Data Governance

A healthy data governance program transcends process, organizational and system boundaries, providing protection for the lifeblood of any organization: data.

Data governance for
optimized data value

A tailored Data Governance program unlocks the latent value of your data for greater innovation, operational agility and improved organizational performance.

Datasource is your data governance partner

Leveraging our proven methodology and extensive experience in Data Governance, we can help you develop a roadmap tailored to your organization's requirements, ensuring internal support. We can also guide you around obstacles and utilize your strengths to deliver business value to your organization.

Business Value

Data as Lifeblood

Build for
Business as Usual

the Organization

1 Why data governance?

Enterprise Data Management (EDM) seems to be at the top of many organizations’ strategies for 2018, as the importance of data to organizations continues to grow exponentially. EDM plans may include modernizing an existing data warehouse to enable near real-time data, building a big data environment to support deeper analytics, focusing on and increasing digital capabilities and associated analytics, moving existing data and analytics to the cloud, increasing analytics capabilities in the organization, or most likely a combination of these.

Data governance is a key component of EDM, and is also taking on a higher level of importance. Some of the key trends that are causing a greater need for data governance include:

  • increasing data volumes from more and more sources, causing data inconsistencies that need to be identified and addressed, before decisions are made using incorrect information
  • more self-service reporting and analytics (data democratization), creating the need for a common understanding of data across the organization
  • the continuing impact of regulatory requirements such as GDPR, making it even more important to have a strong handle on what data is where, and how it’s being used
  • an increasing need for a common business language to enable cross-departmental analysis and decisions

Regardless of the type of data an organization is managing – data warehouse, data lakes, big data, etc., a strong data governance capability is important.  It will enable proactive management of data. 

Data governance used to be a nice to have, but due to the increasing focus and importance of data and analytics, it’s becoming a necessity that helps to drive data management across the enterprise.

Datasource helped us understand how to enhance the value of our data assets through Data Governance. They helped us establish our Data Governance program and worked alongside us to develop our metadata management process. Some of these practices were new to us but, with Datasource as a partner, we feel we now have a great start on all of them.”

BI Director, Kaiser Permanente

Data governance used to be a nice to have, but due to the increasing focus and importance of data and analytics, it’s becoming a necessity that helps to drive data management across the enterprise.

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Watch Nancy Couture, Datasource's SR. Director, Delivery Enablement Competency Director at the DGIQ Conference Discussing combining an initial assessment with implementation.


2 The Discovery Phase

Starting with a discover phase for your data governance initiative will provide you with a greater probability of success.

Data governance is the foundation for organization-wide data management. The DAMA DMBOK2 Framework shows data governance in the center for that very reason. Data management functions are more straightforward to implement if a data governance capability can be leveraged.

Regardless of the type of data an organization is managing – data warehouse, data lakes, big data, etc. – a strong data governance capability is important. It will enable proactive management of your data in support of your business strategy and vision.

The activities needed to initiate and successfully scale a data governance capability remain the same, regardless of the data environment:

Data governance is a key component of EDM, and is also taking on a higher level of importance. Some of the key trends that are causing a greater need for data governance include:

  • Discover
  • Implement
  • Scale
Taking the first step

Understanding the current data landscape, as well as what key business leaders envision, is the first step. Data governance can be leveraged for many purposes:

  • Data quality management
  • Developing standard business language and a business glossary for common data definitions
  • Data or report certification
  • Prioritization of data-related initiatives
  • Development and enforcement of various standards or policies
  • Regulatory compliance
  • Access and security

The discovery phase of a data governance initiative can help to identify the key focus areas. It may be that the organization starts with an initial purpose, then expands into other areas as the program takes hold.

Once the data governance focus areas are identified, identification of the key stakeholders can be completed. This information will then lead to a draft charter and a proposed organization and approach.

With the information gathered from the discovery phase, the data governance roadmap can be developed. In addition to the charter and proposed organization, it will include the implementation approach, timeline, staffing needs and communication strategy.

The discovery phase can be accomplished in a few short weeks, and enables a well-coordinated data governance implementation.

Working through the discovery phase with the desired future state in mind, the resulting roadmap will provide you with a greater probability of establishing a data governance capability that will scale in the long run.

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3The Five Components of a solid Data Governance Program

Data Governance is a key component to any company that wants to leverage the full value of their data. A solid Data Governance framework helps ensure quality, reduces risk, and establishes and reiterates best practices.

At Datasource Consulting, we look at Data Governance Programs as an evolution. We believe there are five critical elements to develop a solid framework for a Data Governance Program.

