What are the top 5 skills for resilience in Data Governance?

Resilience in data governance centers on adept communication. Professionals must convey complex concepts clearly, tailoring messages to diverse stakeholders, elucidating data governance's value, and addressing concerns. Adapting communication to evolving needs and tech changes is crucial, along with agile problem-solving to enhance overall governance. Stakeholder relationships, managed through effective communication, ensure alignment and support. Emotional intelligence, recognizing and addressing concerns empathetically, fosters trust. Communication forms the bedrock of resilience, encompassing clarity, adaptability, problem-solving, stakeholder engagement, and emotional intelligence.

Along with all the above mentioned, emotional intelligence is a crucial skill in data governance. As governance is not only achieved by data. It involves understanding and managing emotions, empathizing with others, and navigating relationships effectively. In the context of data governance, emotional intelligence helps in dealing with stakeholders, resolving conflicts, and fostering a collaborative environment, contributing significantly to the overall resilience of data governance processes.

In the last five years, organizations have recognized the importance of data governance in efficiently governing their data management function, reducing resource wastage, and realizing a significant return on their substantial investments in data management. As most organizations transition from data governance strategy to data governance implementation, a few key skills emerge as crucial differentiators for data governance professionals: Data Valuation Expertise Leveraging Generative AI Data Management Skills for Compliance Understanding New Architectures like Data Mesh Defining Data Governance Program ROI

In Data Governance, effective communication is a crucial element of resilience. It is about clearly conveying policies, changes, and the importance of data governance to stakeholders at all levels. Strong communication skills can be employed in bridging gaps between technical and non-technical teams, ensuring everyone is aligned and comprehends the significance of data governance in achieving organizational objectives.

O uso da gamifica??o na governan?a de dados pode ser uma estratégia criativa e eficaz para aumentar o engajamento das equipes e promover uma cultura de dados dentro das organiza??es. Importante ressaltar que quando introduzimos elementos de gamifica??o na governan?a de dados, é preciso alinhar os objetivos da gamifica??o com as metas estratégicas da empresa, garantindo que a abordagem seja relevante e eficaz na promo??o de uma cultura de dados.

As a first step to problem solving, a data governance professional must be vast at understanding identified problems and more importantly proactive in identifying problematic processes, practices and technologies that will create problems soon. Effective problem solving involve the following: 1. Effective and complete stakeholder identification and engagement on data issues 2. Thorough root cause analysis to uncover the why's of the data issues 3. Appropriate fix to stop further issues from arising 4. Appropriate cleansing/remediation approach for already created issues. Great communication, analytic, investigative and creative thinking skills are necessary capabilities of data governance professionals for problem solving and resilience.

Some market proven lean tools could help to increase your capability to solve data governance problems, mainly related to data quality, data access management and policies adoption. I strongly recommend to use Ishikawa diagram and 5 whys from root cause analysis and then set a 5W2h action plan to resolve the issues found. Another tip is that, usually data governance problems point their roots to business processes.

Problem solving acumen is also one of the effective ways of data governance, underlying the value of the business data with effective problem solving mindset will ensure the right data is identified correctly and looked into with due importance and priority

Data Governance practitioners are expected to solve problems that gets identified. While the most related to data, there can be others too; Examples like business process gaps and broken lineage between systems. They are expected to understand the issues and challenges and get them resolved using appropriate means.

The relation and importance of problem-solving skills in data governance can be outlined through several key aspects: Identifying and Addressing Data Quality Issues, Navigating Compliance and Regulatory Challenges, Enhancing Data Security, Facilitating Data-Driven Decision Making, Managing Change and Stakeholder Expectations, Optimizing Resource Allocation, Innovating and Continuous Improvement.

As part of "learning", data governance professionals should pursue and maintain industry certifications in areas such as data governance, data management, data quality, data security, etc. Mastering one of the leading data governance applications is another avenue for learning in data governance.

Learning is a key for data governance resiliency. DG is vast and if you are a DG professional, learning must be considered as 2 sides of a coin: HEADS: As a DG professional, challenge yourself to continuously learn and improve your depth on various associated domains such as Data Quality, Data Architecture & Modelling, Metadata Management and Master & Reference data management. TAILS: Learning will be instrumental in ensuring successful operationalization of DG by establishing a data literacy program that will focus on pointed trainings for DG roles in the organization and associated DG domains DG professionals must always look to learn for: - certifications - sector expertise in DG context - regulatory implications - innovation

Learning is a life long never ending journey which every individual should pursue throughout their life and its not limited to data governance but applies to all aspects of life. An individual who has the eagerness and curiosity to learn will always stay ahead of times and will be able to seamlessly adapt to both technical challenges and as well as challenges in other aspects of life

Continuous learning is crucial for a resilient Data Governance. This involves keeping up to date with the latest industry trends, regulations, and technological developments. Embracing a mindset of lifelong learning allows data governance professionals to adapt to changing circumstances and overcome new challenges. This continuous increase in knowledge and skills ensures that data governance strategies remain effective and relevant.

