A Guide to selecting the right Data Governance Tooling

How can Data Governance Tooling help me? 

The Data Governance landscape is constantly changing. As business direction shifts, data technologies advance and new regulations come into force – governance has to change with them. Delivering Data Governance at scale and keeping up is a constant challenge.  

Having the right tooling and technology to make sure data is in the hands of the right people has never been more essential. So, it’s no wonder that Data Governance tooling like Alation, Atlan, Collibra, Informatica, and Purview (just to name a few) has never been so popular. We can bet that you have not only heard of these names, but also you are probably on every salesperson’s speed dial. So why is Data Governance tooling so popular, and is it the silver bullet to fixing all of your Data Governance challenges? 

Our guide will help you ask the right questions, whether you are currently in the selection process or need help with implementation.

Jeff Gilley
Head of Sales
The Oakland Group
Email: jeff.gilley@weareoakland.com

Data Governance tooling has gained popularity for several reasons. Many organisations consider it a plug-and-play solution to a wide range of data-related problems due to its comprehensive functionalities and the importance (not to mention volume) of data management to the enterprise.  

Here at Oakland, we have seen many of these tooling products sold as a solution that addresses EVERY Data and Analytics Governance need, which has led to confusion in the market. We believe tooling plays a huge part in delivering effective Data Governance, and there is the right option out there for the job, but there is work involved in finding it, integrating it, and getting value from it.  

Data Governance tooling is so popular because it can address numerous problems: 

Increased Data Volume and Complexity: 

The explosion of data from various sources has made it challenging for organisations to manage and utilise data effectively. Data Governance tooling helps manage large volumes of complex data efficiently. 

Regulatory and Standards Compliance: 

With stringent data security and privacy laws and standards like GDPR, CCPA, PCI, ISO27001, and others, organisations need to ensure compliance to avoid the ire of regulators and customers. Data Governance tooling provide features to help manage compliance requirements effectively. 

Data Quality Improvement: 

High-quality data is crucial for accurate analysis and decision-making. Data Governance tooling helps ensure data accuracy, completeness, and consistency, which improves the overall quality of data. 

Enhanced Data Security: 

Protecting sensitive data from breaches and unauthorised access is a major concern. Data Governance tooling offer robust security features to understand the access landscape for data and protect data. 

Operational Efficiency: (one of our favourites and the foundation Oakland was built on): 

Automating Data Governance processes reduces manual efforts and increases operational efficiency, allowing organisations to focus on strategic activities. 

Better Decision-Making: 

With reliable and well-governed data, organisations can make informed decisions, leading to better business outcomes. Data Governance tooling provides the necessary infrastructure to support data-driven decision-making. 

Good Data Governance is an essential component for organisational growth and resilience in an increasingly data-driven world. The challenge is how to support your organisation along the journey to better data maturity and effective governance.  

As you address your data issues and start your Data Governance journey, you will start to create resources that benefit your whole organisation, from glossaries to lineage models. Tooling helps create a structured way for these resources to be shared and evolved. You might want to use Governance tooling for some or all the following purposes. 

• Data Catalogue – defining available data. A data catalogue works alongside glossaries and dictionaries to create transparency around the available data across your organisation, what it means, and how to access it within your organisation. 
• Business Glossary – defining key terms. The business glossary communicates what data means using business language. It contains common business terms used throughout the organisation and their associated definition with additional fields, including synonyms, acronyms, and status. 
• Data Profiling – understanding the data within systems. Data profiling allows users to run analysis of the data in is current state, highlighting gaps, inconsistencies and other quality issues as well as giving more data about what the data means and how it is and could be used in your organisation. 
• Data Dictionary – defining what makes up data elements. It defines the data in technical terms providing a definition of data sets and associated fields, with descriptive fields including data type, size, value, purpose, and relationship with other data elements. 
• Data Lineage – understanding the journey data takes across an organisation. Data Lineage provides information around the lifecycle of your data. It provides a visual representation of each stage that data goes through, from creation through to presentation. 
• Documentation – policies, standards, rules, controls and classifications. The ability to document and apply standards, rules, controls and classifications against data enables quality and other metrics to be measured and monitored ensuring trust, transparency and understanding of your data is maintained.
• Data Quality Management – visualising the adherence of data in your enterprise to documented policies, standards, rules, controls, and classifications. The ability to show compliance with this agreed set of documents and to identify, manage, and remediate issues is a powerful feature of Data Governance tooling. This is sometimes considered a separate category to Data Governance tooling; however, for the purposes of this guide, we have included it. 

