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Top five data governance practices every organisation should follow

According to research, 45% of IT leaders say that data quality is the biggest detractor from Return on Investment (ROI) in their data governance efforts.

While they recognise its importance, they are struggling to improve the quality of their data, and thus the ability to leverage that data in practice. A core part of becoming a ‘data-driven’ organisation is setting in place robust data governance. Here are five first steps you can take to ensure that your organisation’s data is being created, collected, stored, and accessed by the right people for the right purpose.

Define your capabilities

First, if your organisation has not been through this initiative before, you may find that you do not have sufficient in-house experience with data governance best practices. In that case, bringing in outside experts who have helped other organisations successfully implement data governance is usually money well spent. This outside experience from engaging experts can help you build successful business cases and guide your initiative using processes and techniques that have resulted in sustainable data governance programmes elsewhere.

Use a blueprint for successful scale-up

The next step is to take advantage of generalised frameworks that already have data governance best practices built in and create your path to a scalable operating model. Your path to a scalable operating model is to blend a data governance framework with an understanding of the needs of your organisation, knowing that the framework will change over time. New requirements, risks and opportunities will arise around the data, so the governance needs to be as flexible as it is comprehensive. With modern tools, IT teams can create new framework pieces and relate them to the rest of the framework. The new pieces become an integral piece of the framework from that point forward. It’s an important capability that should be part of your data governance effort.

Start small but think big

Data governance adapts to two main drivers: everything that is going on inside the business, and everything that is going on with your data and the technology managing that data. That is because, if changes in both of those drivers are not reflected in data governance, the effort will not be successful.

From the start, the goal is to determine where the results will have the biggest impact and to articulate those results. The system put in place to generate results must be comprehensive, scalable to meet the needs of the organisation and adaptable to new requirements.

Most organisations start by looking at where data is ingested for analytics and business intelligence. It makes a good starting point because it has natural boundaries that make it easy to scale up. It also has natural links to the systems that produce data and feed it to the data warehouse. I would recommend considering the scenario of examining the data, then classifying it as personally identifiable or sensitive.

Although the priorities and drivers will be different for every organisation, eventually the effort must have enough impact to be measurable as a proof of concept (POC) thus the results must be consumable and deliverable in a reasonable period.

Additionally, the POC plays a role in building the larger business case as you set about getting stakeholders on board with your data governance effort. The role of the POC is as simple as proving the impact and value of data governance. The POC will feed the business case by pointing out where you are in reducing data discovery time — the start, stop and rework involved with data quality issues.

We also always need to remember that one of the biggest things people currently want from their data is the ability to explain it: where it came from, what it measures, and why it is important to a current business case. This will help to build a data-driven culture.

Set definitions and define controls

One of the main goals of data governance is to increase everybody’s data literacy, and thereby increase strategic usage. A standard set of definitions provides a vocabulary for data governance that ensures everybody uses and understands the same terms in the same way. That extends to having a standard set of definitions across the diverse types of data within your organisation, which goes directly to data literacy. Solid definitions and a mutual understanding of those definitions have an accelerating effect on data governance, so take advantage of standard, battle-tested ontologies and taxonomies for different lines of business-like finance, marketing, healthcare, and insurance.

The role of stakeholders

Finally, the data governance initiative should have a steering committee with a wide range of stakeholders whose interests go well beyond data.

Once the stakeholders are identified, the next step is to educate them on how data governance will benefit the organisation – by setting their expectations, clearly defining how they will contribute and explaining their responsibilities in the effort. Most of all, they need to understand the benefits of data governance.

Importantly, continuous communication with stakeholders is essential to build and set up communities in which people can build less-formal, ad hoc relationships around data governance. Giving them success stories and sharing updates about what has been achieved because of following data governance best practices will accelerate the data-driven culture uptake.

The best success stories clearly show how data governance best practices make day-to-day operations better than they were before. At the heart of a successful data governance effort is the establishment and maintenance of the perception of being a valuable undertaking.

Data governance has evolved from the era when organisations built all their own systems and ran them in house. As more data comes from outside, companies have far less control over its creation. As a result, data governance can help untangle and organise the data coming in from various sources. By implementing these data governance best practices, you can take better advantage of your data and enable your teams for success.

Picture of Danny Sandwell
Danny Sandwell
Data Analyst at Quest

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