How to crack the code on data and analytics governance

Data and analytics leaders, including chief data officers, are facing an increasingly challenging business landscape. As the volume and velocity of data rises and CDOs are under pressure to support digital business outcomes, data and analytics governance is no longer just a matter of remaining compliant with regulations – it is a critical business capability.

D&A governance is essential to driving efficiency and realizing value from data assets and data-driven processes. Simply having lots of data is not sufficient.

Data used in analytics, data science and machine learning must be governed along its entire life cycle to establish its provenance, meaning and relationship to the business, and how it can and should be used. Governance is required to establish decision rights and accountabilities, to define policies such as quality, ethics, privacy and security, and to ensure that better information related behaviors deliver better business outcomes.

Yet Gartner research shows that organizations are falling well short of their D&A governance objectives, resulting in higher costs, slower time to market and increased business risk.

Gartner predicts that through 2025, 80% of organizations seeking to scale up digital business will fail because they do not take a modern approach to D&A governance. If CDOs expect to deliver effective D&A strategies, they must crack the code on governance. Here’s how:

Analyze and address governance barriers to business success

Many governance initiatives concurrently focus on enabling several business objectives, such as cost optimization, information product monetization and regulatory compliance. Organizations expect that their D&A governance will enable all these outcomes, but they often fall short. This means investment in existing and emerging D&A opportunities cannot be fully realized, leading to higher operational costs, slower time to value for products and services and decreased competitiveness.

To understand how to create a successful D&A governance program, it’s important first to analyze some of the most common pitfalls and barriers impacting the success of governance initiatives. These include:

  • Lack of business leadership. While CDOs and their teams are accountable for achieving enterprise results through D&A, they cannot do this alone. Since the purpose of D&A is to enable business results, it is critical that business leaders engage in governance initiatives to take accountability for the data that they create, consume and – to some extent – control.
  • Focusing on the wrong metrics. When governance initiatives are IT-led, efforts often focus on achieving D&A metrics rather than enabling specific business outcomes. For example, a D&A governance dashboard may target a specific data quality metric, like number of records cleaned. A better dashboard would show how data quality improvement supports business metrics, like customer churn or compliance with GDPR.
  • Failure to track governance investments against business value. In many cases, IT leaders do not track the work of D&A governance against the business value they expect to deliver.
  • One-size-fits-all approach. Most D&A governance initiatives, particularly those led through IT, are control-oriented and centralized. While this is an efficient approach for some scenarios, such as regulatory and compliance, it is ineffective for others, such as self-service models and data science labs.
  • Silo-based approach. D&A leaders are quick to highlight the need to break down silos across business areas, but they often fail to recognize the siloed approaches that they themselves are taking when focusing on governance from an IT perspective, rather than taking a business approach.

To overcome the barriers to success, data and analytics leaders should use root cause analysis to understand the issues, and explore available remediation options, resulting in a plan to address governance barriers. The cost-benefit projections of each option can then be assessed with key business stakeholders, so that the best option is selected. As a result, a medium-term and longer-term roadmap for your D&A governance program can be calibrated for success.

Establish balanced D&A governance leadership and teams

It is critical that “success” in D&A governance is understood consistently across both business and IT. Therefore, establishing balanced D&A governance leadership and teams is critical for governance success.

Gartner research has found that a combined IT/business-led team is the most effective approach to enabling business outcomes from D&A governance initiatives.

Combined teams contain a more balanced representation of business and IT leaders and team members that work together to achieve measurable business results through more targeted governance of D&A assets. Furthermore, combined teams offer greater credibility, business focus and prioritization for the work of D&A governance.

Merely having combined teams for D&A governance is not enough, though — everyone must work collaboratively to enable successful business outcomes. The hallmark of highly successful D&A governance teams is effective communication. In fact, Gartner’s 6th Annual CDO Survey found that effective communication and cross-function collaboration were the top two critical competencies for CDO success.

Build a governance leadership team that contains the owners of key business processes directly related to your target governance outcomes, as well as senior D&A leadership, such as the CDO. Ensure that the responsibilities and accountabilities of your governance leadership and program team are agreed, documented and operationalized.

Focus on communication and collaboration by building and deploying a communications plan based on your governance objectives. Measure the improvement of specific business outcomes achieved through D&A governance and promote that improvement widely.

As a D&A leader, you are an agent of change in your organization, so think of governance as the mechanism for achieving organizational outcomes and design your stakeholder engagement plan around it.

Saul Judah is a research vice president at Gartner Inc., focusing on information governance, data quality and information strategy. He wrote this article for SiliconANGLE. Gartner analysts will discuss the latest trends in data, analytics and artificial intelligence at the Gartner Data & Analytics Summit in August.

Image: geralt/Pixabay

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