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How to select the best data governance tools

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At Cognopia we have been fortunate to work with some of the best Data Governance Tools. We have also had the benefit of running Data Management Maturity Assessments to see the outcome of vendor selections. We wrote this article to help you in your vendor selection and management process. Before we list the top data governance tools, let’s focus on the process to pick a tool.

What not to do when selecting a data governance tool

First and foremost, do not immediately reach for the tool. We usually see this approach when Data Governance is an IT function. Before you pick the best data governance tool, you need to set up the best data governance team.

The “wrong” approach to data governance tool selection is as follows:

  1. Create boilerplate RFP and send to top data governance tool vendors
  2. Select tool based on the availability of “best practice” data governance processes that come “out of the box”
  3. Train your internal team how to use the tool, and integrate it to every other system in your firm
  4. Sit back and measure the outcome (this is usually when you want to start polishing your CV)

Why is this the wrong way to select the best data governance tool? Because you’ve got the process backwards. Let’s look at a better way to approach the problem.

How to run a successful selection process

In order to get the right data management software, you first need to establish the right data management processes. To do that, you’ll need to evaluate your current Data Maturity, and make a plan to improve it. You’ll also need to prioritise the data management work your organisation needs to avoid falling into the trap of trying to “boil the ocean”.

Data value
Prove the value of what you do

A better approach to picking the top data governance tool:

  1. Establish the outcome you are trying to achieve for your organisation
    • This should not be “we want to pick the best data governance tool”, or “we want to treat data as an Enterprise asset”
    • Instead align your outcome to a Strategic business priority. “We will deliver high quality data that supports the objective of growing market share in Vietnam by 7% in 2021, delivering $8,700,000 in new revenue” is better
    • If you’re selecting a Data Lineage tool, do not say “we will automatically produce lineages for all our CDE’s” – this has no value attached. Instead say “we will reduce the next MAS compliance audit cost by $3m SGD”.
  2. Design and change the organisational structures so you can achieve the outcome(s):
    1. Data Governance is an exercise in Change Management, you’ll need to change the way your team works to get it right
    2. You will need a Data Governance Operating Model. This will establish the right Data Owners and Data Stewards (or change them if your as-is team are not performing to your expectations)
    3. You’ll need Data Governance Principles and Policies. These set the rules your team must follow, and are essential to your success.
  3. Design the new business processes for the Data Governance team
    1. Who does what, where and when?
    2. What happens when Data Stewards disagree on the right definition?
    3. Leverage your Operating Model’s RACI to create new workflows
  4. Finally, select the best data governance tools to automate these new business processes

Why is this approach a better way to choose a data governance tool?

By establishing the outcome you want to achieve, you have something against which to measure your success. Because you’ve tied the outcome to a dollar figure, you can measure the improvement in terms the business cares about.

Having established a great data governance business case you’re then in the position to design the right operating model. One of the major causes of failure in data governance projects comes from putting the wrong people in charge of the project. Data governance is not an IT function. Do not appoint IT roles as Data Owners (unless they’re governing IT data). The business creates the data, they need to own it’s quality and meaning. To engage them in this role you’ll use the dollar benefits in your business case to prove the value of this work.

With the team in place and on-board about their new role, you can design workflows. Before you select a data governance tool you need to ensure these workflows are operating properly. Start your journey using excel, then look at where the pain-points are for the business to identify your tool requirements.

The final step: selecting the best data governance tool

With the team set up and operational, they will be able to determine the most important tool features. Do not start with a series of product demonstrations. Asking your vendor to demonstrate their solution without first sharing your goals and requirements is asking for trouble. First, sit down with the business users and document what is most time-consuming, or what they feel is missing in their current process.

Technology can be a great enabler, but it can also over-reach. For each requirement, consider how important it is to the team members. Get everyone to rank the requirements by order of importance to them. Just because someone in management wants lineages that scan every technology in your organisation does not mean you should do it.

