Data governance leadership is hard. Can you adapt to the most common challenges?

In my experience, the idea of 'adaptability' within a data governance function must necessarily embrace the idea that context matters. In other words, your governance strategy, framework, and operating model must all support the idea that that governance policies can vary across the organization across three levels: 1) functional/domain centric policies, 2) cross functional policies, and 3) enterprise-wide policies. Adaptability comes as a result of the ability to define, implement, and manage/enforce policies which differ across these levels. This will require a highly flexible operating model that federates some policy decisions, while also centralizing others.

La gouvernance des données est un défi complexe qui nécessite une approche adaptative. Pour relever ce défi, il est essentiel de comprendre les besoins métier et de communiquer efficacement les initiatives de gouvernance des données aux différentes parties prenantes. Favoriser une culture de la données au sein de l'entreprise, impliquer les salariés de l'entreprise à tous les niveaux et offrir une formation sur les bonnes pratiques de gouvernance des données sont des étapes clés. De plus, investir dans des nouvelles technologies, évaluer régulièrement l'efficacité de ces incitatives et ajuster la stratégie en conséquence sont des aspects importants pour maintenir une gouvernance des données efficace dans un contexte en constante évolution.

Lier la gouvernance des données avec les objectifs de l'entreprise ainsi que montrer les progès effectués sur la gestion des processus grace aux progrès de la qualité des master data est clef pour l'entreprise et la réussite de l'initiative. Montrer également les soucis de reporting et donc de prises de décisions dus à des données erronées est aussi une approche qui fonctionne

Aligning data governance with business goals requires understanding these objectives and translating them into tangible data requirements. Key strategies include effective engagement and ongoing communication with stakeholders, implementing data governance initiatives with business goals in mind, and maintaining flexibility to adapt to evolving business objectives. Regular review and updates of data governance strategies are also crucial to ensure continued alignment with the business needs.

This is where data governance council plays a major role . We used to steering committee and change review board meetings where all business stakeholders and IT stakeholders contribute to resolve the conflict and prioritise the business goals which are important to the enterprise. Data governance needs sponsorship to implement any change so you need buy in from all stakeholders to implement the change which aligns business goals

Different departments have different needs and priorities concerning data, leading to potential conflicts and challenges in implementing a unified data governance strategy. Despite these challenge, effective data governance leadership can bring significant benefits to an organization. These include improved data quality, better decision-making, increased efficiency, and enhanced compliance with regulations. So, while it's a demanding role, it can also be a very rewarding one.

I believe Data Governance is a push for a big change and change is always hard be it customers, employees or leaders. The main thing to keep in mind while strategizing about Data Governance is to expect resistance and push back from multiple stakeholders and to have action plan ready to make everyone part of big shift. Training and education is key for creating awareness and data centric mindset. It should be part of organizational mandatory trainings and employment goals at organizational level to make an impactful change where everyone see themselves as change agent for more better tomorrow with reliable and clean data.

My perspective is without data governance data change can be chaotic it provides a data risk management framework which helps all parties involved . Change management should be open , discussed and explained so everyone understands the rational and everyone can contribute or input which mitigates the resistance.

My experiences have shown that there are some keys to managing change as part of Data Governance. 1) Start thinking about change management on day one by identifying how successful changes have been implemented in the past and learn from that. 2) Too many times I have seen Data Governance as an IT-only project. I have not seen a company yet that takes well to IT telling the business how to change. Work together. 3)Resistance can come from your data champions because it just piles on to their overloaded workload. Recognize this and work to alleviate the pressure.

Typically the biggest resistance to change will come from the middle management layer. The executive team understands the efficiency gains and regulatory purpose of data governance. The analysts are agnostic to process, and will contribute in whatever way they are instructed. Middle management will have concerns about team KPIs, productivity decrease during the change and that will impact them both short and long term. Your must support the management layer in every way possible and enable them to succeed. Educate them on the purpose of the change, and provide them them with the resources to enact the changes with constant support during the transition period.

