How Data Governance Leaders Can Identify Quick Wins

I am writing today’s blog post from Tucson, where I wrote the epilogue to Digital Trailblazer.

“I’m taking a different type of walk today, a hike actually, on one of the trails winding through Saguaro National Park in Tucson, Arizona,” is how I opened the chapter where I reflect on my journey writing the book. I took a similar but much longer hike yesterday through Sabino Canyon.

Today, I say goodbye to my older son, who is starting his sophomore year at The University of Arizona studying aerospace engineering.

During my stay here, I encountered two issues at my hotels that made me think about how data issues, or just not leveraging data, can lead to poor customer experiences. At my first hotel, they emailed me three days before check-in, apologizing for the construction renovations at my hotel. There was no context on the type of construction, and of course, the hotel notified me too late to make any change in plans. At the second hotel, one that I’ve stayed at many times, a new parking fee showed up on my bill without any notice when I booked the room or checked in.  

These are the hard data issues where the data exists, but the automation to connect the information to customers is poorly implemented.

Data governance leaders face difficult challenges, including identifying data owners, actioning data policies, automating data flows, and monitoring data health. In most situations, data governance leaders are only facilitators and must partner with business, technology, security, and compliance leaders on the policies and implementation.

Proactive data governance requires a defensive and offensive mindset; how are data leaders protecting the organization and its data – and how are data cleansing, mastering, and automation efforts enabling customer experiences, workflow improvements, and other strategic advantages?

In my Driving Digital Standup video I posted last week, I share four important priorities for smart and safe data governance leaders. These priorities are all in the data leader’s “backyard,” where it should be easier to find partners in IT and security to implement and find quick wins.

Finding the quick wins where data quality is important

Here are some ways I seek quick wins for data governance leaders

• Keep version one of policies simple and add exceptions later
• Prioritize mature data security practices and technologies that are easy to implement
• Look for wins in IT, security, and compliance with leaders ready to partner
• Use agile data practices to align governance work with business efforts in building dashboards and ML models
• Seek business leaders with an analytics mindset, especially in areas where improving data health impacts revenue and customer experiences
• Look for where the organization is growing or should be accelerating its data practices, then align with AIOps, MLOps, ModelOps, and other solutions that drive scalability

Businesses are adding new data sources and looking for new ways to enable data-driven organizations. There are departments adding citizen data scientists to create dashboards for decision-making, while AI and machine learning require creating trustworthy centralized data sources. Bottom line – the priorities for data governance leaders are never-ending, and my advice is to seek quick wins, even when trying to climb data debt mountains.

To read more of my stories, see Chapter 6 of Digital Trailblazer, “Buried in Bad Data.”

Join us for a future session of Coffee with Digital Trailblazers, where we discuss topics for aspiring transformation leaders. If you enjoy my thought leadership, please sign up for the Driving Digital Newsletter and read all about my transformation stories in Digital Trailblazer.