Find good partners
Education technology is evolving at a pace never seen before but there are few organisations that have the internal resources on hand to create and operate effective big data analytics workflows.
Find a partner that is dedicated to enabling you to be successful in every stage of your analytics journey. This partner will help you embrace your current investments and work with you to build processes that won’t require hard-to-find specialised skillsets, which run the risk of becoming outmoded in a manner of months.
By trying to use yesterday’s technology and methods for today’s data opportunities, higher education institutions are struggling to take advantage of insights needed to make impactful decisions and push them ahead of their competitors.
By using new tools and applying their subsequent learnings, universities and colleges can materially improve their businesses processes and attract the young talent that will set them apart.
Don’t look at big data as a problem but seek to use it to your advantage by changing your approach.
Sourced from John Bailey, Dell UK