Establish Data Governance

Deliver measurable business value.

Executive Brief

Analyst Perspective

Establish a data governance program that brings value to your organization.

Data governance does not sit as an island on its own in the organization – it must align with and be driven by your enterprise governance. As you build out data governance in your organization, it’s important to keep in mind that this program is meant to be an enabling framework of oversight and accountabilities for managing, handling, and protecting your company’s data assets. It should never be perceived as bureaucratic or inhibiting to your data users. It should deliver agreed-upon models that are conducive to your organization’s operating culture, offering clarity on who can do what with the data and via what means. Data governance is the key enabler for bringing high-quality, trusted, secure, and discoverable data to the right users across your organization. Promote and drive the responsible and ethical use of data while helping to build and foster an organizational culture of data excellence.

Crystal Singh

Director, Research & Advisory, Data & Analytics Practice

Info-Tech Research Group

Executive Summary

Your Challenge

The amount of data within organizations is growing at an exponential rate, creating a need to adopt a formal approach to governing data. However, many organizations remain uninformed on how to effectively govern their data. Comprehensive data governance should define leadership, accountability, and responsibility related to data use and handling and be supported by a well-oiled operating model and relevant policies and procedures. This will help ensure the right data gets to the right people at the right time, using the right mechanisms.

Common Obstacles

Organizations are faced with challenges associated with changing data landscapes, evolving business models, industry disruptions, regulatory and compliance obligations, and changing and maturing user landscape and demand for data. Although the need for a data governance program is often evident, organizations miss the mark when their data governance efforts are not directly aligned to delivering measurable business value. Initiatives should support key strategic initiatives, as well as value streams and their underlying business capabilities.

Info-Tech’s Approach

Info-Tech’s approach to establishing and sustaining effective data governance is anchored in the strong alignment of organizational value streams and their business capabilities with key data governance dimensions and initiatives. Organizations should:

• Align their data governance with enterprise governance, business strategy and value streams to ensure the program delivers measurable business value.
• Understand their current data governance capabilities so as to build out a future state that is right-sized and relevant.
• Define data leadership, accountability, and responsibility. Support these with an operating model that effectively manages change and communication and fosters a culture of data excellence.

Info-Tech Insight

Your organization’s value streams and the associated business capabilities require effectively governed data. Without this, you face elevated operating costs, missed opportunities, eroded stakeholder satisfaction, and increased business risk.

Your challenge

This research is designed to help organizations build and sustain an effective data governance program.

• Your organization has recognized the need to treat data as a corporate asset for generating business value and/or managing and mitigating risk.
• This has brought data governance to the forefront and highlighted the need to build a performance-driven enterprise program for delivering quality, trusted, and readily consumable data to users.
• An effective data governance program is one that defines leadership, accountability, and responsibility related to data use and handling. It’s supported by a well-oiled operating model and relevant policies and procedures, all of which help build and foster a culture of data excellence where the right users get access to the right data at the right time via the right mechanisms.

As you embark on establishing data governance in your organization, it’s vital to ensure from the get-go that you define the drivers and business context for the program. Data governance should never be attempted without direction on how the program will yield measurable business value.

“Data processing and cleanup can consume more than half of an analytics team’s time, including that of highly paid data scientists, which limits scalability and frustrates employees.” – Petzold, et al., 2020

“The productivity of employees across the organization can suffer.” – Petzold, et al., 2020

Respondents to McKinsey’s 2019 Global Data Transformation Survey reported that an average of 30% of their total enterprise time was spent on non-value-added tasks because of poor data quality and availability. – Petzold, et al., 2020

Common obstacles

Some of the barriers that make data governance difficult to address for many organizations include:

• Gaps in communicating the strategic value of data and data governance to the organization. This is vital for securing senior leadership buy-in and support, which, in turn, is crucial for sustained success of the data governance program.
• Misinterpretation or a lack of understanding about data governance, including what it means for the organization and the individual data user.
• A perception that data governance is inhibiting or an added layer of bureaucracy or complication rather than an enabling and empowering framework for stakeholders in their use and handling of data.
• Embarking on data governance without firmly substantiating and understanding the organizational drivers for doing so. How is data governance going to support the organization’s value streams and their various business capabilities?
• Neglecting to define and measure success and performance. Just as in any other enterprise initiative, you have to be able to demonstrate an ROI for time, resources and funding. These metrics must demonstrate the measurable business value that data governance brings to the organization.
• Failure to align data governance with enterprise governance.

78% of companies (and 92% of top-tier companies) have a corporate initiative to become more data-driven. – Alation, 2020

But despite these ambitions, there appears to be a “data culture disconnect” – 58% of leaders overestimate the current data culture of their enterprises, giving a grade higher than the one produced by the study. – Fregoni, 2020

The strategic value of data

Power intelligent and transformative organizational performance through leveraging data.

Respond to industry disruptors

Optimize the way you serve your stakeholders and customers

Develop products and services to meet ever-evolving needs

Manage operations and mitigate risk

Harness the value of your data

The journey to being data-driven

The journey to declaring that you are a data-driven organization requires a pit stop at data enablement.

The Data Economy

Data Disengaged

You have a low appetite for data and rarely use data for decision making.

Data Enabled

Technology, data architecture, and people and processes are optimized and supported by data governance.

Data Driven

You are differentiating and competing on data and analytics; described as a “data first” organization. You’re collaborating through data. Data is an asset.

Data governance is essential for any organization that makes decisions about how it uses its data.

Data governance is an enabling framework of decision rights, responsibilities, and accountabilities for data assets across the enterprise.

Data governance is:

• Executed according to agreed-upon models that describe who can take what actions with what information, when, and using what methods (Olavsrud, 2021).
• True business-IT collaboration that will lead to increased consistency and confidence in data to support decision making. This, in turn, helps fuel innovation and growth.

If done correctly, data governance is not:

• An annoying, finger-waving roadblock in the way of getting things done.
• Meant to solve all data-related business or IT problems in an organization.
• An inhibitor or impediment to using and sharing data.

Info-Tech’s Data Governance Framework

Create impactful data governance by embedding it within enterprise governance

Organizational drivers for data governance

Data governance personas:

Conformance: Establishing data governance to meet regulations and compliance requirements.

Performance: Establishing data governance to fuel data-driven decision making for driving business value and managing and mitigating business risk.

Data Governance is not a one-person show

• Data governance needs a leader and a home. Define who is going to be leading, driving, and steering data governance in your organization.
• Senior executive leaders play a crucial role in championing and bringing visibility to the value of data and data governance. This is vital for building and fostering a culture of data excellence.
• Effective data governance comes with business and IT alignment, collaboration, and formally defined roles around data leadership, ownership, and stewardship.

Traditional data governance organizational structure

A traditional structure includes committees and roles that span across strategic, tactical, and operational duties. There is no one-size-fits-all data governance structure. However, most organizations follow a similar pattern when establishing committees, councils, and cross-functional groups. Most organizations strive to identify roles and responsibilities at a strategic and operational level. Several factors will influence the structure of the program, such as the focus of the data governance project and the maturity and size of the organization.

A healthy data culture is key to amplifying the power of your data.

“Albert Einstein is said to have remarked, ‘The world cannot be changed without changing our thinking.’ What is clear is that the greatest barrier to data success today is business culture, not lagging technology. “– Randy Bean, 2020

What does it look like?

• Everybody knows the data.
• Everybody trusts the data.
• Everybody talks about the data.

“It is not enough for companies to embrace modern data architectures, agile methodologies, and integrated business-data teams, or to establish centers of excellence to accelerate data initiatives, when only about 1 in 4 executives reported that their organization has successfully forged a data culture.”– Randy Bean, 2020

Data literacy is an essential part of a data-driven culture

• In a data-driven culture, decisions are made based on data evidence, not on gut instinct.
• Data often has untapped potential. A data-driven culture builds tools and skills, builds users’ trust in the condition and sources of data, and raises the data skills and understanding among their people on the front lines.
• Building a data culture takes an ongoing investment of time, effort, and money. This investment will not achieve the transformation you want without data literacy at the grassroots level.

Data-driven culture = “data matters to our company”

Despite investments in data initiative, organizations are carrying high levels of data debt

Data debt is “the accumulated cost that is associated with the sub-optimal governance of data assets in an enterprise, like technical debt.”

Data debt is a problem for 78% of organizations.

40% of organizations say individuals within the business do not trust data insights.

66% of organizations say a backlog of data debt is impacting new data management initiatives.

33% of organizations are not able to get value from a new system or technology investment.

30% of organizations are unable to become data-driven.

Source: Experian, 2020

Absent or sub-optimal data governance leads to data debt

Only 3% of companies’ data meets basic quality standards. (Source: Nagle, et al., 2017)

Organizations suspect 28% of their customer and prospect data is inaccurate in some way. (Source: Experian, 2020)

Only 51% of organizations consider the current state of their CRM or ERP data to be clean, allowing them to fully leverage it. (Source: Experian, 2020)

35% of organizations say they’re not able to see a ROI for data management initiatives. (Source: Experian, 2020)

Embrace the technology

Make the available data governance tools and technology work for you:

• Data catalog
• Business data glossary
• Data lineage
• Metadata management

While data governance tools and technologies are no panacea, leverage their automated and AI-enabled capabilities to augment your data governance program.

Measure success to demonstrate tangible business value

Put data governance into the context of the business:

• Tie the value of data governance and its initiatives back to the business capabilities that are enabled.
• Leverage the KPIs of those business capabilities to demonstrate tangible and measurable value. Use terms and language that will resonate with senior leadership.

Don’t let measurement be an afterthought:

Start substantiating early on how you are going to measure success as your data governance program evolves.

Build a right-sized roadmap

Formulate an actionable roadmap that is right-sized to deliver value in your organization.

Key considerations:

• When building your data governance roadmap, ensure you do so through an enterprise lens. Be cognizant of other initiatives that might be coming down the pipeline that may require you to align your data governance milestones accordingly.
• Apart from doing your planning with consideration for other big projects or launches that might be in-flight and require the time and attention of your data governance partners, also be mindful of the more routine yet still demanding initiatives.
• When doing your roadmapping, consider factors like the organization’s fiscal cycle, typical or potential year-end demands, and monthly/quarterly reporting periods and audits. Initiatives such as these are likely to monopolize the time and focus of personnel key to delivering on your data governance milestones.

Sample milestones:

Data Governance Leadership & Org Structure Definition

Define the home for data governance and other key roles around ownership and stewardship, as approved by senior leadership.

Data Governance Charter and Policies

Create a charter for your program and build/refresh associated policies.

Data Culture Diagnostic

Understand the organization’s current data culture, perception of data, value of data, and knowledge gaps.

Use Case Build and Prioritization

Build a use case that is tied to business capabilities. Prioritize accordingly.

Business Data Glossary

Build and/or refresh the business’ glossary for addressing data definitions and standardization issues.

Tools & Technology

Explore the tools and technology offering in the data governance space that would serve as an enabler to the program. (e.g. RFI, RFP).