George Firican - The Data Governator

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Here are the highlights -

Data Governance and Vendor Perspective Insight

• Vendors may sometimes emphasize data governance in their products to align with the current industry focus, leading to varying levels of capability in different solutions.
• Emphasizing caution when selecting a vendor for data governance solutions due to the diversity in available options.
• Not one vendor typically covers all aspects of data governance comprehensively.

Data Governance and Vendor Perspectives

• Data governance is often used by vendors as a buzzword to align with current trends. However, not all vendors offer comprehensive solutions in this space.

Data governance is such an interesting space and from a vendor perspective, sometimes it's a word that they just attach because they know it's something that's in focus right now.

Defining Data Governance

Understanding Data Governance Levels

• Data governance is a multifaceted discipline encompassing principles, policies, processes, standards, roles, and responsibilities that ensure data is treated as a valuable asset.
• Data governance can vary in scope and depth across different companies depending on their interpretations, cultures, and applications.
• The definition of data governance varies widely, leading to multiple interpretations and definitions across different sources.

Data Governance and Communication Strategies

• Data governance involves fixing reporting issues sustainably, necessitating ongoing communication and planning tied to organizational goals.
• Regular communication is crucial to combat the perception that data is not well-managed or utilized within the organization.
• Visibility into past actions and current initiatives fosters a sense of responsibility and involvement across the business.

Data Quality Framework and Communication

• Promoting awareness and outcomes of data-related efforts ensures continued engagement and support from stakeholders.

"You need to keep on reminding them if you have done things you need to remind them of those things that you have done."

• Having a sustainable data quality framework involves fixing issues promptly and efficiently to avoid recurring work and resource drain.
• Ongoing communication is crucial to ensure awareness about data quality efforts and maintain engagement from stakeholders.

"You need to keep on voicing that awareness so that people know okay we're doing things."

Importance of Data Governance and Communication

• It's essential to discuss plans, current actions, and past accomplishments to align data governance activities with organizational goals.
• Continuous communication is key to dispelling misconceptions and ensuring involvement from all business units.

"You need to remind them of those things that you have done."

Lessons Learned in Previous Roles for Effective Data Governance

• Engaging individuals from various backgrounds emphasizes that data governance and quality are everyone's responsibility, not limited to technical or business units.
• Involving stakeholders in decision-making processes ensures a holistic approach to data governance and fosters a culture of shared responsibility.

"Everybody needs to be involved in data governance; data quality is everybody's responsibility."

Importance of Business Involvement in Data Governance

• Data governance efforts should be oriented towards the business, as they should ultimately own the processes and standards, defining goals, priorities, and governance strategies.
• Starting data governance requires understanding business goals and aligning data strategies accordingly.
• Ideal data governance professionals need a multidisciplinary background and internal company knowledge to effectively address business needs and pain points.

Business Ownership and Data Governance

• Understanding the goals, priorities, and standards of data governance is crucial for businesses to take ownership of the process.

Towards the business because in the end, the business should be the one that's owning all of this... what are the priorities for data governance that we need to tackle first.

Ideal Background for Data Governance Professionals

• Starting data governance involves defining goals, priorities, and tackling issues like data governance structure within each business unit.
• For those entering data governance, having a background that spans different disciplines and technical expertise is beneficial.
• Ideal backgrounds for data governance professionals involve a mix of different disciplines and internal company relationships for an in-depth understanding of business needs.
• Knowledge of technical aspects like data management practices is crucial for data governance roles.
• Effective communication and change management skills are paramount to driving successful data governance initiatives.

"The person from within the company already has ideally the relationships with all these business units, so somebody that's well-versed in different disciplines maybe they were able to have a touch base with at least two, three of those main business units within the organization."

I think that somebody that needs to grow from within is a lot more valuable than bringing in a data governance expert externally.