Ask critical questions. How frequently are we performing the task(s) we are looking to automate? What is the cost of performing these manually? How much risk does it add to do this manually? If we could automate this work, what more could we produce and what benefits would we unlock for the business?

What are the top data governance tools?

The top data governance tool
You deserve the best!

This entirely depends on the following:

  1. What is the use-case you’ve outlined for the data governance tool?
  2. What budget do you have to spend? (hint, this should be a function of the benefits your outcome will deliver)
  3. What tools do you already have in-house that could be leveraged?
  4. What restrictions and controls do your IT team place on new technology (e.g. are you allowed cloud-only solutions?)
  5. Which vendor has the best cultural fit against your requirements?
  6. Which vendor has the best product fit against your requirements?

Picking the right tool for you:

If you are looking for the enterprise-grade tools that have been given the Gartner Magic Quadrant “seal of approval”, you’ll be looking at:

  1. Informatica – they have a suite of tools and will likely try to sell you all of them. Beware of long projects and resource availability (in Asia)
  2. Collibra – more business friendly, SaaS first solution. Strong workflows and an API led approach to integration
  3. ASG – the broadest and deepest data lineage scanning solution available. Newer features greatly help data science teams with data discovery.
  4. Alex Solutions – a surprise find in the “Leaders” section. Well used in their home-turf (Australia) but users give poor marks for user-friendliness as projects are custom-built
  5. Alation – designed for Data Curation, and widely praised as user friendly. Seen as somewhat lacking in features. Lots of manual effort required for lineage building. Some users complain about bugs in new builds, suggesting features are rushed out.

What about tier 2 data governance solutions?

You might not need the Enterprise grade tech, especially if you’ve got a niche problem to solve. Consider the following solutions:

  1. OvalEdge – a newer entrant into the market, offering lineage and cataloging capabilities at a reasonable per user price. Seen as a useful tool for Data Science teams and quick to install. Some users complain about the UX.
  2. Talend – a strong ETL contender that has expanded its offering into Data Management. Features include Data Cataloging, Stewardship and Preparation. Some complaints over speed and profiling capacity.
  3. Erwin – Building on their experience in relational database diagrams, Erwin has added Data Governance functionality. Users complain about the search capabilities and UI. The overall experience seems positive for technical users.

What else should we consider?

So, you’ve nailed your value proposition. The business bought into the plan. You have solid requirements, ranked by priority. You have documented technical requirements, with no frivolous bells and whistles. And finally, you’ve picked a handful of data governance tool vendors. What about the implementation experience?

Some things to consider:

  1. Does the vendor implement the product themselves, or do they rely on a partner?
    1. You want the vendor to have “skin in the game”
    2. Include partner firms when they expand the skills your vendor offers
    3. Partners should advise you on best practice. As a result, they will deliver the right solution for your business. Be wary if they are “yes men” that will just hack the technology you selected. The partner should feel confident in pushing back against scope creep and over-reach.
  2. How long will it take to implement?
    1. Your scope needs to be achievable in 3-6 months. Otherwise your executive stakeholders may lose patience.
    2. It’s your responsibility to set the right scope. Do not force vendors to meet unrealistic timelines. It hurts both parties equally, and your job is on the line if this goes south
    3. Discuss this with your vendors and collaborate on the approach to deliver
  3. What is the after-service support like?
    1. Do you need a vendor to manage the whole solution? Or are you looking for product support only?
  4. How are enhancements and bug fixes handled?
    1. Make sure your vendor cares about your success. If you’re a small customer for a big vendor, you might not get the love you expect.

Next steps to select the best data governance tools?

If you need help selecting the best data governance tool, get in touch. Cognopia can help determine your data maturity. Once we understand your maturity, we can advise on your requirements. As a result of getting the requirements right you can set up the people and processes you need to succeed. We can train the team in Data Stewardship best practices, and ensure you get the right solution for your needs.

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