A common problem for data governance is that the people attending Data Governance working groups are not the people that can make a difference in data quality.

We have seen this repeatedly – the data governance program was launched with an overly theoretical approach and the business could not see the value.

When data stewards are requested junior staff are “volunteered” as the nominal business representatives for what is seen as a bureaucratic waste of time.

Over the last several months we have had the privilege of assisting to turn this around with one of our clients.

Six simple steps

The steps we suggested were simple.

1. Cut the numbers.

Data Governance councils should be decision-making bodies – discussions and debates should be left to working committees.

2. Cut the overheads

Requesting too much information will create a perception of red tape and hassle that people will avoid on principle. Rather identify and document the critical data elements required to get going, and then build from this.

3. Simplify user documentation

Make sure any documentation you require is easy to understand and complete quickly – without losing sight of the fact that it must be meaningful and add value

4. Pilot the process

Get continuous feedback from a small set of users and improve your process and documentation before rolling your structure or processes out to the larger community. Feedback may be as simple as changing the format of a report, or adding or removing a small number of fields, but can have a massive impact on the perceived value and ease of use of the process.

5. Remember that there is no silver bullet.

Your organisation is unique. The detail will depend on the level of buy-in in your organisation – and that in turn is dependent on your level of maturity.

6. You know you are doing something right when people choose to join you!

We now have busy people volunteering to join our committees because they are seeing value.

The Data Governance community is growing through word of mouth – with meaningful input coming from the right level of person.

This, in turn, drives additional value – for example, previously numerous tactical data cleansing projects were running across the business with no visibility to each other, with duplicated effort or, in the worst case, contradictory goals.

Now the Data Governance community has an enterprise view that enables these projects to be coordinated.

Conclusion

A lean approach to data governance focuses on cutting inefficiencies. Incorporating data quality into data governance goals helps to make data governance practical, again showing value and increasing support.