The journey through Data Governance Framework is an overview of the five components of a solid Data Governance Program.

  1. Understanding the Problem: how to understand the organization’s pain point and address them
  2. Strategy & Planning: developing a plan to focus on the pain points
  3. Organizing: how to recruit a dedicated cross-functional team of executive sponsors and governors who can help promote the message and do the hard work
  4. Communicating: using many vehicles to communicate the program, illustrate the value of what you’re doing, and showcase success
  5. Executing: deliver quick wins, early and often


Phase 1: Understanding the Problem

Before beginning a vacation or a journey, it is important to understand the goal of the trip.  For some, that may be relaxation while others may be seeking the thrill of an adventure.  Those of you on the Data Governance journey may be more focused on solving issues with quality, trust, integration, or how to categorize information for maximum efficiency.  Regardless of your objective, it is important to talk to executives and managers across the organization and understanding what pain points they’d like to solve.  This will become part of our Data Governance framework, so take notes.  Also, be aware that there are many different ways executives express their frustration, so keep your ears open and clarify what you’re hearing.

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Phase 2: Strategizing & Planning

With destination in hand (e.g. Paris, France), the next step in our journey is planning.  Large, multi-destination trips, present almost limitless opportunities to the traveler; therefore, many novice (and experienced) travelers will enlist the help of a professional travel agent.  The travel agent serves as a valuable resource and will present would-be travelers with many different options, coordinate and confirm all of the details about the upcoming trip for the traveler.  In a similar fashion, a Data Governance framework presents a wealth of options for organizations.  Datasource Consulting is a Data Governance Travel Agent for many enterprises.  During the Strategizing & Planning phase, we outline both strategic and tactical approaches focused on achieving the long-term goals.  We love data and would be happy to help manage the entire Data Governance framework for you.

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Phase 3: Organizing

Passports? Check!  Itinerary? Check!  Kevin? Where’s Kevin? Yes, a little Home Alone reference from back in the day.  However, this isn’t all that far off from reality.  Up to this point, we’ve listened and we understand the challenges that face our organization.  We’ve also planned and strategized.  Now, we need to organize and involve everyone that will be participating in the program.  The executive level and program sponsors will be our steering committee and are typically the best people to involve first.  We also need to create the Data Governance Council and include the people performing the hard, day to day work of Data Governance.  If any of these people are left out, the chances of us having a relaxing vacation in Paris…well, we’ll end up in the back of a moving truck with a Polka band. 

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Phase 4: Communicating

“Are we there yet?”  Many people get excited with the thought of taking a trip.  However, without frequent communication, time seems to slow down, especially for our children.  For the same reason, communication in your Data Governance Program is critical.  Not only is it important to involve executives and people who are allocating and committing resources to the Data Governance framework; it can be equally, if not more critical, to keep them informed of the progress and the victories.  Data Governance Programs extend over a period of time and program fatigue can be common.  Good communication leads to continued buy-in from the program sponsors.  The best way to alleviate program fatigue is to communicate by developing marketing materials, program updates, elevator pitches and the like.  For more tips on communicating the mission and goals of your Data Governance framework, please watch the  Data Governance Webinar video.

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Phase 5: Executing

We’ve finally arrived! This is where you deliver on the promises you are making through this program. The fun begins and we see the fruits of our labor. Take some time and ride the rides, enjoy those long walks on the beach and jump on the bed - everything a good vacation is meant to be - even losing the luggage. Ok, we know there will be obstacles during execution. The pros at Datasource Consulting have been through the process many times before and we know what obstacles may arise and how to overcome them. Allow us to help you with your Data Governance framework.

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4Can Data Governance be Agile?

Many organizations are now recognizing the need for data governance but are still struggling with the right way to structure it. A good approach: be agile!

The answer to this question is yes, data governance can definitely be agile. However, like the development of data management solutions, there does need to be some initial set up.

Data governance models can be loosely or highly structured. There are as many ways to “do” data governance as there are organizations “doing” it – and this is okay. The approach you decide to take should be one that correlates to your organization’s culture, data management maturity level, data governance objectives and desire for structure.

Some of the initial, foundational activities needed to initiate a data governance capability in your organization include:

  • A discovery phase to assess sentiment, identify stakeholders, identify opportunities (and business value) and focus areas, and start to develop goals and a data governance roadmap.
  • A foundational implementation phase to define the organization around data governance, communicate and educate stakeholders, secure executive support, and assign data stewards.

Following these initial steps, the data governance program can be set up as a highly-structured organization and set of defined processes with tools and templates, or it can be set up as a less structured team of individuals who work together to accomplish the goals and work through the roadmap.

