Data, analytics and AI governance is perhaps the most important yet challenging aspect of any data and AI democratization effort. For your data, analytics and AI needs, you’ve likely deployed two different systems — data warehouses for business intelligence and data lakes for AI. And now you’ve created data silos with data movement across two systems, each with a different governance model.
But data isn’t limited to files or tables. You also have assets like dashboards, ML models and notebooks, each with their own permission models, making it difficult to manage access permissions for all these assets consistently. The problem gets bigger when your data assets exist across multiple clouds with different access management solutions. Good news, there’s a way to unify data governance. But why should you care?
Without robust data governance, teams companies cannot fully understand their audience, drive better business outcomes operationally and across the consumer lifecycle, or control for algorithmic and data-centric bias. As AI models become more intricate, it’s vital to understand how they’re governed and how they interact with both internal and external data assets.
CIOs understand this. In fact, 98% of CIOs say that moving toward a unified, consistent approach to governance is important, as found in the MIT Technology Report co-developed by Databricks: Bringing Breakthrough Data Intelligence to Industries.
No personalized experiences without governed data
In the fast-paced world of communications, media and entertainment, audiences demand personalized experiences tailored to their unique preferences. This requires a deep understanding of user data – from content consumption habits and understanding clickstream engagement to demographic, past purchase and transaction information. However, this data is often siloed across various systems and platforms, making it challenging to gain a unified view of the customer. Effective data governance is essential to consolidate and harmonize this disparate data, enabling media companies to build a 360-degree profile of their audience. From that 360-degree profile, teams can better build AI models and systems around hyper-personalized experiences, content recommendations, and more that keep audiences engaged and coming back.
As AI models become more complex, it’s crucial to understand how they interact with the data that feeds them. Robust data governance practices ensure transparency and explainability in the AI-powered insights. This is achieved by maintaining detailed data lineage, provenance, and quality metrics that allow data teams to trace the origins and transformations of the data used to train AI models.
Establishing an AI ready data governance strategy
This means that robust data governance is no longer optional—but essential. According to McKinsey & Company, firms lacking effective data governance waste up to 29% of their workforce’s time on unproductive tasks due to poor data quality. Yet, despite its critical role in enhancing data quality and decision-making, data governance often doesn’t directly boost profits, leading some firms to relegate it to IT departments rather than treating it as a strategic priority.
For data governance to transform an organization, it must be led by top executives. With the increase in government mandates, roles such as Chief AI Officer and Chief Data Officer (CDO) are now driving the initiative. This is especially important for AI, which relies on business context in addition to underlying data. When launching a data governance program, the CDO’s first step is to win business backing. This involves setting up two key entities – the Office of Data Management (DMO) to establish policies, and a Data Council of business leaders to set priorities and ensure compliance. A crucial strategy is to focus on specific data areas, such as customer or product data, to make the initiative manageable and aligned with strategic goals. This targeted approach helps prevent the overwhelming scope of data governance from derailing the program. Demonstrating the tangible business benefits of data governance is vital for maintaining funding and executive support. The DMO plays a critical role in documenting successes and communicating the value of continued investment, ensuring data governance remains a priority even after immediate issues are addressed.
Unity Catalog supports data governance
When defining data governance standards, the DMO must closely examine how data is created and accessed across the entire enterprise. While some level of data redundancy is often unavoidable, especially for operational purposes, advancements in analytics capabilities now allow organizations to consolidate their analytics infrastructure. Rather than maintaining a fragmented landscape of standalone data warehouses, data marts, data lakes, and specialized data science platforms, organizations should consider consolidating these disparate environments into a unified, enterprise-wide data platform. This centralized approach can better support the full breadth of analytic needs found across the business.
The Databricks Data Intelligence Platform has been built from the ground up with this vision of a unified approach to data and analytics in mind. Powered by a data management layer in Delta Lake that’s capable of working with structured and unstructured data originating both internally and externally with performance and cost-effectiveness, Databricks allows organizations to consolidate all their analytically oriented information assets within a single, unified platform.
With support for both real-time and batch processing, the Databricks platform allows data engineers to process and deliver insights to the business at whichever speeds are critical to supporting a desired business outcome. With integration with every modern business intelligence and data discovery platform on the market today as well as robust support for machine learning and AI workloads, including generative AI, Databricks is capable of meeting the fullest range of the organization’s analytics needs.
The Databricks Data Intelligence Platform is quickly becoming the industry standard for innovative data management. This is where Unity Catalog comes in. Unity Catalog is revolutionizing data governance by offering a seamless, unified layer for managing both structured and unstructured data, machine learning models, and various other digital assets across any cloud or platform. As a result, Unity Catalog enables data professionals to securely access and collaborate on trusted data, leveraging artificial intelligence to enhance productivity and fully exploit the lakehouse architecture’s capabilities. This is particularly important because of data privacy regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), where data lineage is a critical consideration when addressing the “right to be forgotten”. These regulations require organizations to be able to identify the origin and flow of personal data, so they can locate and delete it upon request.
Media and entertainment organizations often operate in a multi-cloud environment, with data and applications spread across various cloud platforms. Databricks Unity Catalog simplifies the permission model and governance of data assets, regardless of their location, by providing a single, unified layer for managing structured and unstructured data, machine learning models, and other digital assets across any cloud or platform.
Key enablers available through Unity Catalog include:
- Unified visibility into data and AI
- Single permission model for data and AI
- Built-in auditing, lineage and data quality enforcement
- AI-powered monitoring and observability
- Zero-copy, zero ETL open data sharing within and between enterprise boundaries
This unified approach to governance accelerates data and AI initiatives while simplifying regulatory compliance. For an increasing number of organizations, the Unity Catalog as a core component of the Databricks platform has become the cornerstone of their enterprise data governance strategy.
Unlocking the Full Potential of Generative AI
As the media and entertainment industry continues to embrace the power of Generative AI, data governance will be the key to unlocking its full potential. By ensuring the quality, security, and accessibility of the data that feeds these AI models, organizations can drive innovation, create more engaging content, and deliver exceptional experiences to their audiences.
Data governance is not just a compliance requirement, it’s a strategic imperative. By embracing a data-centric, business-led approach to governance, organizations can unlock the full potential of their data and AI initiatives, delivering personalized experiences, driving innovation, and ensuring long-term success in the ever-changing media landscape.
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