What do you do if your data governance project needs effective management through logical reasoning?
Data governance is the practice of ensuring the quality, availability, security, and usability of data across an organization. It involves defining policies, roles, processes, and standards for data management and usage. However, data governance is not a one-size-fits-all solution. It requires logical reasoning to adapt to the specific needs, goals, and challenges of each data governance project. Logical reasoning is the ability to analyze, evaluate, and infer from information based on facts, evidence, and rules. In this article, you will learn how to apply logical reasoning to your data governance project and improve its effectiveness and efficiency.
The first step in logical reasoning is to identify the problem or the question that your data governance project aims to solve or answer. This will help you define the scope, objectives, and expected outcomes of your project. To identify the problem, you need to gather relevant information from various sources, such as data owners, stakeholders, users, regulations, and industry standards. You also need to verify the accuracy, completeness, and reliability of the information. Then, you need to synthesize the information and formulate a clear and concise problem statement that summarizes the main issue, the context, and the desired result.
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Connect the Data Governance program to business KPIs. This is the only way you are going to get traction within the enterprise. Also, you need real pain points that resonates with people you are working to solve in the Data Governance program. Third item is to have a non invasive structure and the right senior and executive leadership who signs up to support the initiative. Let me know if I can help in any other way. Hope this is helpful.
The next step in logical reasoning is to generate hypotheses or possible solutions to the problem. A hypothesis is a tentative explanation or prediction that can be tested and verified. To generate hypotheses, you need to use your creativity, knowledge, and experience to come up with different ideas that might address the problem. You also need to use your critical thinking skills to evaluate the feasibility, relevance, and impact of each idea. You can use various techniques to generate hypotheses, such as brainstorming, mind mapping, scenario analysis, and SWOT analysis.
The third step in logical reasoning is to test hypotheses or validate the solutions. Testing hypotheses involves collecting and analyzing data that can support or refute each hypothesis. To test hypotheses, you need to design and execute experiments or tests that can measure the performance, effectiveness, and outcomes of each solution. You also need to use appropriate methods and tools to collect, store, process, and visualize the data. Some examples of methods and tools are surveys, interviews, observations, benchmarks, metrics, dashboards, and reports.
The final step in logical reasoning is to draw conclusions or make decisions based on the results of the tests. Drawing conclusions involves interpreting and synthesizing the data and comparing it with the problem statement and the objectives. To draw conclusions, you need to use your judgment, logic, and intuition to identify the best solution or the most likely answer to the problem. You also need to use your communication skills to present and explain your conclusions to the relevant audience, such as data owners, stakeholders, users, and regulators.
Logical reasoning is a valuable skill for data governance projects. It can help you define, solve, and communicate data-related problems in a systematic, rational, and evidence-based way. By applying logical reasoning to your data governance project, you can improve its quality, efficiency, and alignment with your organizational goals and needs.
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I love this article and the description of the prompts because it’s supposed to be about data governance, but let’s think about what this really sounds like… product management 👀. Interview stakeholders, understand pain points, build experiments, test hypothesis and collect data and user feedback, iterate and improve. All product management fundamentals. And this is a great thing! The data and analytics world is embracing data products and data product thinking. This is the new look for governance. It’s no longer about red tape and no longer a brake pedal on your data/analytics/AI projects. Governance is more about this product mindset and connecting data activities to business value. Maybe even use a different word internally 🤷🏽♂️
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