What do you do if AI and machine learning are transforming careers in Data Governance?

To stay relevant in Data Governance as AI and ML transform careers, embrace change: Stay informed on the latest tech and how to apply it. Be open to new roles emerging from automation. Upskill via training, courses, and hands-on projects focused on AI/ML integration. Collaborate with AI/ML experts, fostering interdisciplinary partnerships. Adapt governance policies/frameworks to leverage AI/ML capabilities. Promote a culture encouraging innovation and exploring AI/ML ideas. Continuous learning and proactively adapting strategy are key to thriving amidst this transformation.

Embracing changes brought about by AI and machine learning is essential for professionals in the field of Data Governance to adapt and thrive in the evolving landscape. This can be done by Continuous Learning, Adapting changes, Data Literacy, Change Management. By embracing changes brought about by AI and machine learning, professionals in Data Governance can position themselves for success in a rapidly evolving landscape, driving innovation, efficiency, and value creation within their organizations.

If AI and machine learning are transforming data governance careers, embrace the change. Educate yourself on AI concepts and tools, collaborate with tech teams, and gain hands-on experience. Focus on strategic, high-value tasks while using AI to automate routine ones. Adapt your skills to include data analytics and storytelling. View AI as an opportunity to enhance your capabilities and drive innovation. By proactively integrating AI into your work, you can future-proof your career and become a valuable asset in the digital age.

Take a Udemy course on "Building Artificial Intelligence Automation Agency" to learn AI techniques like machine learning models for data extraction, cleaning, and ETL pipelines. Apply learnings by building an AI-driven data pipeline using open-source tools, automating 80% of manual tasks. For example, create a model to intelligently read invoices, classify line items, and automate data entry by integrating with company ERP - reducing workload and errors while focusing on higher-value governance tasks.

Such a beautiful question. AI is absolutely changing Data Governance framework, technology, and processes. It is making humans smarter, faster, and focused on solving data problems associated with business value. Learn the art of presenting your findings and storytelling. Ups killing using modern DG tools.

Like any new topic you need to know about the capabilities and benefits offered by AI and ML by reading articles and attending demos of new tools. It’s also good to learn about key concepts and the key differences between LLMs. In addition, most data vendors including data catalog, MDM, data quality, no code low code solutions integrate AI and ML capabilities, and it’s a good opportunity to learn about the new capabilities and benefits provided by the new releases of these tools.

Acquire technical skills in AI and machine learning, including programming languages (such as Python or R), data analysis, statistical modeling, and machine learning algorithms. This knowledge will enable you to leverage AI and machine learning tools effectively in your data governance initiatives.

Focus on Storytelling, Ethics, Policies, and Quality of data instead of building hands on code such as SQL. English is the next SQL. So learn to ask right questions to the data to obtain business value.

As we all expect, in the Data Governance landscape, there will be exciting things reducing and minimizing the efforts of Business Data Stewards. Instead of performing repetitive tasks like creating business glossary definitions, the definitions will be auto populated and it will allow the business stewards to focus on the review and certification steps only. Similar to business definitions, currently a lot of effort goes towards data classifications and access management. Envisioning data access validation performed by AI capabilities, able to process all user personas and permissions and also will be able to map it to access management policies, we can anticipate Data Governance roles to focus on new things, like AI Governance & Ethics.

Collaborate with AI - Data Governance professionals should embrace automation across all processes of data governance, from reference data management where AI will detect and propose valid value, to data quality monitoring and detection of anomalies to even more advanced items like issue impact assessment and prioritization recommendations offered by AI governance tools and capabilities. The data stewards supporting the Data Governance programs will be able to focus their contributions in collaborating with these AI embed systems to train and fine-tune the output by complementing the AI generated insight with the business context, and unlocking the full potential of these future frameworks. This partnership will increase speed to product.

Embrace automation tools and platforms that leverage AI and machine learning to streamline data governance processes. Collaborate with data scientists and machine learning experts within your organization to understand their workflows, methodologies, and best practices. By working closely with these professionals, you can gain valuable insights into how AI and machine learning can enhance data governance efforts. Gain a solid understanding of data science principles and methodologies, including data preprocessing, feature engineering, model selection, evaluation metrics, and model deployment. This knowledge will help you apply AI and machine learning techniques to solve data governance challenges.

There are many areas to innovate in data governance with AI and ML, starting with GenAI for generating definitions and descriptions, enriching metadata, and classification of data assets through tagging. But there can be additional use cases with LLM helping to identify data owners, find right data sources, write SQL queries, answer advanced analytics queries. Other areas of development can be the improvement of matching records to build MDM single records or implement data quality checks on unstructured data.

Develop a roadmap aligning data governance with AI/ML adoption. Assess current state, identify gaps, and prioritize areas for automation (e.g., data quality checks, and metadata management). Set goals, and timelines, and measure progress. Involve cross-functional teams early. As an example Implement AI-assisted data cataloging to automate metadata extraction, enabling better data discovery and lineage tracking. Pilot with high-value datasets, gather feedback, refine strategy, and then scale enterprise-wide for centralized, AI-driven data governance.

While considering many aspects on how AI and machine learning will transform Data Governance roles, there remains a bigger question, what else should we consider? Data monetization! Trusted data as a product. For Data Governance programs who spend so much energy and focus on producing the most trusted certified quality data sets, the focus will naturally shift to maximize profits of such high end products. Not only will Data Monetization lead to a retail mindset on how to create the best marketplace external and internal to your organization, but also the ideal state of a self-funded Data Governance program. Such ideal state with continuous funding will enable capacity for innovation, and a true flywheel effect.

- Foster a culture of data literacy within the organization. This involves educating stakeholders at all levels about the basics of AI and ML, their implications for data governance practices and the value-added. - Ensure ethical data use. This includes establishing clear guidelines for the ethical use of AI algorithms, ensuring transparency in decision-making processes, and mitigating biases in data models.