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Irrespective of the industry that your organisation is a part of, it is inevitable that you would need relevant and good-quality data to run even the most basic processes. According to McKinsey’s 2019 Global data transformation survey, an average of 30% of the respondents’ enterprise time was spent on non-value-added tasks, thanks to poor data quality and availability – an effective solution to this predicament is to have a good data governance programme in place.

Good governance not only helps organisations to eliminate costly overheads associated with managing their data ecosystems but also enables them to implement digital and analytics programmes that help generate significant competitive advantage. However, to reap these benefits it is important to understand the stage that you are at in your data governance journey, where you would like to go from there and what are the next steps to be taken.

In this blog post, we will delve into the three different stages of the data governance journey and discuss the next steps that could be taken to get the most out of data governance.


Data governance and its importance

Before we get started with the different stages of the data governance journey, let us quickly take a look at why data governance is important for your organisation.

While GDPR and other regulatory requirements do play an important role in the growing adoption of data governance, it is not the only driving force behind it. With each passing day, organisations are realising the importance of implementing effective data governance programmes, and this is clearly demonstrated by the fact that 98% of the respondents of our 2022 data governance survey had an ongoing data governance programme. So it is safe to say that the availability, usability, integrity and security of data has become indispensable for those looking to productively unlock business intelligence from their data sets.

It is helpful to note that while building an effective foundation for data governance, a one-size-fits-all approach doesn’t work and it is vital to have a programme that is well-suited to your business needs. Every organisation has a different reason behind pursuing good data governance. While for one, it might be a matter of improving compliance standards, for another, it might be to implement a ground-up strategy that would support new data-driven initiatives focused on delivering measurable business benefits.

Combining these objectives with the level of experience and the resources at your disposal will help understand the stage at which you are on in your data governance journey.


The three stages of the data governance journey

The journey of data governance is one that never ends, as it’s a continuous process that exists as long as an organisation operates. As with undertaking any journey, the journey toward data governance requires proper planning, implementation and iteration. Depending on your business needs, level of experience and resources, you could be at any of these three stages.

1. Foundation stage

An organisation that is at this stage could have already made the decision to begin a data governance programme but might not have moved further. Although you are just getting started/restarted in the data governance game, an advantage of being at this stage is that there is an opportunity to build on the existing learnings from the other players in the landscape. This step is all about planning and setting the stage for the implementation of the data governance strategy.

The next steps would involve asking the following relevant questions to help set up an effective data governance strategy:

    1. Why are you focusing on data governance?
      Be specific about the challenges and pain points that you can address while prioritising the elements that will drive the greatest impact and help the organisation achieve its strategic goals and objectives.
    2. How will the data strategy align with other strategic initiatives and priorities?
      Outline how key stakeholders can benefit and the value it can provide to win support throughout the organisation.
    3. When and how will the success be measured?
      Determine the key performance indicators that can be used to track progress, outcomes and measure the impact on the bottom line. Consider a phased approach by prioritising projects that will enable quick wins, provide clear measurement and an opportunity for regular impact review.
    4. What are the budget and resource considerations for implementing the data strategy?
      Identify the gaps in your data assets, capabilities and resources between your current and desired state. Evaluate the required processes, procedures and policies that can help you get there.
2. Implementation stage

Organisations at this stage have already laid a foundation and have the necessary tools in place. However, they might be facing the challenge of finding the right people and processes to support the implementation. While technology does indeed play a key role in data governance, it is not a substitute for the leadership and guidance delivered by experts.

To move forward, organisations could either develop existing talent through training and reskilling or acquire new talent through recruitment or partnerships. However the recent skills gap in the data landscape makes it difficult for organisations to find the right people to manage data solutions. While automation and outsourcing could be an answer for this, leveraging managed data governance solutions could be the best way forward.

Once you have the right people in place, you need the right processes. An effective way to develop processes is by working with organisations that have previous experience delivering a data governance programme of work. This is to mitigate the risk of teams working with misaligned objectives and getting bogged down with understanding how to approach data governance, rather than actually delivering it.

Something to keep in mind while dealing with people and processes is to ensure stakeholder management. Simply put, this means that organisations have to ensure that the correct people remain engaged at every stage for the project to succeed. Good data governance processes must guide where and when they will be needed.


3. Continuous improvement stage

Organisations at this stage have ticked all the boxes in relation to issues like data classification. However, the stakeholders are looking for results that support better business outcomes. How do we get there?

The first step is to begin by defining what value means for your organisation. Only 1/3 of respondents of our 2022 market research study felt that that they get any value from their data governance programmes. This is mainly because organisations fail to define and understand what represents ‘value’ from the outset of the programme.

To define value, let’s start by understanding that data governance serves two main purposes; cost avoidance and enabling value creation (we cover this extensively in the data governance 101). Most organisational leaders are usually focused on driving value from their investments and rightfully so. For this to happen, it is important to understand what to evaluate.

The three key areas that businesses could measure to depict that impact of a data governance programme are listed below. By grouping the impact into the following, data leaders will be able to clearly articulate and define the value and business outcomes delivered.

  1. Compliance: Has the data governance programme affected the compliance performance and has it improved compared to legacy processes?
  2. Productivity: Has the data governance strategy driven measurable improvements in the time required to discover, classify and act on key data sets?
  3. Finance: Has the data governance strategy driven identifiable and recordable improvements in financial performance?


Final thoughts

Data governance, when implemented strategically, can deliver significant impact by enabling leaders to act with new levels of insight and confidence. By empowering organisations to make major improvements across a wide range of critical and performance issues such as data integrity, compliance, decision-making and bottom line growth, effective data governance could transform business operations, but only if done right.

Through the course of this blogpost, we have discussed the various steps that could be taken to get the most out of your data governance strategy, irrespective of the stage you are at. However, not all organisations might have the time or resources to invest in training their internal team. In such a situation, it would be helpful to outsource the implementation and management of your data governance programme to experts who have been there and done that.


Interested in getting started with a governance strategy made-to-fit for you? Our team of experts stand ready to assist:


Already have a data governance strategy in place but need assistance operationalising it at your organisation? Our flagship offering might be what you have been looking for:

Amritha Nampalat

Driven by her belief in the power of storytelling, Amritha endeavors to create content that not only captivates but also drives meaningful results. With each piece of content, she strives to seamlessly merge compelling narratives with insightful data analysis.

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