Navigating the complexities of data governance requires a nuanced approach that strategically balances centralization and decentralization. Opt for a hybrid model that leverages the strengths of both, employing a centralized framework for overarching policies and standards while empowering decentralized units with the autonomy to innovate within those guidelines. This approach ensures consistency in data quality and compliance across the organization, while also fostering agility and responsiveness to department-specific needs. Regularly review and adjust governance structures to align with evolving business goals and technological advancements, ensuring optimal performance and stakeholder satisfaction.

I’ve written research on this while at Gartner, and I’ve worked with 100s of data teams on understanding this balance. No org will be exactly the same. Embrace the mindset of data trust can be as effective as command and control. What do I mean? The hesitation to decentralize usually comes from a place of fear that decentralized teams won’t know how to properly use data. However, I would argue that if data was well described, had clearly defined ownership, and lineage to show where data assets were being used, centralized teams are much less concerned. They could easily audit their environment for improper data use or areas where metadata enrichment is needed. This is also why modern data catalogs are so important for reaching this balance.

Regardless of the model, it's valuable to help build a sense of "team" across the business for those roles crucial to Data Governance. Even in a decentralised model the Data Governance discipline usually requires a passionate "evangelist" who can ensure a cohesive strategic vision. And even in a centralised model, it's the business representative roles that actually fulfil the majority of the living, breathing governance. Whichever model the business decides on, find your champions and your passionate aficionados, and build a virtual team around their shared responsibilities and help them spread the word. My preference? "Hub and Spoke" works well, with centralised strategy, standards and planning and decentralised stewardship.

When the topic is Data Governance, centralization or decentralization is always on the agenda. I understand that centralization is not an option because data governance requires engagement from the entire company, and it becomes more difficult if apparently only one area has the commitment to deliver this. I see data governance as something shared where everyone contributes; a committee centralizes decisions and shares goals with all teams. Everyone must be committed; otherwise, the possibility of failure is high.

Adapting to the ever-changing tech scene is key in data governance. Think of it like updating your phone to the latest version; you want your data strategy to stay fresh too. Leaders need to stay in the loop with new data tools and trends. Just as you keep your phone up-to-date for better performance, keeping an eye on the latest in data tech ensures your governance stays sharp and effective.

Acrescentaria que, para além da equipe diretamente envolvida com a área de Governan?a, a responsabilidade de acompanhar as mudan?as e transforma??es que ocorrem ao redor da organiza??o (e na sociedade e planeta como um todo também, é claro), bem como seus efeitos diretos e indiretos, riscos e oportunidades, é da alta lideran?a das organiza??es. é lá que se encontra os comandos e controles centrais da "embarca??o". Quando esta sincronia de vis?es e responsabilidades n?o está muito bem ajustada nas empresas, a dissonancia de perspectivas podem produzir ruídos na comunica??o, cria??o de silos e outros obstáculos que certamente impactariam negativamente na atua??o do time de Governan?a.

In the lens of someone working in higher education, I couldn't agree more with the importance of keeping up with technology and trends in data governance. Higher education institutions must remain agile and adaptable in the face of rapid technological advancements and evolving data landscapes. By staying informed about the latest developments and innovations in data management, analytics, and governance, we can ensure that our data governance framework remains effective and responsive to the changing needs of our institution. This includes integrating new data sources, formats, and tools as they emerge, ultimately enhancing our ability to harness the power of data for informed decision-making and strategic planning.

As mudan?as constantes devido ao rápido avan?o da tecnologia e às mudan?as nas necessidades e expectativas das partes interessadas. A governan?a de dados deve permanecer atualizados sobre os mais recentes desenvolvimentos em gerenciamento, análise e governan?a de dados para garantir que suas práticas estejam alinhadas com as melhores práticas da indústria. Podemdo envolver a incorpora??o de novas fontes de dados, mídia social ou IoT, novos formatos de dados (JSON ou XML), novas ferramentas de dados, Cloud ou AI. Manter-se atualizado sobre as últimas tendências e inova??es. Avaliar regularmente o impacto e as implica??es das mudan?as tecnológicas.

