If you go to most business leaders to discuss data governance, you are likely to get a glazing stare that screams, “What the heck are you talking about?” And then your mind races to figure out where to begin. Data policies? Data security? Data quality? Data cataloging? Master data management?Let’s face it, most business leaders even today have little understanding of data governance even with all the headlines around regulations, privacy, data security, data breaches, algorithm bias, and the impacts of poor data quality.

Finding Supporters for Data Governance Initiatives

But ask anyone who’s job depends on data quality, and you’re likely to get an entirely different response. It sounds like a wallowing, “help meeeeeee,” because they don’t have the skills, tools, processes, authority, or access to make substantive changes.

But more often, you have to find supporters by relating to their wants and needs. Examples:

• Ask citizen data scientists trying to wrangle in new data sets and don’t have reasonable data prep tools.
• Look at the CMO who’s survived the negative headlines from a data breach, and they’ll show interest in securing data.
• Try legal who has to wait weeks for data during a discovery process because there’s lacking policies and procedures.
• Discuss with IT, who often rescue poorly implemented data integration procedures that get stuck or entirely stop because of bad data.
• Ask the COO, who sits through too many meetings around workforce scheduling or supply chain issues because they are working with days old data.
• Try the CFO, who must forecast financial performance using data with known data quality issues.
• Now go to the CEO and provide examples of how other companies in your industry are using analytics and machine learning to competitive advantage.

An Offensive, Proactive Data Governance Strategy

Proactive data governance means an offensive implementation strategy rather than just a defensive one. – Isaac Sacolick

You’re not just complying with regulation, privacy, or security; your organization elects to get one step ahead of compliance and aims to be more transparent. You’re not just fixing data debt and data quality issues, you’re democratizing data with data catalogs and establishing master data sources. DataOps is not just about getting data from source to destination; you’re actively investing in finding new data sources and publishing APIs to work with partners.

You’re also scaling these practices to handle more data for machine learning experiments and becoming a real-time enterprise to support IoT.

To implement proactive data governance, you need agile data practices that enable prioritization and implementation in iterations. Furthermore, the feedback loop from those consuming data in citizen data science through machine learning programs must be in place to drive priorities and investment.

But I confess, getting organizational support and implementing this program is a lot harder than a graphics, blog post, or PowerPoint presentation. I’m happy to discuss proactive data governance!