Why data quality is critical for enterprise AI
According to the Cisco AI Readiness Index 2025, only 13% of organizations are fully prepared to derive value from artificial intelligence due to mature infrastructure. Most Large Language Model (LLM) implementation projects face challenges with data silos and a lack of unified quality standards. Granting AI models access to unorganized repositories poses serious business risks due to the "garbage in, garbage out" principle.
Market implications
Failing to resolve data silos and quality issues means enterprises risk deploying AI models that produce inaccurate or biased results, leading to flawed decision-making. Furthermore, companies that do not modernize their data infrastructure will fall behind competitors who can successfully leverage AI to optimize operations.
New approaches: Data Mesh and Data Lineage
To overcome this chaos, large enterprises are adopting the Data Mesh concept, which shifts data ownership to business domains. It is built on four core principles:
- domain-driven data ownership;
- data as a product;
- self-serve data infrastructure;
- federated computational governance.
Reliable AI performance also requires data lineage—the automated tracking of the complete data journey from source to consumer. Utilizing platforms based on Apache Kafka allows organizations to capture events and replay them to troubleshoot model errors.
Security standards and technological foundation
Systemic risk mitigation is provided by the NIST AI RMF 1.0 framework, which structures AI risk management around four functions: Govern, Map, Measure, and Manage. Data lineage plays a key role during the mapping and measurement phases.
The low-code platform UnityBase (jointly developed by the Intecracy Group consortium) serves as a technological tool for infrastructure preparation. The platform offers built-in auditing (DataHistory), auto-generated REST APIs, and flexible access control. Additionally, the expertise of alliance companies like Softengi (ISO/IEC 42001:2023 certified) and Nectain helps evaluate the performance of AI algorithms against reference data.
Where to start
To successfully prepare your enterprise data for AI, take the following practical steps:
- Transition to a Data Mesh architecture to establish domain-driven data ownership and treat data as a product.
- Implement automated data lineage tracking using Apache Kafka to monitor the data journey and troubleshoot errors.
- Adopt the NIST AI RMF 1.0 framework to structure your AI risk management.
- Leverage low-code platforms like UnityBase for built-in auditing and access control, and collaborate with certified experts such as Softengi and Nectain to evaluate algorithm performance.
Prepared by a Software Ukraine member. Original publication.