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How do you show the impact of data governance on organization performance?

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Data governance is the process of managing the quality, security, availability, and usage of data in an organization. It involves setting policies, standards, roles, and responsibilities for data-related activities and ensuring compliance and alignment with business goals. But how do you show the impact of data governance on organization performance? How do you measure and communicate the value of data governance to stakeholders and decision-makers? In this article, we will explore some of the ways to demonstrate the benefits of data governance and how to use them to support your data strategy.

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1 Define your data governance objectives

The first step to show the impact of data governance is to define your data governance objectives. What are the specific outcomes that you want to achieve with data governance? How do they align with your organization's vision, mission, and strategy? How do they support your data-driven initiatives and projects? By defining your data governance objectives, you can establish a clear direction and scope for your data governance program and identify the key performance indicators (KPIs) that will help you track and evaluate your progress.

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Piyush Malik

LinkedIN TopVoice 2023 | Data, AppliedAI, Technology & Strategy | CXO | BOD Advisor | Entrepreneur | Analytics | Cloud | Do click ?? to be notified of my latest posts
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One thing that has always worked with my clients irrespective of industry is tying their business KPIs back to data strategy and data governance programs. For instance when a neo bank wanted to become the top 3 bank in Latin America, we implemented their data governance initiative such that first we built their ideal customer profile and various personas involved. After mapping existing customers to those personas, we provided visibility & traceability to the management team so they could track the growth not only in their customer numbers as signups via marketing campaigns increased, but also how each customer segment's Life Time Value & personalized interactions with the bank were contributing to the bank's improved profits. #HappyCDO

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Gautham Nandyala
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In addition I would also look to align the overall data strategy objectives with the business KPI’s which includes aspects of data governance. As this paints a holistic picture of the data strategy landscape and how it can enable the organization’s vision.

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Yaswanth Dontu

Generative AI, Data & Digital Innovation manager with experience in digital transformation in Mobility, Business Consulting and Energy industries
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For me, it is very important to link the business need and value to the governance activities. We all know that data is becoming key asset of an organisation. But why should it be governed and to what extent? This question needs to be answered. One way we tried to implement this in a previous project is by linking the the value of a dataset (in terms of monetary value and number of projects using a data set) to the rigour with which we will govern that data set. In essence, we do not want to boil the ocean. Govern the right data set, in the right amount at the right time.

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2 Select your data governance metrics

The next step is to select your data governance metrics; quantitative and qualitative measures that will help you assess the performance and impact of your data governance program. These metrics should be aligned with your objectives and reflect the value that data governance brings to your organization. Examples of data governance metrics include data quality metrics, which measure accuracy, completeness, consistency, timeliness, and validity of data; data security metrics, which measure protection, privacy, and compliance of data; data availability metrics, which measure accessibility, availability, and recoverability of data; and data usage metrics, which measure utilization, value, and impact of data. These measures can help you identify and resolve data quality issues, monitor and prevent breaches, optimize storage and backup, understand how data is used in the organization, and support business decisions.

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Mark Beyer
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Data governance is not data management. If there were a relationship statement it would be Governance > Authority granted > Responsibility proof > Management accountability Rather than going all TLDR, the best illustrations are the periodic table and GPS. Almost every enterprise uses them, but none govern them. Anyone using them understands the rules and have no authority to change the rules. Use cases must prove any management system is responsible to the model. Balance the governance model that shifts the pendulum where authority and responsibility meet. Keep that same mindset addressing how management and governance interact. Central governance is not the same as centralization. Distributed/distribution? Federated/federation?

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Neil Raden

Author, Advisor, Mathematician .Thinkers360 Global Thought Leader/Influencer iAI, Analytics, Predictive Analytics, National Security, GenAI, International Relations, Design Thinking, InsurTech, Quantum, and Health Tech
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I like the way Mark Beyer described governance vs data management. Metaphor is our most powerful tool to explain difficult concepts.

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3 Collect and analyze your data governance data

The third step is to collect and analyze your data governance data. This involves gathering the data that supports your data governance metrics and performing the calculations, transformations, and aggregations necessary to produce meaningful results. To do this, you may use tools such as data catalogs, which store metadata about your data assets; data quality tools, which help monitor and improve the quality of your data; data security tools, which help protect and encrypt your data; and data analytics tools, which help explore, visualize, and communicate your data. All of these can help you discover, document, and manage your data assets; implement policies and controls; provide reports and dashboards on metrics such as quality, security, and usage; and create a single source of truth for your data governance.

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4 Communicate and report your data governance results

The final step is to communicate and report your data governance results, which involves presenting and sharing your data governance data and metrics with stakeholders and decision-makers, while highlighting the impact and value of the program. Various formats and channels can be used for this, such as data governance reports that provide a comprehensive overview of performance and impact; dashboards that offer quick access to insights and trends; and stories that illustrate data governance value and outcomes. By following these steps, you can show the effect of data governance on organization performance, identify opportunities and challenges , prioritize them, and continuously improve your data governance program.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you show the impact of data governance on organization performance?

1

2

3

4

5

1 Define your data governance objectives

2 Select your data governance metrics

3 Collect and analyze your data governance data

4 Communicate and report your data governance results

5 Here’s what else to consider

Data Governance

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How do you show the impact of data governance on organization performance?

1

2

3

4

5

1 Define your data governance objectives

2 Select your data governance metrics

3 Collect and analyze your data governance data

4 Communicate and report your data governance results

5 Here’s what else to consider

Data Governance

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