What does good data governance look like – and how do we build it?

As we emerge from the pandemic, for many businesses the biggest concern isn’t being too bold – it’s being too cautious.

Business leaders are looking to accelerate transformation and deliver ambitious new services that are invariably delivered through technology. IT leaders are in the hot seat, and that’s a worry if you’re not 100% confident in your data.

Can you guarantee that data quality meets requirements? Do you have the systems and skills to integrate data from multiple platforms, silos and applications? Can you track where data comes from, and how it is processed at each stage of the journey?

If not, you’ve got a data governance problem.  

Without strong, high-quality governance, organisations are at the mercy of inaccurate, insufficient and out-of-date information. That puts you at risk of making poor decisions that lead to lost business opportunities, reputational damage and reduced profits – and that’s just for starters.

What does high-quality data governance look like?

It’s likely that the IT department will own data governance, but the strategy must be mapped to wider business goals and priorities.

As a rough guideline, here are 10 key things that we think must be a part of an effective data governance strategy:

  1.     Data security/privacy: do we have the right measures in place to secure data assets?
  2.     Compliance: are we meeting industry and statutory requirements in areas such as storage, audit, data lineage and non-repudiation.
  3.     Data quality: do we have a system in place to identify data that is poor quality, such as missing data points, incorrect values or out-of-date information? Is such information corrected efficiently, to maintain trust in our data? 
  4.     Master/Reference data management: If I look at data in different systems, do I see different answers?
  5.     Readiness for AI/automation: If we are using machine learning or AI, do I know why decisions are being made (in line with regulations around AI/ML)
  6.     Data access/discovery: Are we making it easier for people to find and reuse data? Can data consumers query data catalogues to find information, or do we need to find ways to make this easier?
  7.     Data management: Do we have a clear overview of the data assets we have? This might require the creation of data dictionaries and schema that allow for consistent naming of data items and versioning.
  8.     Data strategy: What business and transformation strategy does our data support? How does this impact the sort of decisions we make?
  9.     Do we need to create an operating model so the business can manage – and gain value from – this data?

Moving from data policy to data governance

As we can see, data governance is about more than simply having an IT policy that covers the collection, storage and retention of data. Effective, high-level data governance needs to ensure that data is supporting the broader business strategy and can be accessed and relied upon to support timely and accurate decision-making.

So how do IT leaders start to move away from the first view of governance to the latter? `

While it can be tempting for organisations to buy an off-the-shelf solution for data governance, it’s unlikely to meet your needs, and may not align with your strategic goals.

Understanding your strategy first means the business can partner with IT to identify the architecture changes that might be needed, and then identify solutions that will meet these needs.

Understanding Lean Data Governance

Here at Equal Experts, we advocate taking a lean approach to data governance – identify what you are trying to achieve and implement the measures needed to meet them.

The truth is that a large proportion of the concerns raised above can be met by following good practices when constructing and operating data architectures. You’ll find more information about best practices in our Data Pipeline and Secure Delivery playbooks.

The quality of data governance can be improved by applying these practices. For example:

  • It’s possible to address data security concerns using proven approaches such as careful environment provisioning, role-based access control and access monitoring.
  • Many data quality issues can be resolved by implementing the correct measures in data pipelines, such as incorporating observability so that you can see if changes happen in data flows, and pragmatically applying master and reference data so that there is consistency in data outputs.
  • To improve data access and overcome data silos, organisations should construct data pipelines with an architecture that supports wider access.
  • Compliance issues are often related to data access and security, or data retention. Good implementation in these areas makes achieving compliance much more straightforward.

The field of data governance is inherently complex, but I hope through this article you’ve been able to glean insights and understand some of the core tenets driving our approach.

These insights and much more are in our Data Pipeline and Secure Delivery playbooks. And, of course, we are keen to hear what you think Data Governance means to your organisation. So please feel free to get in touch with your questions, comments or additions on the form below.