You're tasked with developing adaptability in data governance. What's the best way to get started?

Data governance (DG) is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage. Effective data governance ensures that data is consistent and trustworthy and doesn't get misused.

I’d focus on assessing the data governance's current state, evaluate its maturity level, conduct an inventory and mapping of data assets, review data policies for compliance, engage with stakeholders for feedback, examine the technology infrastructure, analyze the governance framework, and assess performance metrics and reporting practices.

The biggest factor, with adaptability in mind, is understanding how data governance aligns with and impacts business strategy. Through that lens you can develop a far more robust methodology for prioritizing your data governance work. I assess elements by three factors: (1) business impact, (2) effort needed, and (3) urgency of request, all combined together to determine the priority. With a consistent method for prioritizing governance, you can build out a roadmap, and as new change requests come up you can easily adapt to them and prioritize them correctly.

First step is to understand what is and what is NOT data governance. Frameworks defined by DAMA or EDMC gives the foundational perspective and basic understanding. It gives an idea about where to get started e.g. before setting up a goal understand what’s a DG goal. Since it’s a subject and not a topic, over dependence on blogposts is not a good start. Comprehensive reading through the framework is the mantra.

Es fundamental comenzar evaluando el estado actual de la madurez de gobierno de datos en la organización. Esto implica analizar la infraestructura existente, las prácticas de gestión de datos, la calidad de los datos y la cultura organizacional en torno a la gestión de datos. En mi experiencia, esto proporciona una base sólida para identificar áreas de mejora y establecer objetivos realistas para el desarrollo de la adaptabilidad en Gobierno de Datos.

I am a firm believe in the Infonomics approach to establishing data asset valuation model within a company. The company needs to recognize the value of managing data as a strategic asset in the same way it would value tangible assets like inventory. A KPI for data quality is great, and showing data quality improvements over time is a great way of demonstrating governance efforts... but it does NOT answer what the business value is, or why the C suite should care. KPIs are only as good as the story they can tell, and so to tell a story that people actually care about, you have to relate your KPIs to business objectives that the audience is actually invested in. How did we impact the bottom line? How did we create business value?

Una vez que se ha evaluado el estado actual de madurez, es importante definir claramente los objetivos y métricas para el desarrollo de la adaptabilidad en la gobernanza de datos. Esto puede incluir objetivos específicos relacionados con la agilidad, la eficiencia operativa, la calidad de los datos, cumplimiento regulatorio y de seguridad. En mi experiencia, establecer métricas claras y objetivas es fundamental para medir el progreso y el éxito de una estrategia de Gobierno de Datos.

defining the goal to why data is being collected is the best way to start. adding and defining clearcut metrics as: who may have access to: view, edit further add a layer of protection to the data gathered in effect bolstering trust and confidence to data contributors. Firthermore,a data governance body should be able to act independently and with utmost agility to address both existing and future threats.

Defining goals & success metrics for the success of a data governance initiative for any organization is crucial. Goals help strategically prepare the organization to rapidly evolve and adopt the defined framework for data governance, thereby leveraging the business opportunities inherent in the initiative. Whereas metrics or key KPIs facilitate close monitoring of the progress of data governance initiatives and their adoption within the organization. Metrics also aid in mitigating any risks that may arise due to non-compliance with local or international data management guidelines.

I prefer the agile approach to governance over most others due to the simple fact that it lends itself well to creating compelling use cases for demonstrating business value achieved through data governance. By defining clear user stories and acceptance criteria at the outset, closely collaborating, then iteratively delivering and receiving feedback, you end up with a solution that is more likely to meet stakeholder needs, a more invested and happier stakeholder, and a clear and concise use case you can share with others to demonstrate data governance's business value.

La implementación de prácticas ágiles es fundamental para fomentar la adaptabilidad en la gobernanza de datos. En mi experiencia, la agilidad ha sido crucial para responder a las demandas cambiantes del negocio en relación con los productos de datos que están siendo desarrollados y proyectos que agregan valor a la organización, cumpliendo al mismo tiempo con las políticas y actividades establecidas por el equipo de Gobierno de Datos.

“Agile” governance means more than just coordinating effective, efficient, and reliable public and private institutions to effectively manage problems - the term implies a forward-looking approach that seeks to anticipate problems before they materialize. We can follow the below practices. Collaboration Iterative approach Flexibility Empowerment Continuous improvement Automation Metrics and measurement

Agile data governance is a modern, flexible approach to data governance that emphasizes collaboration, adaptability, and iterative improvements. Focus on 1. Collaboration 2. Iterative approach 3. Flexibility 4. Empowerment 5. Continuous improvement 6. Automation 7. Metrics and measurement

En mi experiencia, establecer prácticas de monitoreo y validación de la calidad de los datos de forma automatizada, así como también garantizar la disponibilidad y accesibilidad de los datos para los usuarios autorizados es fundamental para desarrollar la adaptabilidad en la gobernanza de datos dentro de la organización. Esto implica tratar los datos como un activo operativo crítico y establecer procesos y procedimientos para su gestión continua

1. ?????????????????????????? ????????????????: Encourages teamwork among data professionals, fostering cross-functional collaboration and breaking down departmental silos. 2. ???????????????????? ????????????????: Prioritizes automation tools and techniques to streamline processes, reducing manual tasks and enhancing efficiency across data pipelines, testing, deployment, and monitoring. 3. ?????????? ??????????????????????????: Promotes agility in data management, enabling quick adaptation to evolving requirements through methodologies like Scrum or Kanban, fostering experimentation, and continuous improvement.