Focusing on the pains, difficulties, and issues you are currently facing with your data is the key part to understanding which tooling will provide you with the most value.  

Starting here also enables you to build use cases for tooling, which will support the piloting and testing phases of your later implementation.  

The most common pain points that organisations looking at Data Governance tooling to solve are set out below. 

Here at Oakland, we work with our clients to help them to select, implement, and optimise the right Data Governance tooling. We believe that the tooling you select should fulfil three key requirements:  

1. Address your organisation’s most important priority and highest level of risk 
2. Be easy to use by EVERYONE who needs to use it! 
3. Integrate with your existing data landscape 

The fundamental part of the selection process is clearly defining the ‘why’ you need Data Governance tooling. Tooling can be very expensive and need to be backed by a robust business case. We recommend you:  

1. Take your time to speak to the different solution providers (it’s easy to be seduced by those charismatic sales folk)  
2. Outline your vision – clearly identify what good looks like for you? 
3. Be clear on your brief – what are your expectations, what problems are you trying to solve?  

An essential part of the selection process should be how your vendor helps you ensure the end users of the tools are engaged in providing data and how they will interact with the tools. Your tooling needs to be easier to use than the alternatives, enrich and engage all stakeholders and users, and provide value. It needs to become a supportive resource, not a hindrance that everyone rolls their eyes at.  

Buyer beware!  

If you are just starting out on your Data Governance journey, tooling probably isn’t going to solve a lot of your problems. We see many organisations with low data maturity jumping straight into tooling. This can be very expensive, and without a better understanding of what you’re going to govern and how you’re planning to govern it, tooling is a red herring to actually making the change that is necessary in your organisation happen.  

For those organisations who have a clear view of how they will operate Data Governance, tooling can provide significant benefits of speed and scalability that will be needed to help drive governance adoption. 

Data Governance tools can help with this journey, but with so many different tooling providers, selecting the right one for your organisation can be difficult. 

When it comes to tools there is not one option that can fix everything. Data Governance tools aren’t the holy grail, so make your selection based on the pains the tooling solves. It sounds obvious but we see many organisations choosing tools which, 6 months down the line, aren’t delivering on the organisation’s requirements.  

Examples: 

Do you have pain points from a lack of understanding around your data? – Then look for a data catalogue with data dictionary capacities. Or is your issue around not knowing where your data comes from or its journey through your organisation? – Data lineage tooling would be more suited to this case. 

Ensuring the tooling connects to the systems, platforms, and databases that make up your data estate and that it can understand the data within them is fundamental for easy implementation.   

Do your homework to ensure the tooling you select has all the right connectors to enable easy integration and ingestion of data; otherwise, you might have to purchase or build additional connectors, and that is where costs start to mount.  

Consider who you are trying to empower with the toolset and the skills they have, and make sure the user interface is simple enough that everyone who needs to use it can! You don’t want to select tooling that requires every user to understand SQL when you anticipate that everyone in your business should be able to access it. And conversely you don’t want tooling with a restricted front end which means that power users can’t use it to the best of their capabilities to get at information. 

There is a lot of talk about the promise of Generative AI (GenAI) to revolutionise many parts of the business landscape in the coming years. We see significant potential for automation within existing processes and help in managing large volumes of data, high velocities of data, and identifying patterns in data. This shift brings with it a need for expanding Data Governance to include AI Data Governance. The good news is that most of the work you’ve done to put Data Governance in place will have good applicability for AI Data Governance. From a tooling perspective, most of the major players are touting their ability to do AI Data Governance or that they’ve incorporated GenAI into their toolsets. Don’t let these features be the sole reason you choose to go with a provider. As we have mentioned before, the core use cases you are focused on and your non-functional requirements should be driving selection, not a beta-release feature that is probably going to change several times before it demonstrates value. If you do have strong use cases for AI Data Governance that you need tooling to work with, we recommend the following: 

• Understand the data sources for these use cases. These are likely to be unstructured sources including text, video, pictures, and sounds, which may not be on your basic data governance landscape. 
• Understand how data will be used. How is the data going to be ingested and interpreted by the GenAI tools, and to what degree does the organisation need to attest to its validity? 
• Identify your policies, standards, and classifications. Without the necessary guiding documentation in place as to how your organisation will use data within AI, it will be difficult to govern. 

