Here's how you can incorporate adaptability into successful data governance initiatives.

Embracing change is fundamental in data governance. As a global thought leader, I champion adaptability by integrating it into the core of governance frameworks. This involves continually updating our strategies to align with evolving data trends, technologies, and regulations. By promoting a culture of flexibility and innovation, we ensure that our governance practices not only comply with current standards but are also robust enough to drive competitive advantage and meet future challenges head-on.

Une stratégie basé sur Kotter ou lewing avec une création d'ambassadeur pour une évangelisation des équipe est toujours, selon moi une bonne approche, un changement trop brutel et fronal n'est jamais bien apprécié, il faut donné du sens, impliquer els équipes, les fédérer autour des idees nouvelles est primordial

The data landscape is dynamic, with new data sources (IoT, social media), evolving technologies (cloud, AI), and shifting regulations (GDPR, CCPA, DPDP). Rigid data governance can hinder progress. Implement a flexible data architecture that can accommodate new data types and integrations (e.g., APIs, data lakes). Your experience with Qlik and Talend can be valuable in designing adaptable data pipelines. Develop modular policies and procedures that can be easily updated as needs change. Implement automated data quality checks to ensure data integrity and compliance, regardless of the data source. Track data lineage to understand the flow of data and facilitate impact analysis. These things keep your organization agile and competitive.

Adaptability in Data Governance: Essential for evolving data landscapes, ensuring policies and tools adjust to changes. Key Elements: Scalability, flexibility, responsiveness, resilience. Best Practices: Define clear, measurable governance objectives. Implement a modular framework for easy updates. Foster a culture valuing flexibility and continuous improvement. Utilise advanced technologies (e.g., cloud, AI). Regularly review and update policies. Engage diverse stakeholders. Monitor regulatory changes. Adopt robust data management practices. Develop dynamic risk management strategies. Apply agile methodologies. By implementing great standards in this area we promote business value and ensure data integrity.

Le changement dans les technos est aussi une prise de risques mais il faut y voir un mal necessaire pour de meilleurs lendemain. Avec un cadrage de qualité, les risques sont connus et donc anticipés, le changement est souvent une période un peu plus tendu au travail mais , in fine tout le monde est fier d'avoir fait ce changement

It is important to understand that like choosing business technology, your data governance strategy is not a “one and done” process. Rather, it’s an evolving approach to risk management that must be consistently refined and adapted in response to a shifting threat landscape and changes within your organization — again, including people, processes and technology.

Data observability could help to monitor your assumptions don't change too much and then secure your governance project. Then don't just design dashboard mentioned risks and instead define workflow to act based on each risk. Stop sharing, protect in different manner each time a risk appear.

Keeping in mind that change is inevitable, the same will apply to your Risk Management Strategy. Dedicate trend analysis on the field of industry that you are governing and ensure that your Data Team hold Connect Sessions daily to identify risk from all sectors and data input vectors.

Even though it seems like a soft topic - data governance has very strong implications for organizations. Not only in terms of missed chances for business opportunities, also legal and ethical risks are included. Therefore, a high adoptability and monitoring of your data processes, ownership, and standardization, is not an option, it's mandatory.

la formation et la veille au sein des équipes doit faire partie des temps dédiés prévu dans les retro planning, nous somems dans un domaine ou sans des formations régulieres ne sommes vite dépassés, il faut s'accorder du temps pour cette veille sur internet, au fild e l'eau, au fil des clics et découvrir parfois de vrais pépite d'usage ou de nouveaux outils, rien n'est bon à rester sur ces acquis trop longtemps, de plus cela forge l'adaptabilité

Data sharing policies, legal policies between countries, privacy policies, AI policies (new kid in the block) has become very important in the current scenario. In short a paradigm shift is required in our thinking, training employees has to become the essential part of upholding the data culture and its adaptability. Because the challenges and opportunities around data management have become broader, the skill sets needed to address it have become broader as well. The next frontier of data governance, risk monitoring calls for a hybrid talent model—one-part business analyst, one-part cyber risk professional—that can bridge both worlds, from cloud computing,AI and IoT

One thing I have learned is that it cannot be overstated how important it is to implement processes and procedures that enable the regular review data governance policies ensuring they remain 'fit for purpose' whereby they continue to reflect current regulatory requirements, latest industry best practices, and advancements in tech. This enables organisations to continually develop their data governance framework thereby ensuring that it can remain aligned with evolving regulatory landscapes.

Once your Data Governance Team are Subject Matter Experts in your specialized field of industry, then they will stay abreast of out of date policies. Process improvement by means of ISO QA or Six Sigma - Lean Processing is a surefire way to keep Policies relevant

Get buy-in. Show all participants the vale and necessity of proceeding on. Demonstrate the value behind your recommendations and get people moving.

