Empowering energy decisions: AI meets Data Mesh

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Empowering energy decisions: AI meets Data Mesh

In part two of their Energy Transition Talks conversation, Doug Leal and Peter Warren dive deeper into the concept of Data Mesh and its impact on organizational structure. Specifically, they examine how Data Mesh enables business agility and AI innovation while necessitating a cultural shift, robust data governance and collaboration between IT and the business. 

Data Mesh represents a significant cultural shift in how organizations manage and use data. Traditionally, data ownership has resided within IT departments, but Data Mesh advocates for decentralizing this ownership to various lines of business teams. 

Doug highlights the four key principles of Data Mesh:

1. Domain-Oriented Decentralized Ownership: Data is no longer solely owned by IT; instead, it allows teams closest to its creation to take ownership and responsibility for its quality and reliability.
2. Data as a Product: Organizations are encouraged to treat their data sets as products, prioritizing data quality, usability, and timeliness, while focusing on how they can create value from them.
3. Self-Service Data Platforms: With multiple domain-oriented data platforms emerging, automation is key, and teams need to ensure these platforms are user-friendly and efficient. The goal is to remove bottlenecks and accelerate data sharing and collaboration.
4. Federated Computational Governance: This model supports governance tailored to specific domains rather than a one-size-fits-all approach, allowing for more relevant oversight.

The transition to decentralized ownership empowers business teams to take control of their data, fostering agility and responsiveness to market needs. However, it also increases their responsibility. Data governance is paramount for Data Mesh! It ensures data quality and security across decentralized domains, fosters trust and consistency in data usage, and balances autonomy.

Importance of data quality in Data Mesh

“Data quality is still a cornerstone of a Data Mesh platform,” Doug says, explaining that developing this domain-based data architecture requires a robust data quality framework. This involves ensuring data traceability and conducting rigorous quality checks for accuracy, completeness and consistency so organizations can build trust in their data.  

Collaboration between technologists and business stakeholders is essential for identifying the most accurate truth as organizations integrate multiple source systems into their Data Lakehouse. This foundation is also critical for future advanced analytics, machine learning, and AI initiatives.

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