Data Governance is not a one-size-fits-all all. The ability to understand the peculiarity of an organization and implement Data Governance tailored and suited to the business needs is paramount. Flexibility also entails the grit to adapt to change within an organization such as changes arising from regulation, new business initiatives or processes. This implies that a Data Governance professional is open and responsive to change and great at generating multiple ideas and data strategies to measure up to changes when they arise.

As mentioned above with 1. Communication - Data Governance is a living process - ensure you build in excellence maintenance to your Milestones within your overall agile structure. This means re-examining your processes and data repository structures and flows on an on-going basis. And make decisions regularly- keep it alive and keep it safe.

It is about adapting to change, whether it's new regulations, evolving business requirements, or technological advancements. A flexible approach allows you to adjust strategies and processes effectively, ensuring data governance remains consistent with the dynamic environment of data and its management. Being flexible helps in navigating challenges and transforming them into opportunities for growth.

Data Governance practitioners should lead by example and inspire others to follow the path. While there are many leadership styles present, practitioners can choose appropriate to lead on. My preference is on servant leadership. Data Governance leaders should layout the strategy and orchestrate for desired results, that would add value to the business and organization.

Strengthening leadership is essential for the resilience of data governance. It involves not only guiding a team but also influencing the larger organization towards a culture that values and understands the importance of data governance. Effective leaders inspire, motivate, and motivate change, even in difficult situations. They contribute to decision-making, strategy formulation, and fostering an environment where data governance principles are embraced and integrated into daily operations.

Since they are dealing with such a diverse audience, Leaders should focus on the message. Keeping the (sometimes complex) message concise and simple - or at last pyramid - will help to ensure compliance. And again get people involved - you are building a kind of secondary tribe alongside peoples’ main focus. Getting C-Level support/“condonement” and recognition will help to get that acceptance at every level.

In data governance, leadership involves inspiring and guiding others. Professionals in this field must exhibit strong leadership presence and a clear vision. They should effectively communicate the goals, values, and benefits of data governance to the organization. Key to this role is the ability to motivate and empower others, facilitating participation through training, coaching, and recognition. This leadership is crucial in fostering a data-driven culture and advancing data governance maturity. It's about not only managing data but also leading people, enabling a collaborative and informed approach to data governance.

Os lideres precisam lidar cada vez mais com ambientes extremamente complexos e com dados distribuídos em sistemas que n?o est?o interligados e isso alem de gerar "sombra" na governan?a de dados. O lider que possuir uma vis?o holística de como os dados est?o distribuídos dentro do negócio ter?o mais eficácia quando estiverem a frente da governan?a de dados.

Most competencies needed for data governance are foundational for every professional, but effective data governance professionals need strong background and skills in several data management functions as well. Metadata management, management of data quality, knowledge of data privacy and data security (and their relationship to data governance) are also essential for building and maintaining a strong, sustainable enterprise data governance program.

Horizontal thinking: understanding how business concepts interrelate and how they are depicted in IT. Evaluating all new requirements for reporting as well as transaction processing against the current map of data interrelationships. Influencing new directions based on how they fit in or modify the existing data map is key.

Addressing data challenges and ensuring robust governance requires a collaborative effort across tech and functional teams. Below are the top five areas to focus on: 1. Enforcing clear policies and standards across the organization with buy-in from leadership. 2. Aligning functional and technical teams for effective data quality management. 3. Strengthening data security and privacy through effective internal IT controls. 4. Implementing change management processes, including focused groups and PMO governance, to empower teams and streamline training. 5. Emphasizing continuous monitoring and improvement through dashboards, automation, and leveraging trending technologies like RPA, ML, and AI.

Here are some other skills to consider: 1. Adaptability 2. Critical Thinking ( aka Problem-solving) 3. Effective Planning and Communication 4. Emotional Intelligence 5. Decision-making There are so many other hard skills and soft skills to have but there are the ones I've found most apparent in my resilience as a Data Governance professional.

The top skills for resilience in Data Governance include understanding complex regulations, acquiring technical proficiency, overcoming organizational challenges, addressing data quality issues, and managing security challenges. These skills are crucial for navigating the dynamic landscape of data governance and ensuring data resilience. They help in maintaining the integrity and usability of data, which is the cornerstone of digital resilience.