Data governance should not feel like jumping through hoops


It is a sad reality that, for many businesses, data governance is not well-received.

Most commonly, this is because governance programs are far too often driven by supporting functions – like compliance or IT – with a focus on the needs of these supporting functions. The result, for many stakeholders, is that they feel like they are being given a lot of new tasks to fit into their week, without any real benefit.

Image from pxfuels

Data governance is not about data governance

As someone that has done this more than once, in more than one company, I am very clear that any approach that implements data governance without clear uses cases that support multiple stakeholders will struggle.

This does not mean that governance should not start small – it should.

By selecting one use case – where you can deliver value and where you have support – you increase your chances of success as long as:

  1. Your governance program directly includes stakeholders that get benefit about the use case selected.
  2. You do not force work upon people for whom the use case is not value adding
  3. You agree a roadmap and plan for including additional use cases to support additional stakeholders and keep these additional stakeholders included in progress via your communications plan.

Why is this important? Data governance is typically about formalising ad hoc information gathering and decision making processes (about data) so that they become repeatable.

If we engage too many people too early then many will lose interest – because they have nothing to contribute to the information gathering or decision making for the use case in focus, or because they cannot see how this will be applied to support them.

The right level of communication engages different stakeholders at different levels of detail, managing expectations while expecting more effort from those that are getting the immediate benefit.

What are potential use cases

The right use case will vary from company to company and could, for example, be one of these:

  1. Delivering better insights more quickly

Decision support remains one of the most visible applications of data. Yet, for many managers, analytics takes too long, and, when reports are delivered the results are treated with suspicion.

Data governance use cases that can be explored to improve the delivery of insights could include:

  • Improving access to data by cataloguing and publishing datasets, identifying owners and approval processes for data access requests, and delivering fine-grained access control policies.
  • Improving trust in data by making data quality visible, adding context such as definitions for calculations used, and confirming the data lineage.

2. Operational efficiency

Almost any business process generates data. Inaccurate or incomplete data at the first point of capture has a huge impact on downstream business processes that require the missing data, as discussed in the 1:10:100 rule. Fixing data captured incorrectly costs around 10 times what it would have cost to capture correctly the first time, while leaving bad data can cause errors that may cost a hundred times the original cost of capture.

Use cases that can be explored to increase operational efficiency typically focus on data integrity. For example, agreeing on data capture standards and policies, defining data quality rules, applying data quality metrics, and managing data quality remediation issues. These use cases are explored in a recent Precisely webinar: Fueling Enterprise Data Governance with Data Quality

View the Precisely webinar on-demand

Data governance also helps to improve IT efficiency. By ensuring that vital data specifications are available and accessible for impact analysis , data governance practises support agile development methodologies like Dev- and- DataOps. Key data governance use cases here include the delivery of quality metadata, processes to ensure collaboration across cross-functional teams, and managing requirements specifications and approvals to speed quality delivery.

3. Managing data risk

Data risk largely can be separated into reporting risk – which comes down again to the delivery of trusted data in regulated industries – and data privacy risks.

PoPIA implementation requires a series of data management capabilities and processes to be implemented with governance oversights. These can offer a range of possible use cases to kick start data governance.

Data governance is not new

In most organisations, decisions are regularly made to support data projects.

We document and sign off report specifications, we agree to allow a user to access a reporting data set, we ask executives to take responsibility for data breaches, or to sign off financial reports as accurate.

These are all stewardship activities buried into business as usual operations. Data governance as a discipline should be about reducing the overheads associated with these tasks through collaboration and reuse.

We should, at the end, require fewer hoops.

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