A data Ops is anything that extracts value from data and helps you generate meaningful insights. It can be raw data, warehouses, KPI dashboards, domain data, algorithms, and more. Applying product thinking to data can help you generate meaning from data at scale. Unlike a service, you build a product once and many customers can use it to solve a problem. You can update the product and optimize the value your customers receive. However, the basic premise remains unchanged.

Let's explore the journey of a healthcare organization navigating ever-changing regulatory demands. By plunging into their narrative, we uncover critical challenges and witness the ingenuity of their devised solutions. Forums, events, and mentorship programs provide avenues to connect with experienced experts, who serve as guiding lights, illuminating the path forward. Embracing adaptability requires active engagement and experimentation. Each encounter, each exchange of ideas, offers an opportunity to refine methodologies and augment your repertoire of strategies. As a collective, we strive towards a future where the unpredictable nature of data governance is met with collective wisdom, enabling us to thrive in the face of uncertainty.

Es de mucha ayuda aprender de las experiencias de otras organizaciones en el desarrollo de la adaptabilidad o estrategia de gobierno de datos. Esto puede implicar participar en comunidades de práctica, asistir a conferencias y eventos del sector, y colaborar con expertos en Gobierno de datos de otras organizaciones. En mi experiencia, el intercambio de conocimientos y mejores prácticas es invaluable para identificar oportunidades de mejora y evitar errores comunes en el proceso de desarrollo de la adaptabilidad en la gobernanza de datos.

Develop insights from successful use cases across industries and organisations. Participate in seminars, conferences and virtual discussions on Data Governance, its need, the challenges it is facing and the consequences of non-compliance. Different industries have different levels of governance policies and regulatory bodies periodically update the best practises. Keeping a check on these updates can be a good starting point for developing good data governance framework

Consider closely aligning with your Data Governance Steering Committee on planned experimentation or Proof of Concepts (POC). Each should have a deliberate plan and milestones that are able to be reached in the near term. The results of a POC should be presented in a format acceptable of the Steering Committee to review in a post-mortem session to determine a path forward which may result in an adjusted form of the original POC. There should also be caution applied when considering new tools and technologies as many may take a great deal of time to properly configure and may quickly run up costs to the organization if not already licensed.

In today's dynamic world data needs to be not just strong but adaptable. So perfectly positioning adaptive data governance is crucial for business project success. I can think of core practices like 1. Break down silos and help teams collaborate as responsible data stewards across data workflows across functional and technical development teams. 2. Gain momentum to literate teams of data quality and data governance efforts 3. Automate tasks to free up the team for strategic thinking for data lineage tracking and quality monitoring. 4. Integrating data governance checks into your development pipelines to embrace dataops 5. Stay agile and ahead with a data governance framework with regular reviews and policy updates. 6. KPIs for governance

Few more points for better adaptability:- 1. Plan for Comprehensive training to explain the benefits of adaptability and involve diverse community of stakeholders for decision making. 2.Align Governance policies around applicable Organization regulatory Compliance(SOX,FDA etc) for better adaptability. 3. Implement robust data privacy and security measures, including encryption, access controls, and data masking techniques to gain customer confidence. Gaining customer confidence will serve as a catalyst for promoting adaptability within the organization.

Data governance adoption depends majorly depends on : 1) Setting up a driven data culture. 2) Alignment to business strategy 3) Clear focus on segregation of duties. 4) Executive buy-in and leadership alignment on value received by a data governance program 5) Focus on strong It foundation

Data Stewardship: Accountability is assigned, but there's a need for clearer delineation of responsibilities and enhanced collaboration between stewards. Monitoring and Enforcement: Monitoring mechanisms are in place, but enforcement could be strengthened through more timely and consistent actions. Culture and Continuous Improvement: A culture of continuous improvement is encouraged, but there's room to foster greater awareness and engagement across the organization

Most of the time process stimulation is measured and when the stimulation is higher then it is believed that it is a good process. But that’s wrong. Higher stimulation of a process only means that it works for the current use case very well. This can change over time and therefore, it should be allowed to bypass the process and not stimulate it. Of course, the reasons to bypass the process need to be looked into to figure out if adaption is needed. That will ensure adaptability in integrated into data governance.

Adaptability ensures that data governance remains effective and relevant, even as new challenges and opportunities arise. Staying informed about technological advancements and regulatory changes. Encouraging a culture of continuous improvement within data governance practices. Engaging stakeholders in the process of updating governance frameworks.