EdTech: Getting ready for the new academic year: How best to approach data cleansing
As we approach another, but very different looking, academic year, educational institutions will be thinking about how best to manage and tidy up their student data, in order to ensure they are in line with data management policies and stay compliant with GDPR regulations. With students and teachers working remotely for a considerable portion of 2020, cleaning up that data could be quite a hefty task.
Importance of data cleansing
Organisations and education institutions of all sizes need to remove irrelevant or unnecessary data to ensure compliance with GDPR legislation. Clearly adhering to national and international statutory regulations is imperative, including international education providers, so data protection is maintained as a priority and to ensure an efficient data governance strategy.
Who needs access to that data, and why? Who will be responsible for the upkeep of the data and how often will the process be reviewed? Who liaises with your suppliers and ensures we are complying with GDPR and other sector regulations when it comes to data cleansing and management?
Having an inouse training program to remind existing employees and new staff members of the importance of data handling and management and the importance of understanding how detrimental poor practices can keep into the rest of the institution and harm a reputation, is crucil. Training is ongoing an dnow with cyber threats becoming more and more prevalent, education of cyberawareness and how to spot phishing emails and malware and the damage that could be done if an attack occurred and the data held was leaked, or if the university was held to ransom for safe return of the data, this is all more necessary now, than ever.
These are some examples that often produce an understanding that we hold different types or sets of data, and these different types of data may have different associated values to the institution.
Operationally speaking, data cleansing should focus on delivering effective performance preferably through a formal process that is standards based. Incorporating ISO 9001 and 8000 framework models and other frameworks provides assurances for those who are parting with their data. It is important to remember data requires curation; Data ages, and it can degrade in quality. At an individual level for example, I may change address, I may change my name. It serves no purpose for academic institutions to hold historic data, and this data needs to be kept up to date to meet expectations. It is also crucial for safeguarding purposes that all records are up to date so that contact information is correct in case of an emergency.
Retaining old data is not only inefficient, it is also costly and can slow down processes creating inefficiencies. Not only is aged and inaccurate data irritating and harder to mine for the urposein hand, It can also cause potential offense or even a potential risk to an individual.
At a technology level, storage devices containing data are costly to maintain and pose a security risk for any organisation as they’re open to cyberattack if hosted in the cloud or if stored on a local desktop that is not secured. As ever, technology security is paramount, and organisations and institutions are only as secure as the people on the network allow. It is also important to be knowledgeable about the data preferences of the individual and how they wish to be contacts. Using technology to help identify these preferences and opt in and outs is an easy way to maintain data effectively.
Sometimes data can get corrupted (such as a hard disk failure). Human error is also possible; when working with the data in some form of administration role or activity overwrites and corrections to data with inaccuracies can occur. Data is increasingly valuable to drive operational efficiencies, maintain good customer relationships and to build insights that can extend and enhance the relationship of the organisation with the parent, student, colleague, end user or customer in any given environment.
How to clean up data
Educational institutions should build a data governance strategy that considers people, processes, and technology. There are many products on the market, and it is important to make any selection based on an educational institution’s specific requirements, guidelines that need to be adhered to and these can vary from institution to institution. What data are we looking to clean? Where is it stored? How often will it need to be reviewed?
Exploring each data set, we may ask what challenges are we facing with our data . For example, does it age quickly? Is it typically incomplete? Do we hold significant volumes of duplicate data? How much do we spend on management and upkeep of data? Do we need to hold the data for a specific or minimum period of time who is responsible for auditing the data)? As we define, document, and build processes for the data sets, again our organisations quality management systems and recognised frameworks (such as ISO8000) can assist here. This approach can be used to define not only the functional requirements of any tools, systems and solutions to best allow education institutions to clean their data but also the implementation and conformance model to be adopted and configured for each tool
When carrying out a data cleanse, review all three points of the triangle, starting with the people, then the processes and then technology that makes up your business. People are crucial to all organisations and if they are aware of how to use the technology and WHY, then a better outcome is always more likely.
At Nowcomm we use this holistic approach for the design and delivery of any system, service or project. Agreeing formalised best practice models, adopt standards and complying to legislation, or use sections of standard based models.
We recommend all your employees, end users and even suppliers where necessary understand and are committed to your institution’s own operational framework and standard model and that businesses regularly review these models – especially if looking to scale or merge with another institution – and look for opportunities to further improve and enhance existing systems.