Ask yourself the following questions in order to work through the process of selecting tooling: 

• What use cases does the tooling needs to solve? 
• What systems it is critical for the tooling to connect to? 
• What systems it would be nice for the tooling to connect to? 
• Who is going to be using the tools?  
• What are their current skillsets from a governance and technology perspective? 
• What level of support do you anticipate needing to help you to use the tooling effectively? 
• If you need to change or add a feature over time, how difficult is this to do? 
• Have we had a demo of the tooling aligned with our specific use cases? 
• Have we been able to pilot the tooling in a critical data set or area of the business? 

This then gives you a clear set of requirements for you to align your tool selection against. Based on this, you may need to do a build versus buy analysis. Some solutions offer a cost-effective pay per use model, others have high licencing fees which can be prohibitively expensive, whilst your requirements might be simple enough to repurpose existing productivity tooling in your organisation. 

Remember just because the tooling is highly reviewed does not mean that it will solve the problems you have, connect to your current data landscape, or be able to be used by everyone who you want to use it. 

Being able to connect your Data Governance tooling to your data estate reduces manual work of populating information into the tooling, which is often the longest step in the implementation process. So, finding tooling that connects as smoothly as possible to all the systems where data is stored across your organisation is fundamental. To do this you need to answer the following questions: 

• Where is the organisation’s data stored?  Identifies the landscape of connections that you will have to make from the tooling. 
• What type of system it is stored in? So that you can confirm that the tooling has the appropriate connectors or if custom connectors will need to be built.  
• What is the architecture surrounding the systems? Can you easily get at the data, or is there complexity that you will need to address and resolve that could prevent easy implementation? 
• What types of data are stored in these systems? Structured data provides a happy path, whilst unstructured data can create challenges. 
• How critical is the data to the organisation? This helps you know where to focus your efforts and not spend all your time working on data that provides no or little value to your organisation. 
• What is the security posture around the system? The security of the systems, including whether the tool will be able to scan the data within it or if it will not be made accessible. 
• What are the systems overall criticality to your organisation? Identifying the risks associated with integrating the tool with your existing technology will be important, as some systems are too critical to fail and likely won’t allow for integration with Data Governance tooling. 

Use this checklist during the tooling selection process to ensure that the tooling you selected connects to most of your systems, and all the systems containing critical data. 

The features and functionality of Data Governance tooling is often what it is sold upon, with teams becoming enamoured with capabilities embedded within the tooling. Unfortunately, those features are not typically what goes wrong with Data Governance tooling. Too often, non-functional requirements are overlooked during the purchase process, and can cause the biggest hiccups post implementation. For example: 

• Information security will not give the tooling access to critical systems due to the nature of the data stored within those systems, thus limiting the estate that the tooling can cover. 
• The tooling isn’t scalable enough to support the volumes of data you have, so it either operates slowly or only covers a limited portion of your data estate. 
• The maintenance of the tooling is difficult or requires specialist knowledge that you have to seek outside help on, thereby limiting effective wider deployment of the tooling. 
• There is a lack of training and support from your solution provider, which hinders adoption by users and lowers the value of tooling. 
• It is more difficult to connect to your data sources than you thought (either due to a lack of connectors or internal requirements that were not thought of during the purchase process). 
• Reporting on issues such as data quality versus attempting to fix it are very different things, as processes are in place to take identified issues, determine root cause, and implement a remediation plan. 
• The cost is higher than expected. For consumption-based tools, this is often the case as the scale of governance widens across the enterprise.  
• The tooling impacts source system performance, which causes system administrators to ask for it to be disconnected and limits coverage of the data estate.  

Some of these issues can be addressed through a structured pilot; others involve you ensuring that all the right people across your organisation are involved in the conversation to select your ideal tooling. 

