How can you use critical thinking to improve Data Governance?

In my experience I like the approach called "Data Governance Thinking" where we follow 8 steps to ensure our data governance program. The step 1 is related to identify the problem and says: 1. Start with business issues that may affect or be affected by data issues. It seems easy but is not, I usually use to simplify that map the questions your business are not able to answer or struggling to answer that data governance could help with?

Identifying what the errors are in data management is extremely complex as operations and results continue to unfold and daily routines hide structural probles, find a team that has the sharp thinking to pinpoint opportunities for improvement. Improvement is a matter of method.

Problem Identification: Commencing a data governance initiative necessitates a clear understanding of the problem or goal at hand. Evaluate the current state of your data, recognizing existing challenges and risks. Define your desired outcomes and the benefits you aim to achieve. Delve into considerations of stakeholders, project scope, available resources, and constraints. By addressing these questions comprehensively, you establish a firm foundation for your data governance strategy, defining the criteria that will guide your path to success.

Identifying the problem is the cornerstone of any data governance initiative. It’s not just about understanding the current state of your data, but also about anticipating future challenges and needs. This foresight allows you to build a robust and adaptable data governance framework that can evolve with your organization. It’s also important to consider the broader business context and how data governance can support strategic objectives.

Temos que identificar as dificuldades dos usuários que precisam realizar a busca de dados: - possuem uma ferramenta adequada para as pessoas de negócio para a busca de dados? - conseguem buscar encontrar os dados que procuram? - precisam ter liberdade de realizar as consultas no tempo, quantidade de dados que necessitarem -possuir a liberdade de criar as suas próprias visualizações. - os dados devem possuir informações sobre os Data Owners para que possam tirar dúvidas, caso tenham. - a disponibilidade desses dados deve estar de forma segura para esses usuários, uma maneira é atrelar a role de cada colaborador com os dados a serem consultados.

By performing this comprehensive analysis, we not only gain a clear view of the current state of data in the organization, but we also identify critical areas of focus. This analysis is not only about data itself, but also about understanding data culture, and the structure of related processes. By evaluating the accuracy, integrity and consistency of data, we can uncover opportunities for improvement and establish a solid foundation for the effective implementation of data governance practices.

Information Gathering and Evaluation: Once the problem is defined, the next crucial step is to systematically gather and evaluate relevant information. Utilize trusted sources such as data catalogs, metadata, reports, surveys, or interviews to collect pertinent data related to the problem at hand. Employ appropriate methods like data quality assessment, data profiling, data lineage analysis, or data impact analysis to meticulously evaluate the data. Assess its accuracy, completeness, consistency, timeliness, and relevance. This thorough examination allows you to pinpoint root causes, identify gaps, and unearth opportunities for enhancement, forming the basis for informed decision-making in your data governance journey.

The process of gathering and evaluating information is crucial in understanding the depth and breadth of the problem. It’s important to not only rely on existing data but also seek out new sources of information. This could include feedback from team members, insights from industry trends, or even lessons learned from past projects. This broader perspective can help you gain a more comprehensive understanding of the problem and inform your data governance strategy.

Moving forward, it's essential to explore and evaluate various alternatives for addressing the problem or realizing the desired goal. Employ creative thinking techniques like brainstorming, mind mapping, or prototyping to generate a spectrum of potential solutions. Then, apply logical analysis methods such as SWOT analysis, cost-benefit analysis, or risk assessment to systematically compare the merits and drawbacks of each alternative. Consider factors like feasibility, effectiveness, efficiency, and sustainability in your evaluation. This comprehensive assessment will empower you to identify the optimal solution or potentially a strategic combination of solutions that align with your data governance objectives.

The generation and comparison of alternatives is a critical step in the problem-solving process. It’s not just about finding the most feasible or cost-effective solution, but also about challenging the status quo and encouraging innovative thinking. By fostering an environment where team members feel comfortable proposing out-of-the-box solutions, you can uncover new approaches to data governance that may be more effective or efficient.

With a selected solution in hand, the next pivotal step is to put it into action and meticulously oversee its execution. Apply project management expertise encompassing planning, scheduling, budgeting, and delegation to ensure a structured implementation process. Effective communication skills come into play as well, involving reporting, documentation, and persuasive presentation to engage stakeholders and secure buy-in for the chosen solution.

Implementation and monitoring are ongoing processes that require diligence and adaptability. Regularly reviewing the effectiveness of your solution and being ready to make adjustments as needed is key to maintaining a robust data governance framework. This flexibility allows you to respond quickly to changes in the data landscape, ensuring your data governance practices remain relevant and effective.

Review and Enhance: After implementation, assess performance through benchmarking and testing. Reflect on strengths and weaknesses, seeking improvement. Use methods like PDCA, Kaizen, or Six Sigma for optimization. This continuous cycle ensures data governance solutions remain agile and effective.

The review and improvement of the solution are integral to the continuous improvement process. It’s not just about identifying and addressing weaknesses, but also about recognising and building on strengths. Celebrating successes and using them as a foundation for future improvements can foster a positive culture of continuous improvement, motivating your team and driving better results.

The development and application of critical thinking skills are essential for effective data governance. It’s not just about individual growth, but also about enhancing the collective intelligence of your team. Encouraging knowledge sharing, collaborative problem-solving, and continuous learning can lead to more informed decisions, a stronger data governance framework, and a more data-driven organization.

I believe that a significant skill for a data governance specialist is the ability to be doubtful. Imagine: you are exploring a source system to enrich the catalog with descriptions, metadata, and categorize content. It's through your eyes that other data people will see it in the data catalog: data engineers, data analysts, and business users. If you take the material provided by the source system team at face value, there's a risk of not noticing important nuances and not mentioning them in the catalog. For example, possible data issues, similar identifiers, and how to distinguish them from the business perspective. The source system team might also forget to mention nuances because they're used to its features.

In my opinion, critical thinking is the ability to analyze, evaluate, and form reasoned judgments. In data governance, it involves systematically assessing information, identifying patterns, and making informed decisions. To use critical thinking in data governance, question assumptions, consider alternative perspectives, and weigh evidence. Apply logical reasoning to solve problems. Emphasize a thoughtful approach to decision-making and continuously seek improvements.