6 Dos and Don'ts of Data Governance - Part 1

Set Clear Expectations From the Start

One big mistake I see organizations make when starting out on their data governance journey is forgetting the rationale behind data. So don't just govern to govern. Whether you need to minimize risks or maximize your benefits, link your data governance projects to clear and measurable outcomes. As data governance is not a departmental initiative, but rather a company-wide initiative, you will need to prove its value from the start to convince leaders to prioritize and allocate some resources.

What Is Your "Emerald City"? Define Your Meaning of Success

In the Wonderful Wizard of Oz, the "Emerald City" is Dorothy's ultimate destination, the end of the famous yellow brick road. In your data governance project, success can take different forms: reinforcing data control, mitigating risks or data breaches, reducing time spent by business teams, monetizing your data or producing new value from your data pipelines. Meeting compliance standards to avoid penalties is crucial to be considered. Ensure you know where you are headed and where the destination is.

Secure Your Funding

As you're building the fundamentals of your projects and you're defining your criteria for success, you will explain the why, the what, and the how. However, make sure you don't forget to ask "how much" to identify associated costs and the necessary resources to be successful. If you're a newly assigned Data Protection Officer (DPO), make sure you have a minimum secured operating fund.

If you're a Chief Data Officer (CDO), align with the Chief Technology Officer (CTO) to secure your funding together. Then pitch your proposal to the finance team together so that they understand the company risks linked to failed compliance by explaining the value of your data strategy and all the hidden potential behind data. Make sure you present them with the perspective of data as a financial asset.

Don't Go in Alone

As you know, and it cannot be said often enough, a data journey is not another single and IT-specific project. Even if you can go fast apprehending tools and take advantage of powerful apps, delivering trusted data is a team sport. Gather your colleagues from various departments and start a discussion group around the data challenges they're facing. Try to identify what kind of issues they have.

Frequent complaints are:

• "I can't find the data I am looking for."
• "I cannot access datasets easily."
• "Salesforce data is polluted."
• "How can I make sure it's trusted?"
• "We spent too much time removing duplicates manually."

You will soon discover that one of the biggest challenges is to build a data value chain that various profiles can leverage to get trusted data into the data pipelines. Work with peers to clarify, document, and see together how to remove these pains. Bring people along on your data journey and give them responsibilities so the project won't be your project but rather a team project. Show that the success will not just be for you, but for all team members to enjoy together.

Apply Governance With a "Yes!"

Avoid too much control with a top-down approach. On the contrary, apply the collaborative and controlled model of data governance to enable controlled role-based applications that will allow your data stakeholders and the entire stakeholder's community to harness the power of data with governance put in place from the get-go.

Make sure that the business understands the benefits, but also that they are ready to participate in the effort of delivering trusted data at the speed of the business.

Start With Your Data

Traditional governance strategies often apply a non-negotiable top-down approach to assign accountabilities into data. While you should spend time getting directions on your Data Governance, the truth is it won't be highly efficient as you'll often confront high levels of resistance. Start with your data and, more importantly, with the people using it. Listen to business experts and collaborators, get into your data sets to detect business value and potential business risks, then identify who is using the data set the most, as they will often be the ones who will be the most inclined to protect and maintain a high level of integrity into your data sets.