Once you have selected your tooling you now get to the hard part of making sure the tooling adds value. To do this you need to create a robust roadmap that answers the following questions: 

• When data is going to be ingested, and are there any issues with systems that need to be addressed? 
• What connectors are needed, are they all available, and what permissions are needed to use them?  
• Who needs to be involved? 
• Make sure you have everyone involved in the conversation as early as possible from you Information Security, DPO, Architects, IT teams, data owners, and HR if employee data will be used within the tool  
• How is information about data going to be added and enriched? 
• By a person, through automation, or some other tool  
• Who is going to validate the definitions, rules, controls etc? 
• How are you going to manage your vendors? (especially if you are planning on bringing in multiple tooling solutions) 
• How will we deliver? 
• Use a pilot to understand how much time each step will take 
• Identify and address quick wins as early as possible in the process 
• How will we engage users? 
• Ensure you have a training plan for everyone who is going to be using the tooling 
• And remember to communicate to all stakeholders frequently and engage your whole organisation with what is coming and how it will help them 

Align the steps you take to your use cases for the tooling and focus on the data most critical to your organisation to ensure you add value. 

It is always worth remembering that technology is just one facet of delivering and maintaining good Data Governance. Your people and processes are equally, if not more important. If you’d like to learn more about ways to implement and improve your data governance, download our Ultimate Guide to Data Governance (insert link). 

What data means and how it is used will often only be in minds of a select few key individuals across your organisation. A fundamental part of bringing in tooling will be ensuring that you can document this knowledge within the tooling as quickly as possible. 

Already having this information documented will allow you to get going quickly. If you must wait for key personnel to document their knowledge, then by the time they are ready to use your shiny new piece of tech, you’ll have lost months, and people will start to ask about its value. 

While Data Governance tooling is brilliant at many things, it will not do Data Governance for you. In fact, it might end up hindering your progress if you do not have the right capabilities and information to use it effectively. Any tooling you use is only as useful as the knowledge of the people creating information within it and the capabilities of business users to use it. So, you need a network of people with a deep understanding of data who are engaged and ready when you bring on your tool supported by your data stewards and owners. 

You also need to consider who you plan on using the tooling and their current skill set and be aware of what skills are needed to use the tooling effectively. Don’t bring in tooling that requires extensive training. Group the different types of users who you anticipate using the tooling together and research their skill sets, e.g. can they code, could they profile data, and how those align with the skills needed to use the tooling effectively. 

For example, you might have three groups for a data catalogue tool: 

• ‘Developers’ – who are needed to implement the tooling, connect it to systems, ensure data is ingested, and support its maintenance  
• ‘Documenters’ – who are needed to add definitions to the data, and ensure that the data is linked together 
• ‘Designers’ – who need to be able to understand what the data means to be able to select the right data to design reports 

But remember, the more people that can use your tooling, the better outcomes you are likely to create; giving people access to well-defined data removes silos and reduces the risks of local out-of-date spreadsheets and the wrong data being selected to answer questions and inconsistencies in reporting. 

One of your goals should be to improve accountability so that people can take ownership of the data that they work with. 

Don’t underestimate the challenge of getting people to engage with your new Data Governance tooling. 

It may not be obvious, but tooling work best when organisational culture can support it because using tooling effectively means having people capable of interacting with it in a meaningful way. 

At the start of the Data Governance journey, most information created about data can be comfortably documented within readily available productivity tooling. You will also find that the skills and knowledge of your key users at this stage often mean that they need significant support to use any tooling. 

Tooling comes into its own when you have significant detail at a granular level across entire data sets, with multiple functions across an organisation represented. Your users will need to have significant capability in both enterprise and data knowledge to use the tool to provide value, which may involve upskilling your existing team. 

Populating tooling should not be the end goal of any Data Governance tooling project, but the first stage of providing meaningful, timely, accurate information about the data across your organisation which helps support the Data Governance journey. This ensures data becomes a valuable asset and not a blocker to growth. 

At Oakland we offer a variety of services to help you with Data Governance Tooling: 

• Rapid assessment of your data governance and data quality tooling with recommendations to improve capability, increase business uptake, and deliver ROI 
• New data governance and data quality tooling selection 
• Data governance and data quality tooling implementation 

Contact us for more information. 

Wherever you are on your Data Governance journey, Oakland can help you make the next step.