The agile part can come into play regardless of which approach is taken. When we use the term “agile” for building a data warehouse, for example, we’re typically describing the iterative development that takes place. This can be achieved because the subject areas to be developed can be identified, prioritized, and broken into releases, and the releases can be broken into smaller iterations of work, or sprints. The same can be done with a Data Governance program. 

Many organizations will start on an endeavor to create a data dictionary, or develop Standard Business Language, or even define every data domain and assign a data steward accordingly for the entire organization. By taking this approach, it’s a whole lot of work up front without any value realization in the short term. Many of these initiatives eventually run out of steam.

An alternative, and more agile, approach is to identify smaller data governance initiatives based on strategic projects or business needs, and build from there. In this way, the organization can keep everyone apprised of progress and decisions, but the work effort is limited and focused. And the planned business value can be realized more quickly, thus increasing interest. Some may be concerned that taking a more agile approach to data governance may result in siloed decision-making. This is a risk, but if you set up your foundational phases with the “big picture” in mind, the risk is lessened. 

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5Build the Foundation for Sound Data Governance

With the focus that companies have on everything “data” – analytics, reporting, dashboards, Big Data, predictive analytics, decision science and so on – data governance is also increasing in importance. A good data governance program gets business stakeholders involved in deciding on data definitions and supporting or sponsoring consistent data usage across an organization.

Data governance models can be loosely or highly structured. Structured data governance typically entails forming a data governance council, a defined charter, identified data stewards, and prescribed policies and procedures. Data stewards “own” particular sets of data or systems and are responsible for determining associated business definitions and standards. The data governance council oversees the broader data governance process to ensure it is being managed and attended to on an ongoing basis. This is a great approach for companies that take their data quality seriously and are willing to invest time and resources to ensuring that quality.

One of the most common forms is “grassroots” data governance. The main requirement for success with data governance is to have broad business stakeholder representation and involvement. Participants need to be committed to making decisions, communicating these decisions to their organizations as appropriate, and bringing back their groups’ feedback. Ideally, they drive the data governance program; it may take contacted COOs across the company to get individuals assigned to participate. 

Having a successful data governance group is a foundational step in enabling agile development. The group becomes a core part of the agile development process, reviewing and approving deliverables every step of the way. It can continue to monitor and ensure that what is being built over time consistently reflects the business of the organization. 

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Data governance has a bad rap. Rightly or wrongly, people think that it’s just too much to take on, that it introduces additional – and unnecessary – layers of bureaucracy, that its procedures are onerous, confusing, and restrictive. In practice, unfortunately, this is frequently the case.

It doesn’t have to be.

Successful data governance has to do with how and where you begin. In Greek mythology, the goddess Athena emerged, fully grown, from Zeus’ head. Many organizations expect to pull off the same trick with data governance. They start big, with a top-down push to bring governance to all relevant domains. Initiatives of this kind can work, although – owing to their cost, complexity, and enterprise-wide scope – they’re more likely to drag on for years and run significantly over budget. Ultimately, they’re what gives data governance a bad name.

I like to think that there is a better, more pragmatic alternative. Instead of starting with a complete, multi-domain data governance program – i.e., a top-down-first approach – consider taking a cue from the agile project management world and start small. Look for projects, processes, or business domains that are good candidates for data governance. Start with the users or business areas that are experiencing the most pain. At the same time, cultivate a grassroots effort by emphasizing outreach to the lines of business and, crucially, tapping line-of-business experts for data stewardship and data ownership roles. The idea is that you’re bootstrapping your data governance program by growing it, iteratively, from the bottom-up. As your program grows, you’re codifying repeatable procedures that you will formalize as part of your organization’s core data governance policies. Once your bootstrapped data governance program has attained a sufficient degree of critical mass, it’s time to take it to the C-suite. Many enterprise-wide data governance programs started out just like this.

Here are a few practical guidelines for bootstrapping your data governance program.

Think globally, act locally. The impetus for data governance often begins at the grassroots level – i.e., with the people closest to the data. Think of the business users who work with and consume the reports, scorecards, dashboards, and analytic insights generated by the data warehouse as akin to canaries in the proverbial coal mine: they provide a kind of early-warning system for governance-related issues. A grassroots data governance effort usually begins with them. Reach out to managers and other important stakeholders on the business side. Ask them to identify the people in their functional or organizational areas who know the data best. Bingo: these are your grassroots data stewards. They can identify common pain points and pinpoint the types of errors they see on a daily, weekly or monthly basis, as well as ferret out hidden areas of risk. Bear in mind, too, that some on the business side -- the business subject matter experts (SME) and business analysts who know the data inside and out – should also be involved in any governance effort. SMEs and analysts are familiar with the data structures that are (supposed to be) instantiated in the data warehouse: they helped to codify them! On top of this, they have detailed knowledge about the nitty gritty of the business, from the source transaction points used as grist for the data warehouse’s fact tables to the manual processes by which data is entered into upstream systems. Rely on their expertise.