Data governance leaders need to stay up to date with business & product/tech readiness by understanding : 1) Core team resources ( internal/external) 2) Defined User Journey 3) Target Architecture and Clear understanding of Data Architecture 4) Product Capability matching to various business use case delivery.

Um ponto que eu acrescentaria a este tópico (que certamente é um dos mais centrais na atualidade) envolve a heterogeneidade e déficits de habilidades e conhecimentos relacionados a níveis razoáveis de alfabetiza??o digital e analítica. Este descompasso crescente entre a taxa de inova??o e transforma??o tecnológica (aqui considerada de maneira ampla e genérica),e a incapacidade do sistema tradicional de educa??o e dos modelos tradicionais de treinamento e capacita??o corporativos de acompanhar este ritmo acelerado das mudan?as e endere?ar o problema, fornecendo conhecimentos e habilidades mais alinhados com o mundo lá fora, é, talvez, e na minha opini?o pessoal, o principal desafio a ser encarado pelas companhias atualmente.

Inspire your team to keep current on impactful tools, solutions and innovative approaches. Strive to recruit employees with diverse career backgrounds, tech abilities and business competencies. That diversity will be a strength as your data driven needs and asks evolve. Lead by example - build a DG knowledge base (KB) and publish assets that tech and business colleagues gain value from. Ensure both metric driven results and soft skills (e.g. teammate who reviews all demos – gives constructive feedback) are recognized.

Having dedicated roles for data governance is relatively new in and of itself. Traditionally there was maybe one program manager for all governance initiatives. Now, as governance is becoming more front and center, there are more possible governance roles. I think having a clear career ladder for governance in your organization is needed. This org chart might not be very deep at first, so as any good leader you have to be supportive of any high performing team members who want to make an internal shift or seek out senior or other leadership roles.

Begin by identifying team members who are interested in managing various business domains and are tech-savvy; these are your potential core task force. Provide high-value and in-depth knowledge training, workshops, and detailed documentation. Clarify access and decision-making boundaries clearly. Create a space for exploration and testing new ideas. Engage them in the overall strategy; individuals are more likely to be motivated when they have a say in the goals they are working towards. Keep them informed about any upcoming changes and decisions made by business managment

A skilled team is the key component of any successful data governance program. Leaders are facing the challenge of not only attracting, but also developing and retaining top talent in a competitive market. This involves creating opportunities for professional advancement, fostering a collaborative culture, and recognizing and rewarding contributions. Investing in your team's development ensures that the organization has the expertise necessary to navigate the challenges of data governance.

Leadership plays a pivotal role in the success of Data Governance initiatives, serving as the cornerstone on which the entire framework rests. Without committed leadership, the strategic, operational, and cultural shifts required for effective Data Governance are unlikely to materialize. This principle mirrors the adage, "if it's not important to the boss, it's not important to the employee," underscoring the critical need for both executive endorsement and involvement. Leaders not only set the vision and priorities for Data Governance but also model the values and behaviors that underline its importance, thereby embedding Data Governance principles into the organizational DNA.

Despite these challenges, data governance leaders must also address regulatory compliance, data privacy issues, and cross-functional collaboration. It is essential to create a data-driven culture that prioritizes data quality and integrity across all organizational levels. Engaging in industry forums, utilizing external expertise, and promoting a culture of continuous improvement can also contribute to overcoming these challenges and ensuring the success of your data governance initiatives.

Change management is frequently underscoped. This area is typically missed in the Data Governance space. Much focus is on the Process changes , the technology changes but too little focus is on the people change . Change impact assessment is the main ingredient in a successful data governance program and early engagement and establishing a change management team is essential.

Leadership of the CDO or from a representative from the Board is a key success factor for any Data Governance initiative But most difficult topic is cultural change in the company to consider data as an asset and critical source for making decisions in company and to leverage usage as Data for AI projects

Data Governance leadership goes both ways from an Analyst/Lead to a Manager/Director. There has to be clear communication on both ends. An Analyst should manage up and a higher up should manage down. This is a baseline success for both parties.