You’re working on a data governance project. What’s the best way to get everyone involved?

Since Data Governance is one discipline in the enterprise data management realm, it is important to start with an assessment of the organization's capabilities (and challenges) in each data management function - not just data governance. Data Governance is aligned with the functions, so the goals for DG should support other functions (metadata, data quality, data security, etc.) Doing this will help develop the organization's vision for data governance and data management - and identify relevant stakeholders and roles.

ENGAGEMENT is more important than INVOLVEMENT in data governance (DG). What is DG? What & who does DG? How do we get to the future state? What does DG look like? If answers to these questions are known to everyone, then it is easy to involve & engage, which can be done by communicating - 1. Initiation: Leadership approval/awareness, DG team structure/setup 2. Definition: Clear/brief definition of DG and what data is to be managed? 3. Scope: Initial scope/plan for the definition & rollout of the DG program? 4. Assessment: To understand if the organization is positioned to manage data or information as an asset 5. Vision & Mission: To convey understanding and comprehension of what DG means and what the organization wants to do to get there

Often Data Teams face a problem of having a robust Data Governance and this is not a task that can be achieved by the effort of a handful of the team members. It's an absolute need that the whole team slowly but steadily develops the behaviour of improving on the existing data guidelines. A few pointers to get started on this journey: 1. Start documenting the metadata of the table that you own and provide examples on how to use the table 2. Implement the basic DQ checks 3. Try creating data lineage to understand downstream impacts 4. Democratise the design doc While taking up any tasks do consider the effort you would be spending in these and give an estimate so you don't feel these to be a by product.

Setting up DG in an organization is a monumental task. Understanding the key goals and outcomes from each of the stakeholders will help in creating and implementing a successful DG. The goals will need to be revisited on regular basis and has to be adjusted during the course of the program to achieve successful results. Maturing a DG in a large organization would be a multi year effort. Goals and objectives have to be created understanding what is in hand and how it can be achieved successfully.

Data governance is a massive undertaking and requires engagement organization-wide. It can easily seem too daunting to start. Start slow, demonstrate value, and expand over time. One approach is identifying key metrics, business policies, or business processes that already have high engagement and/or are business critical. This will help you get commitment and support from stakeholders. Prioritize one and start breaking it down further to understand current state (data sources, methods of ingestion, transformations, ownership etc.) until you arrive at the individual data elements you need to govern. With this defined scope, you can begin to work towards an ideal state of that one process or metric and use it as a proof of concept.

First of all, do not get everyone involved. Identify the key roles/actors at various levels. Build understanding of the key business goals for the company, and the department you are working with. Align on how data and certain data governance activties can help in achieving those business goals. When you are doing all these, people are going to be working on achieving the business goals of their departments, rather than data governance which may or not may contribute directly to their business goals. This helps in channelizing their time, and efforts by prioritizing their business goals and thus getting their alignment and commitment.

A detailed maturity assessment looks impressive, but its value is really in informing the project on "readiness/quantum of change" for achieving the vision/future state. Understanding the sticking points or limit change possible on the trio of People, Process, Technology. You are answering the question: How do we best design or incrementally implement this vision given the current state?

Yeah, this is a popular one with those who tend to measure their value in the sound and reverb of their Data Strategy Assessment hitting the table. Of course, you need to know where you are today - but this isn't step 2. This is a data point. How about identifying where those "business goal pucks" are landing to figure out where you need to skate to and what processes and capabilities you need to enable that business outcome. That's an outside-in approach. Connecting the dots to what you have, and that "fit-gap" is not where you start, it's what informs what is needed. Are you following me yet? If not, no problem, start now. My best is yet to come, and I guarantee that I will work my hardest to make sure you achieve your best too!

Esto es extremadamente necesario, por que te da un punto de partida, pero es necesario asesorarse sobre la gobernanza del dato y hacer una bajada a toda la compa?ía antes de esta evaluación para que la respuesta sean más certeras. Hoy muchas compa?ías de una u otra forma ejecutan actividades que son parte de la Gobernanza del dato sin saberlo.

The framework you select is less important than the engagement you can create to carry out your assessment. It will be important to establish a baseline to measure progress against it. The toughest part maybe to 'honesty' as some stakeholders may not want to look as bad as they really are.

Once you have a defined roles for your data governance team, getting them involved into a single project won’t be an issue, especially when everyone has a specific part to focus on, and once the person finishes will hand it over to the next person depending on the role and project workflow

Securing buy-in for a data governance project requires a multi-pronged approach. We begin by establishing a Data Governance Council led by the Chief Data Officer (CDO), ensuring high-level support and strategic direction. Next, we engage Data Providers who understand data creation and flow to establish ownership and accountability. Finally, involving Data Stewards from relevant departments and Data Users through workshops and feedback sessions fosters collaboration and addresses their specific needs. By clearly defining these roles, we build a comprehensive team with diverse perspectives, driving project success.

Data Governance is like a team sport. The idea is to encourage each data citizen to participate without overwhelming their boundaries thereby easing their day to day initiatives. The idea is to define their role in the arena of governance thereby playing by their strengths and supported on blind sides by colleagues. RACI or role based participation is a key to success. Check out #Collibra to know further.

Don't forget the "when" in the midst of defining roles and responsibilities. Even "best practice DG" will fail if the organisation isn't ready for the changes to achieve an idealised future state. Be practical about which R&R are needed now, whilst still getting agreement about the R&R that will need to come eventually. Like pursing a Minimally Viable Product, you might need to prove the value of the initiative with a scaled-down version before getting enterprise-wide.

