The dos and don'ts of implementing a Data Governance Strategy

For organisations to have reliable, understandable and easy to find data, they need to manage their data effectively. Therefore, a data governance strategy that sets a standard in how data should be handled within the organisation should be an essential part of every business. A crucial objective of implementing a data governance strategy should be to align the benefits of such a strategy with the business objectives of senior stakeholders. Establishing this sponsorship early on allows for a clear and concise approach to be communicated across the business.

When explaining the benefits of data governance, ensure the response consists of two outlooks. The first being that data governance is a necessary function to help businesses comply with external regulations by confirming that data is being effectively managed to a given standard. The second, which is often overlooked, is that it is fundamental in allowing organisations to make more reliable decisions.

Any data governance capability should be based on the objective of increasing the value of the data that an organisation owns. This focuses the function of data governance to align with the business.

Once sponsorship has been obtained, you can start to build out the strategy and focus on areas that will be most beneficial. As part of my Business Data Analyst development programme, I have considered various elements of data governance at different points of the data lifecycle. For me, some of the most applicable and essential points for organisations to implement are:

Data Quality: A data quality programme focuses on measuring the current quality of data within an organisation and considers ways of correcting errors to enhance the value of the data. This has a huge amount of value-add for an organisation and will enable users to trust the data, which in-turn leads to an increase in data-driven insights within a company.

Roles and Responsibilities: To ensure that a governance framework is effective, there must be clear ownership within an organisation. This ownership will provide the level of responsibility required to ensure data issues are resolved quickly and effectively.

Governance Tools: In my opinion, integrating a data governance tool such as Collibra to visualise data lineage and simplify workflows, which help to resolve data issues, should be considered for all data governance frameworks, as they ensure business users get the most benefit from a governance initiative.

When implementing a data governance strategy, it is important to remember there are several common mistakes that can be more of a hindrance than a help, these are the don’ts. The following should always be remembered:

Stakeholder buy-in: Like most business initiatives, data governance needs to be implemented from the top-down. Without a senior sponsor, processes will not be adhered to and tools will not be effectively adopted by the business.

Business orientation: Whenever a decision is made, you must ask the question “How is this valuable?” Without considering this, you may be building a framework that will never get implemented by the business. If there is a complicated answer, consider how to break it down and explain it to various stakeholders before making the decision.

Treat data governance as a project: To ensure data is effectively understood, trusted and leveraged within an organisation, data governance should be a continuous and well-established process. A structured approach to how a governance framework should be embedded needs to be implemented to allow for continuous ownership as new data enters the organisation.

In addition to avoiding these common mistakes, organisations should further consider the challenges around data governance engagement across the business. Two common challenges are:

Embedding responsibility: If there is no stewardship currently in place, it will take a while to engage with the relevant stakeholders to ensure that data stewards and owners are established. Once put in place, measuring how they are responsible for their data quality is the next step to promoting a healthy data culture. All of this requires senior stakeholder engagement to help effective implementation.

Resource allocation: Ultimately, organisations will need to set aside resource to allow for these changes to be made. Often governance programmes are established within pre-existing IT departments which have their own data initiatives to be executed, leading to governance priorities not being actioned. Ideally, a data governance team should separate from such functions with executive buy-in. This will ensure that people are dedicated to making changes and that they have authority to make the decisions required.

On average, data professionals only spend around 40% of their time analysing data. Instead, a vast majority of their time is spent cleansing, preparing and processing data prior to building the insights that lead to improved decision-making. To increase efficiency, it is therefore important to consider how best to govern data to help enable data professionals to find, trust and understand the data that they are working with.

By implementing an effective data governance programme, you will be making your data more fit for purpose and will provide data owners the clarity that they need to effectively do their job. If your organisation values your data as a strategic asset and hasn’t yet implemented data governance, I suggest you consider what value you could create by having more accurate, better-suited data and how that will allow you to further help your customers.

by Tom White

Posted on August 23, 2019