Line-of-business involvement is crucial for several reasons. First, it’s the best way to identify and redress common data governance problems. Second, it gives the (often frustrated) lines of business a voice. And not just a voice, either, but a kind of agency, too. Because data stewards occupy leadership roles in the data governance program, they’re empowered to actually do something about the governance issues that are at the root of their frustration.

Write it down: document and formalize your grassroots effort. Document the procedures you created when you bootstrapped your organization’s data governance program, starting with the criteria you used to identify and nominate data stewards and data owners. Your purpose is two-fold: in the first place, your organization can use this information to define the repeatable procedures it will instantiate as part of its core data governance policies. These policies will, in turn, form the foundation of its formal data governance program. Because the procedures your organization captures are, in a sense, empirical – i.e., they were reverse-engineered from the ground-up – the formal policies and procedures it creates from them will tend to be more robust. Second, package what you have into a presentation that you can use to build support among business leaders. Present what you have as a kind of “governance blueprint:”, a foundation for a formal, business-driven data governance structure.

Iterate. Iterate. Iterate. Data governance isn’t a monolith. Here as elsewhere, a pragmatic, iterative approach is in order: instead of trying to take on everything at once, focus on manageable, high-value projects, at least at first. (Wherever there’s governance-related pain, there’s potential value.) Look for quick wins, and build on your early successes.

Above all, continue with your grassroots approach. Map your core processes one at a time, starting, again, with the people who are closest to the data. Enlist the relevant business and IT stakeholders to determine what controls, if any, you’re using to govern how data is sourced, persisted, used, changed, and retained. Keep in mind, as well, that relatively routine business events – such as the launch of a new product – can provide both an impetus and an opportunity for remedying otherwise neglected governance issues.

Don’t boil the ocean. Every organization wants a data governance program that emanates from the C-level. This entails CEO- and CIO- level backing, along with the creation of completely new C-level positions, such as a Chief Data Officer or Chief Data Steward.

That’s the goal. Realizing that goal is another matter. By starting at the grassroots level and working iteratively, it’s possible to build out a robust bottom-up framework that addresses many of the key people, process, and procedural aspects of data governance.

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About Datasource Consulting

We are a consulting company that focuses exclusively on Enterprise Data Management and Business Intelligence, including both strategic and implementation services. We are experts in Data Architecture, Data Integration, Data Quality, Data Governance, Master Data Management Reporting & Analytics, and Program Management. We are passionate about data.



Lean on the data governance experts at Datasource Consulting for experienced guidance with building and strengthening your data governance program. We will tailor our expertise to fit your program needs. We can:

  • Assess where your organization will benefit most from data governance practices
  • Guide you in building a data governance program or enhance the one you have
  • Provide focus and leadership around specific governance practices to help you create the most value for your organization
  • Support you in building a stronger business and technology partnership



  • Assessments with a Data Governance 
  • Emphasis: identifying where your organization can benefit from data governance, and what capabilities/maturity level you currently have in governance practices
  • Business Case Development: explaining benefits of a governance program, and justifying the necessary funding
  • Program Plans with Detailed Roadmaps: mapping out how to deploy resources effectively across governance disciplines
  • Data Governance Procedures & Practices: detailed outlines of best practices to be followed by the governance organization
  • Financial & ROI Models: quantifying resources, costs and timelines, along with potential ROI



  • Compliance: ensuring your data meets regulatory, privacy and security demands. 
  • Metadata Management: knowing where your data is sourced, and ensuring the organization understand its meaning and usage.
  • Enterprise Architecture: ensuring you have an effective data strategy and your data assets are organized to optimize their utility in decision support.
  • Data Integration: enhancing the value of data assets by eliminating redundancy and bringing assets together in a unified view for enhanced analysis and reporting.
  • Master and Reference Data Management: managing all of the key data terms that allow your information consumers to aggregate, categorize and analyze their data for effective decisions.
  • Data Quality: ensuring completeness, accuracy, consistency and timeliness in your data assets.


If you’d like us to evaluate your current Data Governance Program, or help you with building a Data Governance Program for your company, please contact us through our website or call us at 888- 453-2624.