Your data governance team is facing a major challenge. What's the best way to tackle it?

We must first identify the challenges we face and then break them down into manageable pieces for effective resolution. Often, these challenges stem from a lack of vision, inadequate staffing, or uncertainty about where to begin. Starting with small steps, we can systematically address each challenge and identify process gaps within the business. Securing buy-in from the business is paramount for the success of Data Governance initiatives. Therefore, it's essential to develop a strategic plan that convincingly addresses these challenges and outlines a clear path forward. Through careful planning and communication, we can demonstrate the value of Data Governance and garner the necessary support from key stakeholders.

The correct answer to this question is 'it depends on the challenge'. However, in my experience most of the bigger challenges, such as a lack of stakeholder engagement or difficulty in getting leadership support, can all trace their origins to a failure to quantify and articulate the business value of a governance program. Collaborating with business stakeholders to model the expected business benefits of governance efforts, and then getting the stakeholders to commit to provide the resources and support needed to deliver them, will eliminate many of the bigger roadblocks governance leaders face in their governance efforts.

A key part of this step is to listen. Ask the hard questions to ascertain the current challenge. Hear the voices from every perspective so that you understand the quantum and magnitude of the challenge with as little bias as possible. In my experience, most challenges boil down to mismatched assumptions and expectations, so seeking an understanding of both sides helps to find anchor points for resolution.

This "powered by AI" generates peculiar categories. IMHO, when the team encounters a significant challenge, it indicates that data governance is already implemented and operational. So start here: 1. Identify the Issue: Clearly define the challenge your team is facing. Is it related to data quality, policy compliance, stakeholder engagement, or technology implementation? 2. Gather Information: Collect as much information as possible about the challenge. This may involve reviewing documentation, analyzing data, and discussing the issue with team members and stakeholders. 3. Assess Impact: Evaluate the impact of the challenge on the organization, including potential risks, affected areas, and the urgency of addressing it.

In previous projects, our data governance team faced a significant challenge of limited resources. We lacked dedicated people, budget, and a clear plan for maintaining an ongoing data governance program. We focused on 2 main actions: appoint data Stewards and automate processes. 1. We appointed data stewards, individuals responsible for specific data domains. These stewards collaborated closely with our team to ensure proper data access and security. For instance, the credit card data steward worked with us to grant access only to authorized people, preventing random data access. 2. To ease the burden on us, we embraced automation. Automated routine tasks like data quality checks, access control, and metadata management.

Structured remuneration and recognition of our data owners in the organisation. And why is this asking for 125 characters minimum? Isn't brevity the soul of wit?

My understanding and past experiences to tackle data governance is to avoid jumping into how to do it and what tools to use it, rather my approach has been three prongs: Firstly, understand ’Why’ see we doing it? What are the key business driver?who are the stakeholders? Understand organisation dynamics Secondly, understand ‘what’ are we doing. I will first stock-take of organisation data assets, understand data life cycle, identify key measure for success in areas such as people, process, technology and data quality. Thirdly, once the why and what are determined half the problem is solved, then you can focus on ‘How’ to carry out design and implementation of data governance framework, standard, governance roles & data management controls

Most importantly, define this vision and/or strategy in accordance with the local users (consumers and producers). It would avoid any type of clash regarding the coming recommendations or advices that you could formulate in your strategy document.

Defining the vision and goals for the data governance team is essential when tackling major challenges. By establishing a clear direction and objectives, team members can align their efforts, prioritize tasks, and work towards common goals, ensuring a focused and coordinated approach to overcoming the challenge.

Defining Vision is important however we should understand that Data Governance is a journey and embedding the Vision into Organization charter is very important. Alignment of Governance Goals should be aligned to Overall Data Strategy Goals to make it successful.

It’s important to clearly understand first what is the major challenge. It can be anything and the actions will be driven by the problem. So jumping to the solution is not the right approach, because each problem is unique and so should be solution.

Governance operates around People, Process and Technology. When establishing roles and responsibilities it's important to understand what additional roles Data Governance would need. Those roles should be called out. Also we should work with he change management team to ensure the communication and role out of Roles and Responsibilities is aligned

To tackle a major challenge, it's crucial for the data governance team to establish clear roles and responsibilities. This ensures that team members understand their duties, authority, and expectations, enabling efficient collaboration, decision-making, and accountability in addressing the challenge.

O Tema governança de dados está cada vez mais relevante para as companhias sendo necessário a criação e estruturação de um time multidisciplinar para atender o tema. Uma boa governança não se dá apenas em criar processos burocráticos e com maior segurança, acredito que o segredo está em compartilhar a responsabilidade com os donos dos projetos ou dados. Sendo assim não existe apenas um responsável e sim um grupo de pessoas/áreas responsáveis. “Burocratizar apenas para evitar tragédias”.

Many of the responsibilities and tasks assigned to the data governance roles are not completely new to the organisation. They are partly already in place, yet not officially assembled in one role. From my experience it is a helpful approach to indicate that data governance roles do not come out of the blue but are an evolution of data roles. This supports the acceptance in adopting the data governance roles.

The first step would be to create a corporate Data Governance policy. That way you make sure that the actual data owners at different levels in the organisation have a sufficiently strong mandate from Executive Management to take and use the ownership. Subsequently Data Owners supported by central and local teams can start defining Data Standards and policies for the specific Data domains, to guide the operational data management practices

Developing robust policies and standards is key to effectively addressing major challenges faced by the data governance team. Clear and comprehensive policies provide guidelines and frameworks for decision-making, behavior, and operations, facilitating consistency, compliance, and risk management in navigating the challenge.