Ideal Background for Data Governance

Resources for Business Professionals Learning Data Governance

• The ideal background for data governance professionals involves a mix of understanding different business areas and having technical expertise.
• Communication skills, change management experience, and the ability to implement data management practices are paramount.
• Business professionals entering data governance can benefit from resources like the Data Management Association (DMA) for comprehensive insights into data management and governance.
• Ideally, individuals should have experience with data quality management, business intelligence, data architecture, and data warehousing.
• Understanding various facets of data governance, including data quality, business intelligence, and data architecture, is essential, making resources like the Data Management Association's Body of Knowledge valuable.

"Awareness of good data management practices comes very, very, very good, but regardless of the background, communication, Michael Daddi mentioned it's paramount to have, and I guess the whole change management experience and skills."

• Lights On Data provides practical resources and courses in data governance, offering templates and real-life examples for individuals stepping into or enhancing their data governance practices.

Resources for Business Professionals in Data Governance

• The Data Management Association (DMA) offers valuable resources covering various areas including data governance.

Implementing Generative AI in Data Governance

• Implementing generative AI can speed up processes like classifying data, providing recommendations on documents or definitions, and empowering users when they can't find what they need.
• Offers recommendations and simplifies workloads, aiming to democratize data governance by involving more users.

Data Governance and Generative AI Interaction

Generative AI can simplify workloads and democratize data governance, putting more power in the hands of users.

Cautioning on Generative AI in Data Governance

• Enhancing data organization through quicker classification methods can involve empowering users within organizations to provide recommendations, simplifying workloads, and democratizing data governance.
• Generative AI should serve as a helper tool rather than a replacement for expertise and judgment.
• Generative AI should be viewed as a helpful tool, not a replacement for expertise or critical thinking in data governance processes.
• Emphasizes the importance of not solely relying on AI outputs and the need for human judgment in decision-making.
• Outsourcing decision-making entirely to generative AI poses risks of reliance on opaque processes and neglect of individual judgment.

Using generative AI as a helpful tool is advised, not as a substitute for human expertise and judgment.

Change Management in Data Governance and AI

• The metaphor of generative AI as a black box underscores the danger of depending solely on automated recommendations without understanding the underlying mechanisms.
• Enforcing a culture that embraces trial and error in AI implementation is crucial.

"The metaphor of generative AI as a black box underscores the danger of depending solely on automated recommendations without understanding the underlying mechanisms."

• Collaboration between data governance and AI teams from the start aids in establishing guardrails and ensuring ongoing communication.
• Creating a safe playground for experimentation can facilitate rapid implementation and prototyping.

"With AI implementation, trial and error should be embraced, and collaboration between data governance and AI teams is essential."

Leading Change in Organizations

• Acknowledging the inherent resistance to change and involving those affected from the beginning can ease transitions.
• Continuous engagement, ongoing collaboration, and involvement throughout the project build acceptance and reduce pushback against change.

"In change management, ongoing engagement and collaboration throughout the project foster acceptance and reduce resistance to change."

Implementation Best Practices

• Implementers like Prosci stand out due to their successful implementations globally, gathering feedback from various industries. However, contextualizing frameworks to suit the organization is crucial for success, requiring customized approaches.
• Ongoing communication, tailored trainings, and addressing diverse personalities contribute to effective change management. Providing rewards can motivate employees, ranging from public recognition to tangible prizes, creating a positive environment for change initiatives.

Perspectives on Implementations and Context

• Managers should leverage the perspectives of those who have hands-on experience to add valuable context to broad frameworks that may overlook specific organizational nuances.

"The one that stands out the most is Prasai implementation. They have had many successful implementations, always gathering data back from implementers across the globe and different industries."

• While frameworks like Prasai's implementation offer successful strategies gathered from global implementers, it's essential to adapt them to suit the unique context of one's organization.
• Effective change management involves continual communication, tailored training, addressing diverse personalities, and providing incentives, all of which contribute to successful implementation.

"Managers need to be very sensitive to contexts, as what works globally might need adaptation for specific organizational contexts."

Happy Learning!

Andrew Madson

Michael Madson

Insights x Design

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