Choosing the Right Tools for Your Data Governance Strategy: Challenges to Look Out for and How to Overcome Them
According to the analysts at Gartner, in 2023, 65% of the world’s population will be covered by laws similar to GDPR. Combined with the explosive growth of data, this ultimately points to one inevitability – that data governance is here to stay and those at the forefront are likely to become the industry leaders.
The value of data governance has evolved over the last couple of decades to become an absolute necessity for organisations in today’s competitive environment. However, the implementation of data governance is not a walk in the park. Organisations attempting to implement an effective data governance strategy face several challenges, one of which is choosing the right tools for their strategy. In our 2022 Data Governance Survey, 37% of the respondents cited the lack of sufficient tools as a barrier that prevents them from delivering value from their governance programmes.
That brings us to the question: how do you choose the right tools for your data governance strategy? This blog post discusses how spending the time to develop your requirements can help overcome the three key challenges that organisations face while choosing tools for their data governance strategy.
Define your requirements
One of the biggest pitfalls that organisations could fall into is not exploring their objective in enough detail to formulate a complete set of requirements. You may have identified an objective as “cataloguing the data” or “managing retention policies,” but to efficiently select the most suitable tool, it’s essential to delve deeper into the details of what you need from this new capability you are investing in. This ensures you find a solution that not only fulfils your needs comprehensively but also avoids unnecessary expenditure on features that exceed your necessities.
Three challenges when looking for the right tool
Sadly, there is no single one-size-fits-all tool or application that is likely to meet all the needs of your data governance strategy, and you will be looking at a selection of tools for different jobs. Just as your data governance strategy is tailored to meet the specific objectives of your organisation, the different tools tend to be tailored to delivering on specific areas of the data governance landscape.
Amongst the myriad of data governance tools that are available on the market, it is not easy to choose the ones that will help derive the most value. The three key challenges that you could anticipate while choosing the right tools for your data governance strategy are:
1. Ambiguous terminology in the marketplace: The data governance landscape includes a lot of terminology with fairly flexible definitions. When someone talks about Data Lineage, for example, are they talking about the flow of data between systems or the mapping of a data asset to defined business terminology? When someone talks about Data Catalogue, does this include Business Glossary or do they consider that to be something different? This makes it hard to compare tools against your needs as you may be talking about different things.
Furthermore, there are different levels of maturity in the services different tools provide. A Data Catalogue tool might include data lineage as a feature, but the scope of this is not likely to compare favourably to a specialist data lineage tool, which will likely capture more detail and provide richer features around data lineage. Do you know which you need?
A clear set of requirements should include details of what each feature should be capable of. You can then use this to properly assess any given tool to see how close it comes to meeting those requirements and therefore, how suited it is to your needs.
2. Limited interoperability: The implementation of your data governance strategy is likely to involve the deployment of multiple tools which serve different, complimentary purposes to fill out your understanding and management of your data. There are limitless opportunities to enrich one tool by bringing in the information or combine the information in multiple tools to do something new. Therefore, in an ideal world, all these tools would seamlessly interact with each other out of the box, but in reality, most of the tools in the market do not. There is a lot of activity in this area, but there is so much variety and volatility that the coverage remains relatively low.
Tools invariably provide an open API to solve this problem for you, but something has to use the API. Some integrations have been implemented between pairs of friendly vendors, but these are relatively few. Where the vendors have not created an integration themselves, all is not lost, as you can create your own. So, on the one hand the ability to integrate exists, unfortunately, it is often incumbent upon you to actually code or otherwise create the integration.
Where you identify an opportunity or need to integrate two tools using a bespoke integration, this introduces two challenges:
- You need to decide what you want to do – the possibilities are endless, so it requires some thought to understand what would be useful. You are free from the restrictions of how a vendor might have chosen to implement an integration, but equally you are somewhat spoilt for choice.
- You need the skills and/or tools in order to implement the integrations. This could be code, but you may also have existing tools which allow you to build workflows leveraging APIs.
Beyond integrating between data governance tools, many of the tools are also reaching into your data assets to capture information. Due to the breadth of existing technologies for storing data, the tool vendors are continually developing new connectors to bring more data sources under their umbrella. Is the tool you are looking at able to connect to all the data sources you need it to? The last thing you want is to buy a tool and then discover that although it works with every new cloud-based data warehouse under the sun, it doesn’t work with the slightly niche, older yet critical business system you run.
Since interoperability is a crucial factor, our advice would be to consider what sort of integrations you might need to meet your objective and record those as requirements, so they are addressed during the project – whether via selecting a tool that provides the integration natively or by delivering in-house.
3. Lack of expertise: The challenge doesn’t end once the tools have been acquired. The next step is to operationalise the software and ensure it is delivering the desired outcomes – this is something that the vendors cannot necessarily assist with, as it relies on people, processes, and an understanding outside of just the tool itself. Many software vendors do not have significant professional services departments you can lean on for this, although customer training is invariably available.
Expertise in installing and running and later configuring and using the tool are obviously important, but also people able to understand what to do with the tool in order to deliver the value you are after. It’s great to know how to drive the car, but you also need someone to provide the map of where you should go. We have seen many instances where an organisation chooses the right tool for a data initiative but fails to realise the expected results due to a lack the expertise and experience required to get it off the ground.
Where to start?
This brings us back to where we started; considering all these aspects early in the process to create a thorough set of requirements will help you address these challenges. But perhaps this is easier said than done – how do you work out what it is you want or need from a tool at a detailed level?
We recommend considering whatever you are doing as a service you are introducing as opposed to a technology project you are delivering. From there, you can explore who it is for and what they will want (i.e., customer requirements) and then who will use it and what they will need to be able to do (i.e., user requirements). This user-centric approach helps to build the requirements needed to navigate the many options and possibilities and help you drill down to the detail around what you need in order to achieve the objective. If you have taken the time to think about who it’s for and what those people need to gain from it, then you can immediately start to qualify a tool in or out. You can assess them not just in general but specifically against what you want and need.
Knowledge is key
Choosing the right tools is a critical step in any organisation’s journey towards building out an effective data governance programme. A clear understanding of your objectives, covering who will use the tool and for what, leads to a detailed set of requirements covering what you need in terms of functionality, integration and internal expertise. This in turn, sets you up to select the right tool and make the most of its abilities and deliver the value you are looking for.
However, you still need to take your requirements to potential vendors and go through the process of checking their product against those requirements. This can be time-consuming and delay-inducing. A good idea would be to consider bringing your requirements to an independent specialist who already has an appreciation of the tools available and can fast-track much of the process for you and suggest a tool based on what you are asking for. If that sounds like it could help, perhaps you could give us a call and our team of experts will be happy to assist you.
Looking for more information to streamline your data governance journey? These resources could help:
- Blog: Stages of Data Governance: Evaluation Where You Are and What to Do Next
- eBook: Data Governance 101 – A Leadership Guide
- Webinar: Operationalising Data Governance – Data Governance: The Undervalued Element of a Data Strategy
- Report: Research Report: The State of Data Governance 2022
Wondering how to operationalise data governance at your organisation? Our team of experts can help: