Further reading
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).
Key takeaways for effective business-driven data governance
Data governance leadership and sponsorship is key.
Ensure strategic business alignment.
Build and foster a culture of data excellence.
Evolve along the data journey.
Make data governance an enabler, not a hindrance.
Insight summary
Overarching insight
Your organization’s value streams and the associated business capabilities require effectively governed data. Without this, you face the impact of elevated operational costs, missed opportunities, eroded stakeholder satisfaction, and exposure to increased business risk.
Insight 1
Data governance should not sit as an island in your organization. It must continuously align with the organization’s enterprise governance function. It shouldn’t be perceived as a pet project of IT, but rather as an enterprise-wide, business-driven initiative.
Insight 2
Ensure your data governance program delivers measurable business value by aligning the associated data governance initiatives with the business architecture. Leverage the measures of success or KPIs of the underlying business capabilities to demonstrate the value data governance has yielded for the organization.
Insight 3
Data governance remains the foundation of all forms of reporting and analytics. Advanced capabilities such as AI and machine learning require effectively governed data to fuel their success.
Tactical insight
Tailor your data literacy program to meet your organization’s needs, filling your range of knowledge gaps and catering to your different levels of stakeholders.
When it comes to rolling out a data literacy program, there is no one-size-fits-all solution. Your data literacy program is intended to fill the knowledge gaps about data, as they exist in your organization. It should be targeted across the board – from your executive leadership and management through to the subject matter experts across different lines of the business in your organization.
Info-Tech’s methodology for establishing data governance
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1. Build Business and User Context
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2. Understand Your Current Data Governance Capabilities
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3. Build a Target State Roadmap and Plan
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| Phase Steps
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- Substantiate Business Drivers
- Build High-Value Use Cases for Data Governance
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- Understand the Key Components of Data Governance
- Gauge Your Organization’s Current Data Culture
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- Formulate an Actionable Roadmap and Right-Sized Plan
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| Phase Outcomes
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- Your organization’s business capabilities and value streams
- A business capability map for your organization
- Categorization of your organization’s key capabilities
- A strategy map tied to data governance
- High-value use cases for data governance
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- An understanding of the core components of an effective data governance program
- An understanding your organization’s current data culture
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- A data governance roadmap and target-state plan comprising of prioritized initiatives
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Blueprint deliverables
Each step of this blueprint is accompanied by supporting deliverables to help you accomplish your goals:
Data Governance Planning and Roadmapping Workbook
Use the Data Governance Planning and Roadmapping Workbook as you plan, build, roll-out, and scale data governance in your organization.
Data Use Case Framework Template
This template takes you through a business needs gathering activity to highlight and create relevant use cases around the organization’s data-related problems and opportunities.
Business Data Glossary
Use this template to document the key data assets that are to be governed and create a data flow diagram for your organization.
Data Culture Diagnostic and Scorecard
Leverage Info-Tech’s Data Culture Diagnostic to understand how your organization scores across 10 areas relating to data culture.
Key deliverable:
Data Governance Planning and Roadmapping Workbook
Measure the value of this blueprint
Leverage this blueprint’s approach to ensure your data governance initiatives align and support your key value streams and their business capabilities.
- Aligning your data governance program and its initiatives to your organization’s business capabilities is vital for tracing and demonstrating measurable business value for the program.
- This alignment of data governance with value streams and business capabilities enables you to use business-defined KPIs and demonstrate tangible value.
In phases 1 and 2 of this blueprint, we will help you establish the business context, define your business drivers and KPIs, and understand your current data governance capabilities and strengths.
In phase 3, we will help you develop a plan and a roadmap for addressing any gaps and improving the relevant data governance capabilities so that data is well positioned to deliver on those defined business metrics.
Info-Tech offers various levels of support to best suit your needs
DIY Toolkit
"Our team, has already made this critical project a priority, and we have the time and capability, but some guidance along the way would be helpful."
Guided Implementation
"Our team knows that we need to fix a process, but we need assistance to determine where to focus. Some check-ins along the way would help keeps us on track."
Workshop
"We need to hit the ground running and get this project kicked off immediately. Our team has the ability to take this over once we get a framework and strategy in place."
Consulting
"Our team does not have the time or the knowledge to take this project on. We need assistance through the entirety of this project."
Diagnostics and consistent frameworks are used throughout all four options.
Establish Data Governance project overview
Contact your account representative for more information. workshops@infotech.com 1-888-670-8889
| 1. Build Business and User context | 2. Understand Your Current Data Governance Capabilities | 3. Build a Target State Roadmap and Plan |
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| Best-Practice Toolkit |
- Substantiate Business Drivers
- Build High-Value Use Cases for Data Governance
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- Understand the Key Components of Data Governance
- Gauge Your Organization’s Current Data Culture
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- Formulate an Actionable Roadmap and Right-Sized Plan
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| Guided Implementation | | |
- Call 6
- Call 7
- Call 8
- Call 9
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| Phase Outcomes |
- Your organization’s business capabilities and value streams
- A business capability map for your organization
- Categorization of your organization’s key capabilities
- A strategy map tied to data governance
- High-value use cases for data governance
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- An understanding of the core components of an effective data governance program
- An understanding your organization’s current data culture
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- A data governance roadmap and target-state plan comprising of prioritized initiatives
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Guided Implementation
What does a typical GI on this topic look like?
A Guided Implementation (GI) is a series
of calls with an Info-Tech analyst to help implement our best practices in your organization.
A typical GI is between 8 to 12 calls over the course of 4 to 6 months.
Workshop overview
Contact your account representative for more information. workshops@infotech.com 1-888-670-8889
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Day 1
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Day 2
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Day 3
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Day 4
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Establish Business Context and Value
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Understand Current Data Governance Capabilities and Plot Target-State Levels
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Build Data Domain to Data Governance Role Mapping
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Formulate a Plan to Get to Your Target State
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| Activities
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- Establish business context, value, and scope of data governance at the organization
- Introduction to Info-Tech’s data governance framework
- Discuss vision and mission for data governance
- Understand your business architecture, including your business capability map and value streams
- Build use cases aligned to core business capabilities
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- Understand your current data governance capabilities and maturity
- Set target state data governance capabilities
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- Evaluate and prioritize performance gaps
- Develop and consolidate data governance target-state initiatives
- Define the role of data governance: data domain to data governance role mapping
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- Identify and prioritize next steps
- Define roles and responsibilities and complete a high-level RACI
- Wrap-up and discuss next steps and post-workshop support
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| Deliverables
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- Sample use cases (tied to the business capability map) and a repeatable use case framework
- Vision and mission for data governance
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- Current state of data governance maturity
- Definition of target state
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- Target-state data governance initiatives
- Data domain to data governance role mapping
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- Initialized roadmap
- Initialized RACI
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Phase 1
Build Business and User Context
“When business users are invited to participate in the conversation around data with data users and IT, it adds a fundamental dimension — business context. Without a real understanding of how data ties back to the business, the value of analysis and insights can get lost.”
– Jason Lim, Alation
This phase will guide you through the following activities:
- Identify Your Business Capabilities
- Define your Organization’s Key Business Capabilities
- Develop a Strategy Map that Aligns Business Capabilities to Your Strategic Focus
This phase involves the following participants:
- Data Governance Leader/Data Leader (CDO)
- Senior Business Leaders
- Business SMEs
- Data Leadership, Data Owners, Data Stewards and Custodians
Step 1.1
Substantiate Business Drivers
Activities
1.1.1 Identify Your Business Capabilities
1.1.2 Categorize Your Organization’s Key Business Capabilities
1.1.3 Develop a Strategy Map Tied to Data Governance
This step will guide you through the following activities:
- Leverage your organization’s existing business capability map or initiate the formulation of a business capability map, guided by info-Tech’s approach
- Determine which business capabilities are considered high priority by your organization
- Map your organization’s strategic objectives to value streams and capabilities to communicate how objectives are realized with the support of data
Outcomes of this step
- A foundation for data governance initiative planning that’s aligned with the organization’s business architecture: value streams, business capability map, and strategy map
Info-Tech Insight
Gaining a sound understanding of your business architecture (value streams and business capabilities) is a critical foundation for establishing and sustaining a data governance program that delivers measurable business value.
1.1.1 Identify Your Business Capabilities
Confirm your organization's existing business capability map or initiate the formulation of a business capability map:
- If you have an existing business capability map, meet with the relevant business owners/stakeholders to confirm that the content is accurate and up to date. Confirm the value streams (how your organization creates and captures value) and their business capabilities are reflective of the organization’s current business environment.
- If you do not have an existing business capability map, follow this activity to initiate the formulation of a map (value streams and related business capabilities):
- Define the organization’s value streams. Meet with senior leadership and other key business stakeholders to define how your organization creates and captures value.
- Define the relevant business capabilities. Meet with senior leadership and other key business stakeholders to define the business capabilities.
Note: A business capability defines what a business does to enable value creation. Business capabilities are business terms defined using descriptive nouns such as “Marketing” or “Research and Development.” They represent stable business functions, are unique and independent of each other, and typically will have a defined business outcome.
Input
- List of confirmed value streams and their related business capabilities
Output
- Business capability map with value streams for your organization
Materials
- Your existing business capability map or the template provided in the Data Governance Planning and Roadmapping Workbook accompanying this blueprint
Participants
- Key business stakeholders
- Data stewards
- Data custodians
- Data Governance Working Group
For more information, refer to Info-Tech’s Document Your Business Architecture.
Define or validate the organization’s value streams
Value streams connect business goals to the organization’s value realization activities. These value realization activities, in turn, depend on data.
If the organization does not have a business architecture function to conduct and guide Activity 1.1.1, you can leverage the following approach:
- Meet with key stakeholders regarding this topic, then discuss and document your findings.
- When trying to identify the right stakeholders, consider: Who are the decision makers and key influencers? Who will impact this piece of business architecture related work? Who has the relevant skills, competencies, experience, and knowledge about the organization?
- Engage with these stakeholders to define and validate how the organization creates value.
- Consider:
- Who are your main stakeholders? This will depend on the industry in which you operate. For example, customers, residents, citizens, constituents, students, patients.
- What are your stakeholders looking to accomplish?
- How does your organization’s products and/or services help them accomplish that?
- What are the benefits your organization delivers to them and how does your organization deliver those benefits?
- How do your stakeholders receive those benefits?
Align data governance to the organization's value realization activities.
Value streams enable the organization to create or capture value in the market in which it operates by engaging in a set of interconnected activities.
Info-Tech Insight
Your organization’s value streams and the associated business capabilities require effectively governed data. Without this, you face the possibilities of elevated operational costs, missed opportunities, eroded stakeholder satisfaction, negative impact to reputation and brand, and/or increased exposure to business risk.
Example of value streams – Retail Banking
Value streams connect business goals to the organization’s value realization activities.
Example value stream descriptions for: Retail Banking
Value streams enable the organization to create or capture value in the market in which it operates by engaging in a set of interconnected activities.
For this value stream, download Info-Tech’s Info-Tech’s Industry Reference Architecture for Retail Banking.
Example of value streams – Higher Education
Value streams connect business goals to the organization’s value realization activities.
Example value stream descriptions for: Higher Education
Value streams enable the organization to create or capture value in the market in which it operates by engaging in a set of interconnected activities.
For this value stream, download Info-Tech’s Industry Reference Architecture for Higher Education.
Example of value streams – Local Government
Value streams connect business goals to the organization’s value realization activities.
Example value stream descriptions for: Local Government
Value streams enable the organization to create or capture value in the market in which it operates by engaging in a set of interconnected activities.
For this value stream, download Info-Tech’s Industry Reference Architecture for Local Government.
Example of value streams – Manufacturing
Value streams connect business goals to the organization’s value realization activities.
Example value stream descriptions for: Manufacturing
Value streams enable the organization to create or capture value in the market in which it operates by engaging in a set of interconnected activities.
For this value stream, download Info-Tech’s Industry Reference Architecture for Manufacturing.
Example of value streams – Retail
Value streams connect business goals to the organization’s value realization activities.
Example value stream descriptions for: Retail
Value streams enable the organization to create or capture value in the market in which it operates by engaging in a set of interconnected activities.
For this value stream, download Info-Tech’s Industry Reference Architecture for Retail.
Define the organization’s business capabilities in a business capability map
A business capability defines what a business does to enable value creation. Business capabilities represent stable business functions and typically will have a defined business outcome.
Business capabilities can be thought of as business terms defined using descriptive nouns such as “Marketing” or “Research and Development.”
If your organization doesn’t already have a business capability map, you can leverage the following approach to build one. This initiative requires a good understanding of the business. By working with the right stakeholders, you can develop a business capability map that speaks a common language and accurately depicts your business.
Working with the stakeholders as described above:
- Analyze the value streams to identify and describe the organization’s capabilities that support them.
- Consider: What is the objective of your value stream? (This can highlight which capabilities support which value stream.)
- As you initiate your engagement with your stakeholders, don’t start a blank page. Leverage the examples on the next slides as a starting point for your business capability map.
- When using these examples, consider: What are the activities that make up your particular business? Keep the ones that apply to your organization, remove the ones that don’t, and add any needed.
Align data governance to the organization's value realization activities.
Info-Tech Insight
A business capability map can be thought of as a visual representation of your organization’s business capabilities and hence represents a view of what your data governance program must support.
For more information, refer to Info-Tech’s Document Your Business Architecture.
Example business capability map – Retail Banking
A business capability map can be thought of as a visual representation of your organization’s business capabilities and hence represents a view of what your data governance program must support.
Validate your business capability map with the right stakeholders, including your executive team, business unit leaders, and/or other key stakeholders.
Info-Tech Tip:
Leverage your business capability map verification session with these key stakeholders as a prime opportunity to share and explain the role of data and data governance in supporting the very value realization capabilities under discussion. This will help to build awareness and visibility of the data governance program.
Example business capability map for: Retail Banking
For this business capability map, download Info-Tech’s Industry Reference Architecture for Retail Banking.
Example business capability map – Higher Education
A business capability map can be thought of as a visual representation of your organization’s business capabilities and hence represents a view of what your data governance program must support.
Validate your business capability map with the right stakeholders, including your executive team, business unit leaders, and/or other key stakeholders.
Info-Tech Tip:
Leverage your business capability map verification session with these key stakeholders as a prime opportunity to share and explain the role of data and data governance in supporting the very value realization capabilities under discussion. This will help to build awareness and visibility of the data governance program.
Example business capability map for: Higher Education
For this business capability map, download Info-Tech’s Industry Reference Architecture for Higher Education.
Example business capability map – Local Government
A business capability map can be thought of as a visual representation of your organization’s business capabilities and hence represents a view of what your data governance program must support.
Validate your business capability map with the right stakeholders, including your executive team, business unit leaders, and/or other key stakeholders.
Info-Tech Tip:
Leverage your business capability map verification session with these key stakeholders as a prime opportunity to share and explain the role of data and data governance in supporting the very value realization capabilities under discussion. This will help to build awareness and visibility of the data governance program.
Example business capability map for: Local Government
For this business capability map, download Info-Tech’s Industry Reference Architecture for Local Government.
Example business capability map – Manufacturing
A business capability map can be thought of as a visual representation of your organization’s business capabilities and hence represents a view of what your data governance program must support.
Validate your business capability map with the right stakeholders, including your executive team, business unit leaders, and/or other key stakeholders.
Info-Tech Tip:
Leverage your business capability map verification session with these key stakeholders as a prime opportunity to share and explain the role of data and data governance in supporting the very value realization capabilities under discussion. This will help to build awareness and visibility of the data governance program.
Example business capability map for: Manufacturing
For this business capability map, download Info-Tech’s Industry Reference Architecture for Manufacturing.
Example business capability map - Retail
A business capability map can be thought of as a visual representation of your organization’s business capabilities and hence represents a view of what your data governance program must support.
Validate your business capability map with the right stakeholders, including your executive team, business unit leaders, and/or other key stakeholders.
Info-Tech Tip:
Leverage your business capability map verification session with these key stakeholders as a prime opportunity to share and explain the role of data and data governance in supporting the very value realization capabilities under discussion. This will help to build awareness and visibility of the data governance program.
Example business capability map for: Retail
For this business capability map, download Info-Tech’s Industry Reference Architecture for Retail.
1.1.2 Categorize Your Organization’s Key Capabilities
Determine which capabilities are considered high priority in your organization.
- Categorize or heatmap the organization’s key capabilities. Consult with senior and other key business stakeholders to categorize and prioritize the business’ capabilities. This will aid in ensuring your data governance future state planning is aligned with the mandate of the business. One approach to prioritizing capabilities with business stakeholders is to examine them through the lens of cost advantage creators, competitive advantage differentiators, and/or by high value/high risk.
- Identify cost advantage creators. Focus on capabilities that drive a cost advantage for your organization. Highlight these capabilities and prioritize programs that support them.
- Identify competitive advantage differentiators. Focus on capabilities that give your organization an edge over rivals or other players in your industry.
This categorization/prioritization exercise helps highlight prime areas of opportunity for building use cases, determining prioritization, and the overall optimization of data and data governance.
Input
- Strategic insight from senior business stakeholders on the business capabilities that drive value for the organization
Output
- Business capabilities categorized and prioritized (e.g. cost advantage creators, competitive advantage differentiators, high value/high risk)
Materials
- Your existing business capability map or the business capability map derived in the previous activity
Participants
- Key business stakeholders
- Data stewards
- Data custodians
- Data Governance Working Group
For more information, refer to Info-Tech’s Document Your Business Architecture.
Example of business capabilities categorization or heatmapping – Retail
This exercise is useful in ensuring the data governance program is focused and aligned to support the priorities and direction of the business.
- Depending on the mandate from the business, priority may be on developing cost advantage. Hence the capabilities that deliver efficiency gains are the ones considered to be cost advantage creators.
- The business’ priority may be on maintaining or gaining a competitive advantage over its industry counterparts. Differentiation might be achieved in delivering unique or enhanced products, services, and/or experiences, and the focus will tend to be on the capabilities that are more end-stakeholder-facing (e.g. customer-, student-, patient,- and/or constituent-facing). These are the organization’s competitive advantage creators.
Example: Retail
For this business capability map, download Info-Tech’s Industry Reference Architecture for Retail.
1.1.3 Develop a Strategy Map Tied to Data Governance
Identify the strategic objectives for the business. Knowing the key strategic objectives will drive business-data governance alignment. It’s important to make sure the right strategic objectives of the organization have been identified and are well understood.
- Meet with senior business leaders and other relevant stakeholders to help identify and document the key strategic objectives for the business.
- Leverage their knowledge of the organization’s business strategy and strategic priorities to visually represent how these map to value streams, business capabilities, and, ultimately, to data and data governance needs and initiatives. Tip: Your map is one way to visually communicate and link the business strategy to other levels of the organization.
- Confirm the strategy mapping with other relevant stakeholders.
Guide to creating your map: Starting with strategic objectives, map the value streams that will ultimately drive them. Next, link the key capabilities that enable each value stream. Then map the data and data governance to initiatives that support those capabilities. This is one approach to help you prioritize the data initiatives that deliver the most value to the organization.
Input
- Strategic objectives as outlined by the organization’s business strategy and confirmed by senior leaders
Output
- A strategy map that maps your organizational strategic objectives to value streams, business capabilities, and, ultimately, to data program
Materials
Participants
- Key business stakeholders
- Data stewards
- Data custodians
- Data Governance Working Group
Download Info-Tech’s Data Governance Planning and Roadmapping Workbook
Example of a strategy map tied to data governance
- Strategic objectives are the outcomes that the organization is looking to achieve.
- Value streams enable an organization to create and capture value in the market through interconnected activities that support strategic objectives.
- Business capabilities define what a business does to enable value creation in value streams.
- Data capabilities and initiatives are descriptions of action items on the data and data governance roadmap and which will enable one or multiple business capabilities in its desired target state.
Info-Tech Tip:
Start with the strategic objectives, then map the value streams that will ultimately drive them. Next, link the key capabilities that enable each value stream. Then map the data and data governance initiatives that support those capabilities. This process will help you prioritize the data initiatives that deliver the most value to the organization.
Example: Retail
For this strategy map, download Info-Tech’s Industry Reference Architecture for Retail.
Step 1.2
Build High-Value Use Cases for Data Governance
Activities
1.2.1 Build High-Value Use Cases
This step will guide you through the following activities:
- Leveraging your categorized business capability map to conduct deep-dive sessions with key business stakeholders for creating high-value uses cases
- Discussing current challenges, risks, and opportunities associated with the use of data across the lines of business
- Exploring which other business capabilities, stakeholder groups, and business units will be impacted
Outcomes of this step
- Relevant use cases that articulate the data-related challenges, needs, or opportunities that are clear and contained and, if addressed ,will deliver value to the organization
Info-Tech Tip
One of the most important aspects when building use cases is to ensure you include KPIs or measures of success. You have to be able to demonstrate how the use case ties back to the organizational priorities or delivers measurable business value. Leverage the KPIs and success factors of the business capabilities tied to each particular use case.
1.2.1 Build High-Value Use Cases
This business needs-gathering activity will highlight and create relevant use cases around data-related problems or opportunities that are clear and contained and, if addressed, will deliver value to the organization.
- Bring together key business stakeholders (data owner, stewards, SMEs) from a particular line of business as well as the relevant data custodian(s) to build cases for their units. Leverage the business capability map you created for facilitating this act.
- Leverage Info-Tech’s framework for data requirements and methodology for creating use cases, as outlined in the Data Use Case Framework Template and seen on the next slide.
- Have the stakeholders move through each breakout session outlined in the Use Case Worksheet. Use flip charts or a whiteboard to brainstorm and document their thoughts.
- Debrief and document results in the Data Use Case Framework Template
- Repeat this exercise with as many lines of the business as possible, leveraging your business capability map to guide your progress and align with business value.
Tip: Don’t conclude these use case discussions without substantiating what measures of success will be used to demonstrate the business value of the effort to produce the desired future state, as relevant to each particular use case.
Input
- Value streams and business capabilities as defined by business leaders
- Business stakeholders’ subject area expertise
- Data custodian systems, integration, and data knowledge
Output
- Use cases that articulate data-related challenges, needs or opportunities that are tied to defined business capabilities and hence if addressed will deliver measurable value to the organization.
Materials
- Your business capability map from activity 1.1.1
- Info-Tech’s Data Use Case Framework Template
- Whiteboard or flip charts (or shared screen if working remotely)
- Markers/pens
Participants
- Key business stakeholders
- Data stewards and business SMEs
- Data custodians
- Data Governance Working Group
Download Info-Tech’s Data Use Case Framework Template
Info-Tech’s Framework for Building Use Cases
Objective: This business needs-gathering activity will highlight and create relevant use cases around data-related problems or opportunities that are clear and contained and, if addressed, will deliver value to the organization.
Leveraging your business capability map, build use cases that align with the organization’s key business capabilities.
Consider:
- Is the business capability a cost advantage creator or an industry differentiator?
- Is the business capability currently underserved by data?
- Does this need to be addressed? If so, is this risk- or value-driven?
Info-Tech’s Data Requirements and Mapping Methodology for Creating Use Cases
- What business capability (or capabilities) is this use case tied to for your business area(s)?
- What are your data-related challenges in performing this today?
- What are the steps in this process/activity today?
- What are the applications/systems used at each step today?
- What data domains are involved, created, used, and/or transformed at each step today?
- What does an ideal or improved state look like?
- What other business units, business capabilities, activities, and/or processes will be impacted or improved if this issue was solved?
- Who are the stakeholders impacted by these changes? Who needs to be consulted?
- What are the risks to the organization (business capability, revenue, reputation, customer loyalty, etc.) if this is not addressed?
- What compliance, regulatory, and/or policy concerns do we need to consider in any solution?
- What measures of success or change should we use to prove the value of the effort (such as KPIs, ROI)? What is the measurable business value of doing this?
The resulting use cases are to be prioritized and leveraged for informing the business case and the data governance capabilities optimization plan.
Taken from Info-Tech’s Data Use Case Framework Template
Phase 2
Understand Your Current Data Governance Capabilities
This phase will guide you through the following activities:
- Understand the Key Components of Data Governance
- Gauge Your Organization’s Current Data Culture
This phase involves the following participants:
- Data Leadership
- Data Ownership & Stewardship
- Policies & Procedures
- Data Literacy & Culture
- Operating Model
- Data Management
- Data Privacy & Security
- Enterprise Projects & Services
Step 2.1
Understand the Key Components of Data Governance
This step will guide you through the following activities:
- Understanding the core components of an effective data governance program and determining your organization’s current capabilities in these areas:
- Data Leadership
- Data Ownership & Stewardship
- Policies & Procedures
- Data Literacy & Culture
- Operating Model
- Data Management
- Data Privacy & Security
- Enterprise Projects & Services
Outcomes of this step
- An understanding the core components of an effective data governance program
- An understanding your organization’s current data governance capabilities
Review: Info-Tech’s Data Governance Framework
Key components of data governance
A well-defined data governance program will deliver:
- Defined accountability and responsibility for data.
- Improved knowledge and common understanding of the organization’s data assets.
- Elevated trust and confidence in traceable data.
- Improved data ROI and reduced data debt.
- An enabling framework for supporting the ethical use and handling of data.
- A foundation for building and fostering a data-driven and data-literate organizational culture.
The key components of establishing sustainable enterprise data governance, taken from Info-Tech’s Data Governance Framework:
- Data Leadership
- Data Ownership & Stewardship
- Operating Model
- Policies & Procedures
- Data Literacy & Culture
- Data Management
- Data Privacy & Security
- Enterprise Projects & Services
Data Leadership
- Data governance needs a dedicated head or leader to steer the organization’s data governance program.
- For organizations that do have a chief data officer (CDO), their office is the ideal and effective home for data governance.
- Heads of data governance also have titles such as director of data governance, director of data quality, and director of analytics.
- The head of your data governance program works with all stakeholders and partners to ensure there is continuous enterprise governance alignment and oversight and to drive the program’s direction.
- While key stakeholders from the business and IT will play vital data governance roles, the head of data governance steers the various components, stakeholders, and initiatives, and provides oversight of the overall program.
- Vital data governance roles include: data owners, data stewards, data custodians, data governance steering committee (or your organization’s equivalent), and any data governance working group(s).
The role of the CDO: the voice of data
The office of the chief data officer (CDO):
- Has a cross-organizational vision and strategy for data.
- Owns and drives the data strategy; ensures it supports the overall organizational strategic direction and business goals.
- Leads the organizational data initiatives, including data governance
- Is accountable for the policy, strategy, data standards, and data literacy necessary for the organization to operate effectively.
- Educates users and leaders about what it means to be “data-driven.”
- Builds and fosters a culture of data excellence.
“Compared to most of their C-suite colleagues, the CDO is faced with a unique set of problems. The role is still being defined. The chief data officer is bringing a new dimension and focus to the organization: ‘data.’ ”
– Carruthers and Jackson, 2020
Who does the CDO report to?
- The CDO should be a true C- level executive.
- Where the organization places the CDO role in the structure sends an important signal to the business about how much it values data.
“The title matters. In my opinion, you can’t have a CDO without executive authority. Otherwise no one will listen.”
– Anonymous European CDO
“The reporting structure depends on who’s the ‘glue’ that ties together all these uniquely skilled individuals.”
– John Kemp, Senior Director, Executive Services, Info-Tech Research Group
Data Ownership & Stewardship
Who are best suited to be data owners?
- Wherever they may sit in your organization, data owners will typically have the highest stake in that data.
- Data owners need to be suitably senior and have the necessary decision-making power.
- They have the highest interest in the related business data domain, whether they are the head of a business unit or the head of a line of business that produces data or consumes data (or both).
- If they are neither of these, it’s unlikely they will have the interest in the data (in terms of its quality, protection, ethical use, and handling, for instance) necessary to undertake and adopt the role effectively.
Data owners are typically senior business leaders with the following characteristics:
- Positioned to accept accountability for their data domain.
- Hold authority and influence to affect change, including across business processes and systems, needed to improve data quality, use, handling, integration, etc.
- Have access to a budget and resources for data initiatives such as resolving data quality issues, data cleansing initiatives, business data catalog build, related tools and technology, policy management, etc.
- Hold the influence needed to drive change in behavior and culture.
- Act as ambassadors of data and its value as an organizational strategic asset.
Right-size your data governance organizational structure
- 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 as well as the maturity and size of the organization.
- Your data governance structure has to work for your organization, and it has to evolve as the organization evolves.
- Formulate your blend of data governance roles, committees, councils, and cross-functional groups, that make sense for your organization.
- Your data governance organizational structure should not add complexity or bureaucracy to your organization’s data landscape; it should support and enable your principle of treating data as an asset.
There is no one-size-fits-all data governance organizational structure.
Critical roles and responsibilities for data governance
Data Governance Working Groups
Data governance working groups:
- Are cross-functional teams
- Deliver on data governance projects, initiatives, and ad hoc review committees.
Data Stewards
Traditionally, data stewards:
- Serve on an operational level addressing issues related to adherence to standards/procedures, monitoring data quality, raising issues identified, etc.
- Are responsible for managing access, quality, escalating issues, etc.
Data Custodians
- Traditionally, data custodians:
- Serve on an operational level addressing issues related to data and database administration.
- Support the management of access, data quality, escalating issues, etc.
- Are SMEs from IT and database administration.
Example: Business capabilities to data owner and data stewards mapping for a selected data domain
Info-Tech Insight
Your organization’s value streams and the associated business capabilities require effectively governed data. Without this, you face elevated operational costs, missed opportunities, eroded stakeholder satisfaction, and exposure to increased business risk.
Enabling business capabilities with data governance role definitions
Operating Model
Your operating model is the key to designing and operationalizing a form of data governance that delivers measurable business value to your organization.
“Generate excitement for data: When people are excited and committed to the vision of data enablement, they’re more likely to help ensure that data is high quality and safe.”
– Petzold, et al., 2020
Operating Model
Defining your data governance operating model will help create a well-oiled program that sustainably delivers value to the organization and manages risks while building and fostering a culture of data excellence along the way.
Some organizations are able to establish a formal data governance office, whether independent or attached to the office of the chief data officer. Regardless of how you are organized, data governance requires a home, a leader, and an operating model to ensure its sustainability and evolution.
Examples of focus areas for your operating model:
- Delivery: While there are core tenets to every data governance program, there is a level of variability in the implementation of data governance programs across organizations, sectors, and industries. Every organization has its own particular drivers and mandates, so the level and rigor applied will also vary.
The key is to determine what style will work best in your organization, taking into consideration your organizational culture, executive leadership support (present and ongoing), catalysts such as other enterprise-wide transformative and modernization initiatives, and/or regulatory and compliances drivers.
- Communication: Communication is vital across all levels and stakeholder groups. For instance, there needs to be communication from the data governance office up to senior leadership, as well as communication within the data governance organization, which is typically made up of the data governance steering committee, data governance council, executive sponsor/champion, data stewards, and data custodians and working groups.
Furthermore, communication with the wider organization of data producers, users, and consumers is one of the core elements of the overall data governance communications plan.
Communication is vital for ensuring acceptance of new processes, rules, guidelines, and technologies by all data producers and users as well as for sharing success stories of the program.
Operating Model
Tie the value of data governance and its initiatives back to the business capabilities that are enabled.
“Leading organizations invest in change management to build data supporters and convert the skeptics. This can be the most difficult part of the program, as it requires motivating employees to use data and encouraging producers to share it (and ideally improve its quality at the source)[.]”
– Petzold, et al., 2020
Operating Model
Examples of focus areas for your operating model (continued):
- Change management and issue resolution: Data governance initiatives will very likely bring about a level of organizational disruption, with governance recommendations and future state requiring potentially significant business change. This may include a redesign of a substantial number of data processes affecting various business units, which will require tweaking the organization’s culture, thought processes, and procedures surrounding its data.
Preparing people for change well in advance will allow them to take the steps necessary to adapt and reduce potential confrontation. By planning for and efficiently communicating any changes that a data governance initiative may bring, many initial issues can be resolved from the outset.
Attempting to implement change without an effective communications plan can result in disagreements over data control and stalemates between stakeholder units. The recommendations of the governance group must reflect the needs of all stakeholders or there will be pushback.
- Performance measuring, monitoring and reporting: Measuring and reporting on performance, successes, and realization of tangible business value are a must for sustaining, growing, and scaling your data governance program.
Aligning your data governance to the organization's value realization activities enables you to leverage the KPIs of those business capabilities to demonstrate tangible and measurable value. Use terms and language that will resonate with your senior business leadership.
Info-Tech Tip:
Launching a data governance program will bring with it a level of disruption to the culture of the organization. That disruption doesn’t have to be detrimental if you are prepared to manage the change proactively and effectively.
Policies, Procedures & Standards
“Data standards are the rules by which data are described and recorded. In order to share, exchange, and understand data, we must standardize the format as well as the meaning.”
– U.S. Geological Survey
Policies, Procedures & Standards
- When defining, updating, or refreshing your data policies, procedures, and standards, ensure they are relevant, serve a purpose, and/or support the use of data in the organization.
- Avoid the common pitfall of building out a host of policies, procedures, and standards that are never used or followed by users and therefore don’t bring value or serve to mitigate risk for the organization.
- Data policies can be thought of as formal statements and are typically created, approved, and updated by the organization’s data decision-making body (such as a data governance steering committee).
- Data standards and procedures function as actions, or rules, that support the policies and their statements.
- Standards and procedures are designed to standardize the processes during the overall data lifecycle. Procedures are instructions to achieve the objectives of the policies. The procedures are iterative and will be updated with approval from your data governance committee as needed.
- Your organization’s data policies, standards, and procedures should not bog down or inhibit users; rather, they should enable confident data use and handling across the overall data lifecycle. They should support more effective and seamless data capture, integration, aggregation, sharing, and retention of data in the organization.
Examples of data policies:
- Data Classification Policy
- Data Retention Policy
- Data Entry Policy
- Data Backup Policy
- Data Provenance Policy
- Data Management Policy
Data Domain Documentation
Select the correct granularity for your business need
Sources:
Dataversity; Atlan; Analytics8
Data Domain Documentation Examples
Data Culture
“Organizational culture can accelerate the application of analytics, amplify its power, and steer companies away from risky outcomes.”
– Petzold, et al., 2020
A healthy data culture is key to amplifying the power of your data and to building and sustaining an effective data governance program.
What does a healthy data culture look like?
- Everybody knows the data.
- Everybody trusts the data.
- Everybody talks about the data.
Building a culture of data excellence.
Leverage Info-Tech’s Data Culture Diagnostic to understand your organization’s culture around data.
Contact your Info-Tech Account Representative for more information on the Data Culture Diagnostic
Cultivating a data-driven culture is not easy
“People are at the heart of every culture, and one of the biggest challenges to creating a data culture is bringing everyone into the fold.”
– Lim, Alation
It cannot be purchased or manufactured,
It must be nurtured and developed,
And it must evolve as the business, user,
and data landscapes evolve.
“Companies that have succeeded in their data-driven efforts understand that forging a data culture is a relentless pursuit, and magic bullets and bromides do not deliver results.”
– Randy Bean, 2020
Hallmarks of a data-driven culture
There is a trusted, single source of data the whole company can draw from.
There’s a business glossary and data catalog and users know what the data fields mean.
Users have access to data and analytics tools. Employees can leverage data immediately to resolve a situation, perform an activity, or make a decision – including frontline workers.
Data literacy, the ability to collect, manage, evaluate, and apply data in a critical manner, is high.
Data is used for decision making. The company encourages decisions based on objective data and the intelligent application of it.
A data-driven culture requires a number of elements:
- High-quality data
- Broad access and data literacy
- Data-driven decision-making processes
- Effective communication
Data Literacy
Data literacy is an essential part of a data-driven culture.
- Building a data-driven culture takes an ongoing investment of time, effort, and money.
- This investment will not realize its full return without building up the organization’s data literacy.
- Data literacy is about filling data knowledge gaps across all levels of the organization.
- It’s about ensuring all users – senior leadership right through to core users – are equipped with appropriate levels of training, skills, understanding, and awareness around the organization’s data and the use of associated tools and technologies. Data literacy ensures users have the data they need and they know how to interpret and leverage it.
- Data literacy drives the appetite, demand, and consumption for data.
- A data-literate culture is one where the users feel confident and skilled in their use of data, leveraging it for making informed or evidence-based decisions and generating insights for the organization.
Data Management
- Data governance serves as an enabler to all of the core components that make up data management:
- Data quality management
- Data architecture management
- Data platform
- Data integration
- Data operations management
- Data risk management
- Reference and master data management (MDM)
- Document and content management
- Metadata management
- Business intelligence (BI), reporting, analytics and advanced analytics, artificial intelligence (AI), machine learning (ML)
- Key tools such as the business data glossary and data catalog are vital for operationalizing data governance and in supporting data management disciplines such as data quality management, metadata management, and MDM as well as BI, reporting, and analytics.
Enterprise Projects & Services
- Data governance serves as an enabler to enterprise projects and services that require, use, share, sell, and/or rely on data for their viability and, ultimately, their success.
- Folding or embedding data governance into the organization’s project management function or project management office (PMO) serves to ensure that, for any initiative, suitable consideration is given to how data is treated.
- This may include defining parameters, following standards and procedures around bringing in new sources of data, integrating that data into the organization’s data ecosystem, using and sharing that data, and retaining that data post-project completion.
- The data governance function helps to identify and manage any ethical issues, whether at the start of the project and/or throughout.
- It provides a foundation for asking relevant questions as it relates to the use or incorporation of data in delivering the specific project or service. Do we know where the data obtained from? Do we have rights to use that data? Are there legislations, policies, or regulations that guide or dictate how that data can be used? What are the positive effects, negative impacts, and/or risks associated with our intended use of that data? Are we positioned to mitigate those risks?
- Mature data governance creates organizations where the above considerations around data management and the ethical use and handling of data is routinely implemented across the business and in the rollout and delivery of projects and services.
Data Privacy & Security
- Data governance supports the organization’s data privacy and security functions.
- Key tools include the data classification policy and standards and defined roles around data ownership and data stewardship. These are vital for operationalizing data governance and supporting data privacy, security, and the ethical use and handling of data.
- While some organizations may have a dedicated data security and privacy group, data governance provides an added level of oversight in this regard.
- Some of the typical checks and balances include ensuring:
- There are policies and procedures in place to restrict and monitor staff’s access to data (one common way this is done is according to job descriptions and responsibilities) and that these comply with relevant laws and regulations.
- There’s a data classification scheme in place where data has been classified on a hierarchy of sensitivity (e.g. top secret, confidential, internal, limited, public).
- The organization has a comprehensive data security framework, including administrative, physical, and technical procedures for addressing data security issues (e.g. password management and regular training).
- Risk assessments are conducted, including an evaluation of risks and vulnerabilities related to intentional and unintentional misuse of data.
- Policies and procedures are in place to mitigate the risks associated with incidents such as data breaches.
- The organization regularly audits and monitors its data security.
Ethical Use & Handling of Data
Data governance will support your organization’s ethical use and handling of data by facilitating definition around important factors, such as:
- What are the various data assets in the organization and what purpose(s) can they be used for? Are there any limitations?
- Who is the related data owner? Who holds accountability for that data? Who will be answerable?
- Where was the data obtained from? What is the intended use of that data? Do you have rights to use that data? Are there legislations, policies, or regulations that guide or dictate how that data can be used?
- What are the positive effects, negative impacts, and/or risks associated with the use of that data?
Ethical Use & Handling of Data
- Data governance serves as an enabler to the ethical use and handling of an organization’s data.
- The Open Data Institute (ODI) defines data ethics as: “A branch of ethics that evaluates data practices with the potential to adversely impact on people and society – in data collection, sharing and use.”
- Data ethics relates to good practice around how data is collected, used and shared. It’s especially relevant when data activities have the potential to impact people and society, whether directly or indirectly (Open Data Institute, 2019).
- A failure to handle and use data ethically can negatively impact an organization’s direct stakeholders and/or the public at large, lead to a loss of trust and confidence in the organization's products and services, lead to financial loss, and impact the organization’s brand, reputation, and legal standing.
- Data governance plays a vital role in building and managing your data assets, knowing what data you have, and knowing the limitations of that data. Data ownership, data stewardship, and your data governance decision-making body are key tenets and foundational components of your data governance. They enable an organization to define, categorize, and confidently make decisions about its data.
Step 2.2
Gauge Your Organization’s Current Data Culture
Activities
2.2.1 Gauge Your Organization’s Current Data Culture
This step will guide you through the following activities:
- Conduct a data culture survey or leverage Info-Tech’s Data Culture Diagnostic to increase your understanding of your organization’s data culture
Outcomes of this step
- An understanding of your organizational data culture
2.2.1 Gauge Your Organization’s Current Data Culture
Conduct a Data Culture Survey or Diagnostic
The objectives of conducting a data culture survey are to increase the understanding of the organization's data culture, your users’ appetite for data, and their appreciation for data in terms of governance, quality, accessibility, ownership, and stewardship. To perform a data culture survey:
- Identify members of the data user base, data consumers, and other key stakeholders for surveying.
- Conduct an information session to introduce Info-Tech’s Data Culture Diagnostic survey. Explain the objective and importance of the survey and its role in helping to understand the organization’s current data culture and inform the improvement of that culture.
- Roll out the Info-Tech Data Culture Diagnostic survey to the identified users and stakeholders.
- Debrief and document the results and scorecard in the Data Strategy Stakeholder Interview Guide and Findings document.
Input
- Email addresses of participants in your organization who should receive the survey
Output
- Your organization’s Data Culture Scorecard for understanding current data culture as it relates to the use and consumption of data
- An understanding of whether data is currently perceived to be an asset to the organization
Materials
Participants
- Participants include those at the senior leadership level through to middle management, as well as other business stakeholders at varying levels across the organization
- Data owners, stewards, and custodians
- Core data users and consumers
Contact your Info-Tech Account Representative for details on launching a Data Culture Diagnostic.
Phase 3
Build a Target State Roadmap and Plan
“Achieving data success is a journey, not a sprint.” Companies that set a clear course, with reasonable expectations and phased results over a period of time, get to the destination faster.”
– Randy Bean, 2020
This phase will guide you through the following activities:
- Build your Data Governance Roadmap
- Develop a target state plan comprising of prioritized initiatives
This phase involves the following participants:
- Data Governance Leadership
- Data Owners/Data Stewards
- Data Custodians
- Data Governance Working Group(s)
Step 3.1
Formulate an Actionable Roadmap and Right-Sized Plan
This step will guide you through the following activities:
- Build your data governance roadmap
- Develop a target state plan comprising of prioritized initiatives
Outcomes of this step
- A foundation for data governance initiative planning that’s aligned with the organization’s business architecture: value streams, business capability map, and strategy map
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/Catalog
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).
Recall: Info-Tech’s Data Governance Framework
Build an actionable roadmap
Data Governance Leadership & Org Structure Division
Define key roles for getting started.
Use Case Build & Prioritization
Start small and then scale – deliver early wins.
Literacy Program
Start understanding data knowledge gaps, building the program, and delivering.
Tools & Technology
Make the available data governance tools and technology work for you.
Key components of your data governance roadmap
By now, you have assessed current data governance environment and capabilities. Use this assessment, coupled with the driving needs of your business, to plot your data Governance roadmap accordingly.
Sample data governance roadmap milestones:
- Define data governance leadership.
- Define and formalize data ownership and stewardship (as well as the role IT/data management will play as data custodians).
- Build/confirm your business capability map and data domains.
- Build business data use cases specific to business capabilities.
- Define business measures/KPIs for the data governance program (i.e. metrics by use case that are relevant to business capabilities).
- Data management:
- Build your data glossary or catalog starting with identified and prioritized terms.
- Define data domains.
- Design and define the data governance operating model (oversight model definition, communication plan, internal marketing such as townhalls, formulate change management plan, RFP of data governance tool and technology options for supporting data governance and its administration).
- Data policies and procedures:
- Formulate, update, refresh, consolidate, rationalize, and/or retire data policies and procedures.
- Define policy management and administration framework (i.e. roll-out, maintenance, updates, adherence, system to be used).
- Conduct Info-Tech’s Data Culture Diagnostic or survey (across all levels of the organization).
- Define and formalize the data literacy program (build modules, incorporate into LMS, plan lunch and learn sessions).
- Data privacy and security: build data classification policy, define classification standards.
- Enterprise projects and services: embed data governance in the organization’s PMO, conduct “Data Governance 101” for the PMO.
Defining data governance roles and organizational structure at Organization
The approach employed for defining the data governance roles and supporting organizational structure for .
Key Considerations:
- The data owner and data steward roles are formally defined and documented within the organization. Their involvement is clear, well-defined, and repeatable.
- There are data owners and data stewards for each data domain within the organization. The data steward role is given to someone with a high degree of subject matter expertise.
- Data owners and data stewards are effective in their roles by ensuring that their data domain is clean and free of errors and that they protect the organization against data loss.
- Data owners and data stewards have the authority to make final decisions on data definitions, formats, and standard processes that apply to their respective data sets. Data owners and data stewards have authority regarding who has access to certain data.
- Data owners and data stewards are not from the IT side of the organization. They understand the lifecycle of the data (how it is created, curated, retrieved, used, archived, and destroyed) and they are well-versed in any compliance requirements as it relates to their data.
- The data custodian role is formally defined and is given to the relevant IT expert. This is an individual with technical administrative and/or operational responsibility over data (e.g. a DBA).
- A data governance steering committee exists and is comprised of well-defined roles, responsibilities, executive sponsors, business representatives, and IT experts.
- The data governance steering committee works to provide oversight and enforce policies, procedures, and standards for governing data.
- The data governance working group has cross-functional representation. This comprises business and IT representation, as well as project management and change management where applicable: data stewards, data custodians, business subject matter experts, PM, etc.).
- Data governance meetings are coordinated and communicated about. The meeting agenda is always clear and concise, and meetings review pressing data-related issues. Meeting minutes are consistently documented and communicated.
Sample: Business capabilities to data owner and data stewards mapping for a selected data domain
Info-Tech Insight
Your organization’s value streams and the associated business capabilities require effectively governed data. Without this, you face elevated operational costs, missed opportunities, eroded stakeholder satisfaction, and exposure to increased business risk.
Enable business capabilities with data governance role definitions.
Consider your technology options:
Make the available data governance tools and technology work for you:
- Data catalog
- Business data glossary
- Data lineage
- Metadata management
These are some of the data governance tools and technology players. Check out SoftwareReviews for help making better software decisions.
Make the data steward the catalyst for organizational change and driving data culture
The data steward must be empowered and backed politically with decision-making authority, or the role becomes stale and powerless.
Ensuring compliance can be difficult. Data stewards may experience pushback from stakeholders who must deliver on the policies, procedures, and processes that the data steward enforces.
Because the data steward must enforce data processes and liaise with so many different people and departments within the organization, the data steward role should be their primary full-time job function – where possible.
However, in circumstances where budget doesn’t allow a full-time data steward role, develop these skills within the organization by adding data steward responsibilities to individuals who are already managing data sets for their department or line of business.
Info-Tech Tip
A stewardship role is generally more about managing the cultural change that data governance brings. This requires the steward to have exceptional interpersonal skills that will assist in building relationships across departmental boundaries and ensuring that all stakeholders within the organization believe in the initiative, understand the anticipated outcomes, and take some level of responsibility for its success.
Changes to organizational data processes are inevitable; have a communication plan in place to manage change
Create awareness of your data governance program. Use knowledge transfer to get as many people on board as possible.
Data governance initiatives must contain a strong organizational disruption component. A clear and concise communication strategy that conveys milestones and success stories will address the various concerns that business unit stakeholders may have.
By planning for and efficiently communicating any changes that a data governance initiative may bring, many initial issues can be resolved from the outset.
Governance recommendations will require significant business change. The redesign of a substantial number of data processes affecting various business units will require an overhaul of the organization’s culture, thought processes, and procedures surrounding its data. Preparing people for change well in advance will allow them to take the necessary steps to adapt and reduce potential confrontation.
Because a data governance initiative will involve data-driven business units across the organization, the governance team must present a compelling case for data governance to ensure acceptance of new processes, rules, guidelines, and technologies by all data producers and users.
Attempting to implement change without an effective communication plan can result in disagreements over data control and stalemates between stakeholder units. The recommendations of the governance group must reflect the needs of all stakeholders or there will be pushback.
Info-Tech Insight
Launching a data governance initiative is guaranteed to disrupt the culture of the organization. That disruption doesn’t have to be detrimental if you are prepared to manage the change proactively and effectively.
Create a common data governance vision that is consistently communicated to the organization
A data governance program should be an enterprise-wide initiative.
To create a strong vision for data governance, there must be participation from the business and IT. A common vision will articulate the state the organization wishes to achieve and how it will reach that state. Visioning helps to develop long-term goals and direction.
Once the vision is established, it must be effectively communicated to everyone, especially those who are involved in creating, managing, disposing, or archiving data.
The data governance program should be periodically refined. This will ensure the organization continues to incorporate best methods and practices as the organization grows and data needs evolve.
Info-Tech Tips
- Use information from the stakeholder interviews to derive business goals and objectives.
- Work to integrate different opinions and perspectives into the overall vision for data governance.
- Brainstorm guiding principles for data and understand the overall value to the organization.
Develop a compelling data governance communications plan to get all departmental lines of business on board
A data governance program will impact all data-driven business units within the organization.
A successful data governance communications plan involves making the initiative visible and promoting staff awareness. Educate the team on how data is collected, distributed, and used, what internal processes use data, and how that data is used across departmental boundaries.
By demonstrating how data governance will affect staff directly, you create a deeper level of understanding across lines of business, and ultimately, a higher level of acceptance for new processes, rules, and guidelines.
A clear and concise communications strategy will raise the profile of data governance within the organization, and staff will understand how the program will benefit them and how they can share in the success of the initiative. This will end up providing support for the initiative across the board.
A proactive communications plan will:
- Assist in overcoming issues with data control, stalemates between stakeholder units, and staff resistance.
- Provide a formalized process for implementing new policies, rules, guidelines, and technologies, and managing organizational data.
- Detail data ownership and accountability for decision making, and identify and resolve data issues throughout the organization.
- Encourage acceptance and support of the initiative.
Info-Tech Tip
Focus on literacy and communication: include training in the communication plan.
Providing training for data users on the correct procedures for updating and verifying the accuracy of data, data quality, and standardized data policies will help validate how data governance will benefit them and the organization.
Leverage the data governance program to communicate and promote the value of data within the organization
The data governance program is responsible for continuously promoting the value of data to the organization. The data governance program should seek a variety of ways to educate the organization and data stakeholders on the benefit of data management.
Even if data policies and procedures are created, they will be highly ineffective if they are not properly communicated to the data producers and users alike.
There needs to be a communication plan that highlights how the data producer and user will be affected, what their new responsibilities are, and the value of that change.
To learn how to manage organizational change, refer to Info-Tech’s Master Organizational Change Management Practices.
Understand what makes for an effective policy for data governance
It can be difficult to understand what a policy is, and what it is not. Start by identifying the differences between a policy and standards, guidelines, and procedures.
The following are key elements of a good policy:
| Heading
|
Descriptions
|
| Purpose
|
Describes the factors or circumstances that mandate the existence of the policy. Also states the policy’s basic objectives and what the policy is meant to achieve.
|
| Scope
|
Defines to whom and to what systems this policy applies. Lists the employees required to comply or simply indicates “all” if all must comply. Also indicates any exclusions or exceptions, i.e. those people, elements, or situations that are not covered by this policy or where special consideration may be made.
|
| Definitions
|
Define any key terms, acronyms, or concepts that will be used in the policy. A standard glossary approach is sufficient.
|
| Policy Statements
|
Describe the rules that comprise the policy. This typically takes the form of a series of short prescriptive and proscriptive statements. Sub-dividing this section into sub-sections may be required depending on the length or complexity of the policy.
|
| Non-Compliance
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Clearly describe consequences (legal and/or disciplinary) for employee non-compliance with the policy. It may be pertinent to describe the escalation process for repeated non-compliance.
|
| Agreement
|
Confirms understanding of the policy and provides a designated space to attest to the document.
|
Leverage myPolicies, Info-Tech’s web-based application for managing your policies and procedures
Most organizations have problems with policy management. These include:
- Policies are absent or out of date
- Employees largely unaware of policies in effect
- Policies are unmonitored and unenforced
- Policies are in multiple locations
- Multiple versions of the same policy exist
- Policies managed inconsistently across different silos
- Policies are written poorly by untrained authors
- Inadequate policy training program
- Draft policies stall and lose momentum
- Weak policy support from senior management
Technology should be used as a means to solve these problems and effectively monitor, enforce, and communicate policies.
Product Overview
myPolicies is a web-based solution to create, distribute, and manage corporate policies, procedures, and forms. Our solution provides policy managers with the tools they need to mitigate the risk of sanctions and reduce the administrative burden of policy management. It also enables employees to find the documents relevant to them and build a culture of compliance.
Some key success factors for policy management include:
- Store policies in a central location that is well known and easy to find and access. A key way that technology can help communicate policies is by having them published on a centralized website.
- Link this repository to other policies’ taxonomies of your organization. E.g. HR policies to provide a single interface for employees to access guidance across the organization.
- Reassess policies annually at a minimum. myPolicies can remind you to update the organization’s policies at the appropriate time.
- Make the repository searchable and easily navigable.
- myPolicies helps you do all this and more.
myPolicies
Enforce data policies to promote consistency of business processes
Data policies are short statements that seek to manage the creation, acquisition, integrity, security, compliance, and quality of data. These policies vary amongst organizations, depending on your specific data needs.
- Policies describe what to do, while standards and procedures describe how to do something.
- There should be few data policies, and they should be brief and direct. Policies are living documents and should be continuously updated to respond to the organization’s data needs.
- The data policies should highlight who is responsible for the data under various scenarios and rules around how to manage it effectively.
Examples of Data Policies
Trust
- Data Cleansing and Quality Policy
- Data Entry Policy
Availability
- Acceptable Use Policy
- Data Backup Policy
Security
- Data Security Policy
- Password Policy Template
- User Authorization, Identification, and Authentication Policy Template
- Data Protection Policy
Compliance
- Archiving Policy
- Data Classification Policy
- Data Retention Policy
Leverage data management-related policies to standardize your data management practices
Info-Tech’s Data Management Policy:
This policy establishes uniform data management standards and identifies the shared responsibilities for assuring the integrity of the data and that it efficiently and effectively serves the needs of the organization.
This policy applies to all critical data and to all staff who may be creators and/or users of such data.
Info-Tech’s Data Entry Policy:
The integrity and quality of data and evidence used to inform decision making is central to both the short-term and long-term health of an organization.
It is essential that required data be sourced appropriately and entered into databases and applications in an accurate and complete manner to ensure the reliability and validity of the data and decisions made based on the data.
Info-Tech’s Data Provenance Policy:
Create policies to keep your data's value, such as:
- Only allow entry of data from reliable sources.
- Employees entering and accessing data must observe requirements for capturing/maintaining provenance metadata.
- Provenance metadata will be used to track the lifecycle of data from creation through to disposal.
Info-Tech’s Data Integration and Virtualization Policy:
This policy aims to assure the organization, staff, and other interested parties that data integration, replication, and virtualization risks are taken seriously. Staff must use the policy (and supporting guidelines) when deciding whether to integrate, replicate, or virtualize data sets.
Select the right mix of metrics to successfully supervise data policies and processes
Policies are only as good as your level of compliance. Ensure supervision controls exist to oversee adherence to policies and procedures.
Although they can be highly subjective, metrics are extremely important to data governance success.
- Establishing metrics that measure the performance of a specific process or data set will:
- Create a greater degree of ownership from data stewards and data owners.
- Help identify underperforming individuals.
- Allow the steering committee to easily communicate tailored objectives to individual data stewards and owners.
- Be cautious when establishing metrics. The wrong metrics can have negative repercussions.
- They will likely draw attention to an aspect of the process that doesn’t align with the initial strategy.
- Employees will work hard and grow frustrated as their successes aren’t accurately captured.
Policies are great to have from a legal perspective, but unless they are followed, they will not benefit the organization.
- One of the most useful metrics for policies is currency. This tracks how up to date the policy is and how often employees are informed about the policy. Often, a policy will be introduced and then ignored. Policies must be continuously reviewed by management and employees.
- Some other metrics include adherence (including performance in tests for adherence) and impacts from non-adherence.
Review metrics on an ongoing basis with those data owners/stewards who are accountable, the data governance steering committee, and the executive sponsors.
Establish data standards and procedures for use across all organizational lines of business
A data governance program will impact all data-driven business units within the organization.
- Data management procedures are the methods, techniques, and steps to accomplish a specific data objective. Creating standard data definitions should be one of the first tasks for a data governance steering committee.
- Data moves across all departmental boundaries and lines of business within the organization. These definitions must be developed as a common set of standards that can be accepted and used enterprise wide.
- Consistent data standards and definitions will improve data flow across departmental boundaries and between lines of business.
- Ensure these standards and definitions are used uniformly throughout the organization to maintain reliable and useful data.
Data standards and procedural guidelines will vary from company to company.
Examples include:
- Data modeling and architecture standards.
- Metadata integration and usage procedures.
- Data security standards and procedures.
- Business intelligence standards and procedures.
Info-Tech Tip
Have a fundamental data definition model for the entire business to adhere to. Those in the positions that generate and produce data must follow the common set of standards developed by the steering committee and be accountable for the creation of valid, clean data.
Changes to organizational data processes are inevitable; have a communications plan in place to manage change
Create awareness of your data governance program, using knowledge transfer to get as many people on board as possible.
By planning for and efficiently communicating any changes that a data governance initiative may bring, many initial issues can be resolved from the outset.
Governance recommendations will require significant business change. The redesign of a substantial number of data processes affecting various business units will require an overhaul of the organization’s culture, thought processes, and procedures surrounding its data. Preparing people for change well in advance will allow them to take the necessary steps to adapt and reduce potential confrontation.
Because a data governance initiative will involve data-driven business units across the organization, the governance team must present a compelling case for data governance to ensure acceptance of new processes, rules, guidelines, and technologies by all data producers and users.
Attempting to implement change without an effective communications plan can result in disagreements over data control and stalemates between stakeholder units. The recommendations of the governance group must reflect the needs of all stakeholders or there will be pushback.
Data governance initiatives will very likely bring about a level of organizational disruption. A clear and concise communications strategy that conveys milestones and success stories will address the various concerns that business unit stakeholders may have.
Info-Tech Tip
Launching a data governance program will bring with it a level of disruption to the culture of the organization. That disruption doesn’t have to be detrimental if you are prepared to manage the change proactively and effectively.
Additional Support
If you would like additional support, have our analysts guide you through other phases as part of an Info-Tech Workshop.
Contact your account representative for more information.
workshops@infotech.com 1-888-670-8889
To accelerate this project, engage your IT team in an Info-Tech workshop with an Info-Tech analyst team.
Info-Tech analysts will join you and your team at your location or welcome you to Info-Tech’s historic Toronto office to participate in an innovative onsite workshop.
The following are sample activities that will be conducted by Info-Tech analysts with your team:
Build Your Business and User Context
Work with your core team of stakeholders to build out your data governance strategy map, aligning data governance initiatives with business capabilities, value streams, and, ultimately, your strategic priorities.
Formulate a Plan to Get to Your Target State
Develop a data governance future state roadmap and plan based on an understanding of your current data governance capabilities, your operating environment, and the driving needs of your business.
Related Info-Tech Research
Key to building and fostering a data-driven culture.
Streamline your data management program with our simplified framework.
Be the voice of data in a time of transformation.
Research Contributors
| Name
|
Position
|
Company
|
| David N. Weber
|
Executive Director - Planning, Research and Effectiveness
|
Palm Beach State College
|
| Izabela Edmunds
|
Information Architect
|
Mott MacDonald
|
| Andy Neill
|
Practice Lead, Data & Analytics
|
Info-Tech Research Group
|
| Dirk Coetsee
|
Research Director, Data & Analytics
|
Info-Tech Research Group
|
| Graham Price
|
Executive Advisor, Advisory Executive Services
|
Info-Tech Research Group
|
| Igor Ikonnikov
|
Research Director, Data & Analytics
|
Info-Tech Research Group
|
| Jean Bujold
|
Senior Workshop Delivery Director
|
Info-Tech Research Group
|
| Rajesh Parab
|
Research Director, Data & Analytics
|
Info-Tech Research Group
|
| Reddy Doddipalli
|
Senior Workshop Director
|
Info-Tech Research Group
|
| Valence Howden
|
Principal Research Director, CIO
|
Info-Tech Research Group
|
Bibliography
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(Venn diagram recreated from 
80% satisfaction score, and the other list is CIOs with <80% satisfaction score.">
Industry Reference Architecture
Prioritized Opportunities for Transformation
Stakeholder Persona
1-Journey Use Case
Map Stakeholder Journey
(Source: Info-Tech Research Group, N=95)
(McKinsey, 2020)