Adapting to ever changing data landscapes and environments is key to a successful and complete data governance program. One of the key factors is the ability to educate and "sell" data governance continuously. One of the "selling points" that I use with my stakeholders is to show the importance of data governance and the role that it plays as it pertains to our data. And as data is viewed and managed as assets, the point that's driven home is, "Data governance is my job but it's everyone's responsibility". I find that this keeps the stakeholders engaged and it gives them a sense of purpose as it relates to data governance.

Le departement formation doit etre dans l'nalyse data des retours d'expérience des apprenants en internes, il doit aussi se tenir informé des projets en cours pour adapter les formats et les contenus en fonction de ces derniers afin de garder une efficacité et une réponse aux besoins de formation la plus qualitative possible

Implementing data governance training through data literacy programs is a pivotal component in driving the success of a data management program, especially in an environment characterized by ever-changing business dynamics. By prioritizing data literacy initiatives, organizations can empower employees across various departments to understand, interpret, and effectively utilize data in their day-to-day operations. This not only enhances decision-making but also fosters a data-driven culture throughout the organization. Furthermore, a flexible and dynamic approach to data governance training ensures that employees are equipped with the necessary skills and knowledge to adapt to evolving business needs and technological advancements.

Additionally to training, acknowledge and celebrate successes achieved through adaptable data governance initiatives. Recognise individuals and teams for their contributions to improving data management practices and achieving positive outcomes; supporting resilience and agility within your data governance framework.

The daily Connect Sessions held in team would identify the need for training required. This would also be steered and driven by Policy Updates and Risks Identified. Getting the best training to assess and alter both the current policies and risks.

Les outils et les usages doivent être les plus agiles possibles pour une adaptabilité et une réactivité de modifications quant à l'analyse des KPI, la lecture des data doit etre fluide et intuitive pour que tous les départements soient en data driven stratégie

Integration tools let you quickly connect to new data sources, while intelligent data catalogs empower users to easily find and understand data assets. Automation with machine learning and robotic process automation makes governance super responsive. Metadata management solutions provide a solid foundation for governance practices to evolve. By embracing the right modern tech, data governance can become a value-driving rockstar instead of a rigid, bureaucratic bottleneck. Technology is the secret sauce that enables governance to be nimble and impactful in the face of constant change.

Imparting strong collaboration and relationships across data streams is essential for optimizing data management and driving success in data-driven initiatives. This involves cross-functional collaboration, effective communication, relationship building, collaborative problem-solving, and a culture of continuous improvement. By prioritizing collaboration, organizations can maximize the value of their data assets and achieve greater success in today's dynamic business environment. Facilitating collaboration among diverse teams involved in data streams, including data engineering, analytics, and business units, to ensure alignment of goals and strategies.

Assign Owner and Steward are good for your data but also for your governance project. Have you defined them on top of metadata type?

Stakeholders and Key Account Holders need synergy and harmony with the Data Governance Team therefore collaboration is essential to Optimum performance and delivery. On-going Cross Skill training is the best manner in which to foster effective communication within the organization. The Data Governance Team, ideally, should lead the initiatives by means of sharing the latest Data Policies and Risks that could expose the business or attract new business.

I find stakeholder engagement and great collaborative framework really important. It builds trust and relationships cross functionally. And is very inclusive, transparent and effective practice.

Culture of the Organization and mind set of personal is also key pillar for data governance strategy. If organisation's culture values data integrity and security, employees are more likely to adhere to data governance policies. A supportive culture fosters compliance and reduces resistance to new processes. A quality oriented mindset towards data governance encourages employees to take responsibility for data quality and security. Clear definitions of roles and accountability are essential for ensuring that data governance practices are followed.

“Change is the only constant” will very well fit for Data Governance in terms of change in Data Landscape and Regulatory compliance needs. In order to be successful, most important factor is Organisation to be more Risk averse and get Executive sponsor throughout lifetime. As this initiatives are not directly linked to Revenue (but focused on Quality and/or Risk mitigation) often started with good intention and fail to meet continuous sponsorship and/or improvements. Organization who does an assessment on their current maturity and establish a path where they would like it to be by establishing with key measurable initiatives are inclined to be more successful.

Data governance initiative adaptability depends on the: 1. Organisation culture 2. Tolerance for data integrity 3. Change acceptability 5. Continuous assessment and remediation 6. Sustainable policies and procedures

In my view, as the importance to AI grows. We should should aim to curtail the impacts through technology and using our experience gathered all these years. AI governance would be the key for many future implementations as many and many AI driven LLM's would prevail and help users and business, technologies to thrive for a quick and easy decisioning to make our life easy. While this is good ,the importance of governing the AI models and its usage of data should be monitored meaningfully as the engaging AI should be to make our life easy and not be the other way around wherein AI overtake human brains and our emotional intelligence

De acuerdo a mi experiencia el sector salud en Colombia se caracteriza por su dinamismo y constante evolución normativa. Para que las instituciones de salud se mantengan a la vanguardia y cumplan con los requisitos cambiantes, es fundamental incorporar la adaptabilidad en sus estrategias de gobernanza de datos.