If you can't find owners for key areas (like your data domains and stewards)... brace yourself! no matter how well written your roles and RASIC is, you risk becoming 'that other meeting we have to sit through'. Owners (ideally in the business) need to drive your agenda... responsibilities can be more flexibly evolve to adjust to serve the business priorities.

Engaging with all key stakeholders and securing executive support is key as Data Governance is a transformational journey and not just a project or a tool. You will need funding, resources, effective change management and ability to influence ways of working across the organization. The sooner you engage with key stakeholders to understand their vest interest or opposition to that journey and what does success look like for them, the better.

I haven’t seen a well-succeeded DG project without support from top down. Securing executive sponsorship and engaging leadership is critical for the visibility and success of any data governance project. By involving leaders who advocate for the project and by identifying champions within different departments, you create a network of influencers who can promote a culture of data stewardship. This approach encourages everyone to view data as a valuable asset for which they share responsibility, enhancing its accuracy, privacy, and security. These strategies ensure that the initiative is not only supported at the top level but is also embraced across the organization, fostering a unified commitment to the project's goals.

Active stakeholder engagement is key for any successful implementation. Understanding the goals of each stakeholder and coming up with a common goal is instrumental. Structured communications with regular key highlights of the success stories and discuss in detail on the opportunities will help a lot. We also need to work closely on the priorities of the stakeholders and align.

Creo que es importante tener el o los Sponsor correctos para implementar la Gobernanza del dato, una correcta bajada comunicacional que la compa?ía entienda que un beneficio para todos y que se necesita trabajo en forma colaborativa.

In order to maintain stakeholder involvement in a data governance, it is crucial to establish clear roles and responsibilities, foster effective communication channels, provide regular updates and progress reports, encourage collaboration and engagement, tailor communication to stakeholders' needs, offer training and education, and recognize and celebrate achievements. These practices ensure that everybody remain informed, empowered, and invested in the program.

I have found that in order to maintain an effective data governance structure you must first understand 1.What data you have. 2.Whose data you have 3.Where it is located And 4. Automate controls to monitor data use and leakage. Todays data tools have far reaching capabilities in governing structured and unstructured data

Data governance frameworks simplify the way that you communicate about your implementation. Choosing a framework early creates a common language around data governance. This common language simplifies meetings and makes it easy to onboard new stakeholders in your process. Many businesses will adopt a framework, then cut out parts of the framework that they find difficult to follow. Resist this temptation, as you’ll likely find that those difficult parts were included for a reason and by cutting them out, you’ve crippled your implementation.

Keeping with my "bring them along the journey" principle, test these frameworks and best practices with stakeholders. Get buy-in through communicating the reason and implications of these designs. Quite often it isn't until this stage that stakeholders truly understand how DG will practically impact them. You'll need to confirm that all concerns regarding business change impact have been addressed before proceeding.

Frameworks. Target Operating Models. Triangles, Funnels, Staircases, and more #DoneWithPowerpoint This isn't rocket science folks - we need to stop making it so complicated. I can do complicated. I can create the best looking powerpoint data governance framework you have ever seen. But it is going to move the needle? Is that really going to provide the business, technical teams, and senior leadership the competetive advantage and roadmap to close the backlog gap and start skating to where the puck is GOING to be, instead of barely keeping up with the technical debt they have either inherited, or continue to contribute to? Let's focus on DOING and LEARNING. If you keep doing what you have been doing why do you think anything will change?

Communication, Communication, Communication! While data is NOT a project, in my experience the biggest risk to change being successfully embedded in a business is not communicating enough. People can't get engaged if they don't know about the initiative and people are critical to delivering data success....

To successfully implement and improve a data governance framework, engage stakeholders from the start, explaining the benefits and their roles. Communicate goals and processes clearly, tailoring messages to different departments. Provide targeted training to ensure understanding and compliance. Initiate with pilot projects to demonstrate real benefits, building momentum. Establish measurable objectives and KPIs, and share these metrics regularly to track progress. Create feedback channels for continuous input. Regularly review and refine the framework . Acknowledge and reward significant contributions to foster a culture of accountability and continuous improvement. This approach effective implementation and improvement.

As in any project, it begins with the leadership—getting its support, commitment, (and budget ??). The leadership must be able to articulate the “why” behind the program. Then, of course, involve the business units and data owners; remember, data governance is a business initiative more than a tech one. Hence, the business units can help make sure the framework works for what they really need. Another must is data literacy; it helps people see data as an asset and why proper data management and governance matter. Finally, show proof of value. Rinse and repeat.

Data Governance is not a project, it's a journey. And it starts with building trust and engagement among different teams within an organization. People across teams need to understand the impact of data on decision-making and how governing it efficiently adds value to it. Start with workshops, coffee conversations or anything that allows people to talk freely about their data challenges and opportunities.

At first, data governance should be a program - long term ongoing process, not a project that has a defined completion deliverables and date. The best way to get everyone really involved in data governance is to ensure support from the executive management and make sure that performance review (and salary increase as well as bonus) of every participant includes person's performance on the data governance program.

The best way to get teams excited about the organization's data governance implementation is to start with more achievable successes and showcase the potential in terms of larger implementation. Real scenarios have better impact.

First, data governance isn't a project; it is a program. One that doesn't ever end. As for getting everyone involved, it comes down to instituting a framework, and an operating model that reflects that framework. This includes identifying owners and stewards using data governance standards, guidelines and best practices. All too often, organizations utilize the "...well, this one's breathing..." approach to stewardship instead of using a winnowing approach to pick the best candidates. Find those that are passionate about making a difference and addressing the gaps and challenges, who are social players that can articulate reasons why governance is a good thing that can bring about good changes based on other's valued opinions.