While it’s possibly a fool’s game to try and predict the future, there’s one aspect of it we can all safely agree on - the amount of data that we are all dealing with will exponentially grow, year on year.
Data is being generated, analysed, stored, and regulated in unprecedented ways. And while it’s the lifeblood of many organisations, the governance of such data is a serious challenge.
Bearing that in mind, the following are the seven key data governance challenges that organisations are facing.
1. Keeping data governance in sync
Capturing the changes that happen to data is a perennial issue for data governors. Typically, this occurs when governance runs separately to the data pipeline which leads to the data falling out of sync. An ideal system will capture daily or real-time data changes and thus keep the data governors up to date.
2. Quality and Compliance rules
The failure to tie data lineage, governance, and quality to Quality and Compliance rules lead to poor data governance outcomes. This often happens when there is not a standard approach or when data engineers are too busy and therefore are not applying lineage and/or quality checks to the data.
3. Data security and privacy
It’s essential that data is only seen by those within your organisation who have the permission to see it, and when data is being accessed for analytics, it is masked in such a way that the underlying data is not revealed. Knowing where the data came from, what happened to it while it was being processed and who interacted with it, is a vital governance issue.
4. Applying data governance in hybrid cloud
The benefits of moving to the cloud should not mean an interruption to your data governance. Before your migration, you should apply tagging, security, anonymisation, validation, and the transformation of that data to make it more transparent, better governed and cloud-ready. This means that your migration, from a data governance perspective, will allow you to seamlessly transition and benefit from the cloud immediately.
5. Data architecture – disparate systems
As most organisations use disparate systems that interact with data, this naturally creates more work for data governors as they grapple with the multiple systems. While many systems will always be in place, data governors need to use a single data governance and operations tool which is ‘system agnostic’ in order to ingest, tag, and perform masking, tokenisation and transformation, among other tasks.
6. Data self-service – data requests and reports
A big issue with reports is both the building of them and what data are people allowed to see. Instead of a data governor spending their time creating reports, an ideal system enables business analysts to prepare their own pipeline which are in line with governance procedures and then execute the report on their own by using self-service data sharing, intelligent data masking and governance approvals.
40.3% Percentage of Executives cite having a lack of organizational alignment as a challenge for data and analytics adoption. Site: Harvard Business Review
7. Unsure of data policies and procedures
A lack of communication within an organisation can lead a general lack of understanding of data policies and procedures. However, instead of relying on people to study such documentation, your system should have in-built rules which automatically match a user’s permission levels thus enforcing regulations such as GDPR. This way data is matched to end users, which makes governance projects more likely to succeed, be quicker to implement and be more manageable.
Bluemetrix’s BDM Control is the most comprehensive data and governance operations solution available. It will future proof your data while helping you track, capture and inform data governors about data changes as they happen.
To learn more about how an enterprise data and governance automation solution can help you solve the organisational challenges above and more, schedule a free demo with one of our product specialist at your convenience!
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