A Solution to Solving the Stubborn Data Silo Problem | Evanta NYC 2024 Review

Thank you to Evanta for organizing this CDAO Executive Summit in New York, where leaders in digital transformation shared insights and best practices. Several key takeaways emerged, including the leverage of Generative AI and an increased focus on AI governance. However, one particular challenge repeatedly surfaced—explicitly and implicitly: the problem of data silos.

Solving Data Silo Problems

Data silos, where information is isolated within departments, teams, or systems, pose significant risks to AI governance. By limiting data diversity, silos can lead to systemic biases within AI models. Furthermore, AI governance relies on transparency, but silos make it challenging to piece together a cohesive view of decision-making processes, resulting in accountability gaps. In a siloed environment, different systems often use varying privacy and security protocols, creating inconsistencies and vulnerabilities. This fragmentation weakens security, complicates compliance with regulations like GDPR or CCPA, and makes comprehensive audits difficult. Additionally, because silos limit insight-sharing across departments, they prevent organizations from gaining holistic, enterprise-wide views essential for optimized, AI-driven decision-making.

The root causes of data silos are twofold: technical and organizational/cultural. Orion Governance’s Enterprise Information Intelligence Graph (EIIG) offers a solution to address both of these stubborn challenges.

EIIG Solves Technical Challenges

On the technical level, a foundational challenge lies in obtaining metadata from various, disconnected technology sources. EIIG, a self-defined data fabric, uses a bottom-up approach to ingest metadata from a broad range of technologies, including legacy systems (COBOL, JCL, PL1, IMS), DBMSs, data pipelines, BI tools, and programming languages like Python, Java, and Scala. EIIG then stitches this metadata into a knowledge graph, mapping technical assets with business ontologies to create a unified, centralized view of enterprise data with granular lineage at the field level and a dynamic catalog. Whether your organization uses legacy systems or modern stacks such as Databricks or Snowflake, EIIG enables intra- and inter-system connectivity. This approach removes technical barriers, allowing data silos to be dismantled without needing to physically move data, resulting in a dynamic, near real-time map of enterprise data flows.

EIIG Solves Organizational Challenges

On the organizational and cultural level, EIIG helps to foster change by addressing the concerns that can perpetuate data silos. One common reason for silo retention is the fear of disruption—DBAs may worry about the integrity of their departmental data, and security teams may fear losing control if silos are broken down. EIIG employs a unique ZIP (zero impact on production) approach, extracting metadata without touching production data. Its powerful impact analysis solution visualizes dependencies in near real-time, showing how a single data point’s change can affect other systems or reports. This visibility reassures stakeholders and provides peace of mind, encouraging them to embrace data integration.

Solution in Weeks, Not Months

Finally, because EIIG can be deployed in weeks (rather than months or years), data and AI governance teams can quickly demonstrate how dismantling data silos accelerates key initiatives like modernization, regulatory compliance, and Generative AI projects. With EIIG, organizations can address both technical and cultural challenges, unlocking the full potential of their data for AI governance and beyond.

About the Author: Niu Bai, Ph.D. is the Head of Global Business Development at Orion Governance, Inc. Connect with Niu on LinkedIn.