This Is Why Your Data Governance Strategy Is Failing

The first step of goal achievement is goal setting, and the first rule of goal setting is to make SMART goals — specific, measurable, attainable, relevant, and time-based. This best practice is well known by teams across industries and has been implemented for decades.

However, as Gartner’s report The State of Data and Analytics Governance Is Worse Than You Think points out, when it comes to data governance, SMART goals are often just the opposite. While 80% of organizations say data governance is important to enabling business outcomes, nearly half do not assess, measure, or monitor their data governance programs. It’s no wonder, then, that fewer than a third expect to exceed data and analytics return on investment in the next two years.

What is keeping data teams from proving data governance ROI to leadership teams — and likewise, why do leaders both within IT and outside of it fail to see the impact of modern data governance on achieving business outcomes? The answer may be simpler than it seems.

Barriers to Successful Data Governance Programs

Goal attainment can be likened to a game of tug-of-war — unless everyone on your team is pulling in the same direction, you will be outperformed by your competitor. When it comes to business goals, the same logic applies. Each team’s specific objectives should map to broader company goals — otherwise, resources will be spent on activities that do not contribute to getting ahead of the competition.

Therefore, when a data governance framework is built in a vacuum and focuses only on data-related goal attainment, other teams struggle to see the value it provides. The average data user may not be concerned with whether their access to data is HIPAA compliant, for example — they just want to be able to use the data they need when they need it, in order to help do their job and achieve their own team’s goals. Anything that hinders this can seem arbitrary and frustrating.

Yet, according to Gartner, most data governance initiatives focus solely on data and analytics outcomes. Even when the performance of those initiatives is measured, success is usually defined on the basis of compliance rather than time, efficiency, or scalability of secure data access. As a result, data consumers feel bogged down by seemingly over complicated processes and leadership teams struggle to see how data governance investments and objectives impact business success metrics. Over time, this may lead to data users sidestepping data governance practices by moving or copying data, which not just heightens the risk of unauthorized data access, but also causes siloed data use that precludes compliant collaboration.

By not aligning data governance goals with business goals, data use — particularly sensitive data use — becomes inefficient, risky, and devoid of real value. Data teams fail to optimize productivity, reduce costs, and accelerate innovation. A quarter of those surveyed by Gartner achieved nothing they set out to accomplish with data. As a result, leadership teams are hesitant to invest in and support data governance tools and initiatives that seemingly have no impact on their bottom line or success.

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Strategic Data Governance Starts From the Top

For leadership teams to fully understand and appreciate the impact of data governance, they need to see how data initiatives are implicitly tied to organizational success. That starts with aligning data governance goals with business outcomes, and framing data and analytics through the lens of leaders’ objectives.

Data leaders should develop measurable KPIs, align them to resources needed to hit them, and clearly link both the metrics and the assets to business and stakeholder value. Next, they should construct a data governance program that enables strategic (data-driven) and business (analytics-driven) outcomes. Data leaders should then familiarize leaders across the organization with how the data governance strategy will help each team meet its goals, and recruit their peers’ support in implementing the framework in their respective lines of business. Importantly, leaders who know their role in data governance and how it relates to business objectives will be more committed to ensuring its success.

A collaborative approach to data governance and data use, transparency in progress to goals and how data governance is helping drive outcomes, and a future-proof strategy built for long term growth are key aspects of getting leadership buy-in organization-wide and implementing a top-down approach to data governance and use.

Making a Data Governance Strategy a Reality

Now that you have leadership buy-in for your data governance strategy, the next step is to make it a reality. As with any organizational shift, change may be slow — particularly when ingrained habits and processes are involved.

Ensuring business leaders understand the data governance operating model and how data decisions are made will empower them to amplify its importance throughout the organization. With documented processes — including a RACI matrix — leaders can confidently reiterate data use standards, anticipate conflicts, and facilitate collaboration. This creates a trickle-down effect that raises the stature of secure data access control and use, which in turn mitigates against siloed and potentially risky data practices.

To simplify the process even further, leaders need to invest in the right tools — specifically, tools that make it as easy as possible for data teams to prepare data for use and for data users to be able to access it in real time. That is where automated tools like Immuta set some organizations apart from the competition.

Immuta’s solution delivers governed data in real time, using fine-grained access controls, intelligent data policies, and advanced privacy enhancing technologies (PETs) to dynamically adapt data views for each user. With Immuta, data teams benefit from:


  • Simplified, centralized cloud data access control — Immuta’s fine-grained, attribute-based access controls help avoid an explosion of roles, views, and data copies across platforms. Policies written in plain English are easy for non-technical stakeholders to understand and are applied at query time, allowing data teams to more easily manage and scale secure data access and transparency.
  • Data discovery and classification — With automated sensitive data discovery, data teams can classify and tag data across platforms after it has been registered with Immuta, and enforce global policies consistently. This avoids time-consuming, risk-prone manual inspection, making data teams more efficient and reducing time to data access — which means faster time to data insights that drive business outcomes.
  • Consistent data privacy — Immuta allows data teams to author data policies once and scale them across platforms, standardizing privacy controls without having to start from scratch for each individual cloud provider. This includes dynamic data masking techniques like k-anonymization, format-preserving masking, joins on masked data, rounding, differential privacy, and randomized response. Along with conditional logic, this means data teams can easily implement privacy controls, and data consumers can access data they have clearance for without hitting any complete dead ends that halt analytics.
  • Data monitoring and auditing — Data and leadership teams alike can rest assured that data protection mechanisms are performing as they should with Immuta’s automated data audit trails, which show what data was accessed, by whom, when, and for what purpose, but also provides data fingerprints that identify changes made to data over time. Any questionable results can be quickly escalated and dealt with before they become full-blown data leaks or breaches.

Find out how you can hone your data access control capabilities and governance strategy when you request a demo of Immuta.


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