Purpose: Establish guidelines and rules to govern how data is handled, ensuring consistency and compliance. Actions: Create comprehensive data governance policies covering data quality, security, privacy, and compliance. Establish standards for data naming conventions, classification, and storage. Involve legal and compliance teams to ensure policies align with regulations. Communicate and educate the team on these policies to ensure understanding and adherence.

La política nos ayuda a tener un esquema o definiciones globales que perduren en el tiempo y que tú data siempre esté actualizada y al alcance de todos tus usuarios. Y está no se tergiverse en el tiempo sin una previa evaluación del cambio

Remove manual implementations as much as needed and possibly try to agree on the policies and process that are relevant for the business and remove the blockers by defining the absolutely necessary requirements and design requirements well in advance and solve them quickly by respective dedicated data owners

Challenges through an opportunity! Major or Minor Challenge, if handled in methodical ways with right processes, the chances of addressing them is far higher. You should use these challenges to develop right processes not only to address the current but future possible challenging scenarios. In one of my case, Business Users were not very enthusiastic about using Data Catalog & Linage tool to identify the dependencies but rather relying their inherent knowledge to decide it. Which is absolutely fine to start with but validating it with actual data flows from system gives confidence & avoids last minute surprises. So we developed a process to have checkpoint before decision to validate with Data Governance team & it helped immensely.

Don't get tools before processes! Tools may seem helpful, but aren't if there aren't processes to maintain the tools. Remember, garbage in, garbage out!

Keep in mind communications and the “why” of what we are doing. Data is the lifeblood of AI, ML, and process optimization. Quality data fuels accurate insights, better decisions, and successful outcomes and reduces workloads on support teams. Poor data leads to flawed models, inefficiencies, and missed opportunities. As we leverage AI and ML to transform our work, governance ensures data is reliable, secure, and used ethically. By championing data quality and governance, we lay the foundation for innovation, competitive advantage, and sustainable growth in the age of AI and make the workplace an easier place to be.

Definitely this is the next ask after standards and policies are set up. A scalable and reliable tool will help get data profiling, cataloging done . Lineage is in need of time in data governance this helps quickly identify the root of problems. One thing to add here, the data governance tool having issue tracking capability is a plus .

If Tools do not represent and enhance processes and integration of enterprise data governance and decision making, they can a disincentivize accountability because of a lack of workflow visibility across technical and business silos. Adopt a simplified set of end - to end tools custodial and steward levels with a view toward continuous compliance and valued work flow support. Training with data governance tools should be complimentary to data governance process and better enable accountable and responsible parties to perform their work.

Developing objective success metrics and evaluating progress against these can be helpful to avoid biases in evaluating the progress of your data governance journey. But in the end, what matters is that business stakeholders acknowledge progress and recognize the programme is bringing value. Ensuring the proper governance bodies are in place at different levels of the organization is key to sharing experiences, evaluating progress in the different domains, and figuring out which practices can be rolled out to a broader scope. Analyzing and communicating successes and failures will help you adapt your roadmap as needed.

Continuously monitor the effectiveness of implemented solutions and adjust as necessary to overcome the challenge and encourage innovation, collaboration, and learning within the team to adapt to evolving challenges and drive ongoing improvement in data governance practices.

Very important to identify the root cause of the problem. Fish-bone diagram can be used to identify the problem, based on which improvement can be decided.

When facing a major challenge, the best approach for a data governance team is to evaluate and improve existing processes and practices. This involves conducting a thorough analysis of the root causes of the challenge, identifying areas for enhancement, and implementing targeted improvements to address the issue effectively.

Purpose: Continuously assess the effectiveness of your data governance efforts and make enhancements. Actions: Establish regular review cycles to evaluate data governance performance. Collect and analyze metrics related to data quality, security, and compliance. Solicit feedback from team members and stakeholders. Implement improvements based on findings, adapting to evolving requirements.

In my experience, the most overlooked step when trying to succeed with any governance initiative is solid stakeholder interviews and surveys. You have to deeply understand the pain and challenges of the people and teams you wish to impact. At the end of the day, governance metrics are secondary to business metrics, so you might as well align them now! The second biggest mistake is ignoring the results of your interviews and surveys in favor of assumed challenges. For example, even if you know data quality is (and always will be) something to work on, but your stakeholders are saying data discoverability is their biggest roadblock, you HAVE to respond to their challenges. Otherwise, you risk losing all momentum and adoption.

All steps are important to go through depending on the actual data governance challenge. The impact that the data management, data governance or AI related challenge has on the value provided to stakeholders needs to be assessed and with that a fitting evaluation and improvement action will be determined. A challenge can be positive and provide insights to improve data governance moving forward leading to even better results. The team should not worry but be eager to embrace the challenge and make a success story out of the challenge thus adding value!

Don’t boil the ocean! - Attack small/tangible items that can jumpstart your program - Tie these efforts to actual business challenges/problems - Show value, and communicate progress broadly across the organization - Connect the dots (people/process/technology) - Did I stress the importance of communication enough? #VicianaData🔥

Acredito que o pilar fundamental de um programa de governança de dados, são as pessoas! Trabalhar na gestão de mudanças, capacitar e empoderar os roles ajuda muito a empreender um programa tão transformacional como é a governança de dados. Tão importante quanto o anterior, está o alinhamento com os objetivos de negócio, seleccionar um caso de negócio em que se possa gerar a percepção de valor no curto prazo. Definir indicadores e monitora-los de forma constante também nos permite identificar o que não está funcionando, e fazer os ajustes necessários.

To enhance data governance framework effectively, focus should be on fostering data literacy across the organization, continuously monitor and improve data quality metrics, ensure strict compliance with privacy and security laws, invest in scalable technology and infrastructure, and encourage innovation and best practices sharing. In my opinion, these steps are crucial for building a resilient, comprehensive data governance strategy that supports strategic decision-making and operational excellence.