Artificial intelligence (AI) can transform your enterprise, but only if your data strategy is ready. This first article in our AI readiness series explores why data challenges derail AI initiatives. It also discusses how knowledge graphs overcome these challenges and how Graphwise helps build a future-proof foundation.
Picture the last day of the month when your finance team rushes to finalize the accounts. You open the dashboard and find the sales figures misaligned with your customer relationship management system. Inventory counts contradict your enterprise resource planning, and customer data is scattered across systems. That frantic chase can indicate a deeper problem.
Your data strategy is broken because data is isolated in tables rather than as interconnected knowledge, resulting in gaps, inconsistencies, and outdated records everywhere you look. A broken data strategy can consume most of your IT budget on fragmented information, leaving your AI models struggling with incomplete inputs.
McKinsey reports that over 70% of organizations deploy AI in at least one business function. Still, only 11% succeed at scale. Nearly four out of five projects fail because their data foundation remains fractured. Those failures cost millions in missed opportunities, erode stakeholder trust, and slow down critical decisions.
Graphwise fixes this at the root by connecting your enterprise data with a knowledge graph and semantic technologies. This gives your AI models the clean, context-rich foundation they need to deliver reliable insights.
The broken state of enterprise data strategies
Enterprises deploy different tools for each function — Salesforce for customer relationship management, Snowflake for data warehousing, and Hadoop or Databricks for data lakes. However, they lack a unified strategy to connect them all. Those disconnected systems create data silos — locking critical data in departmental boundaries and preventing cross-functional analysis.
Knowledge workers spend up to 12 hours each week chasing different data sources — time that could be better spent on higher-value activities such as forecasting or gaining customer insights. Poor data quality costs $12.9 million per enterprise each year, primarily due to cleanup and reconciliation activities.
Beyond costs, silos hinder operational efficiency. Departments cannot share real-time inventory levels, customer histories, or supply-chain status. This results in missed cross-sell opportunities, overstocked warehouses, and slower regulatory reporting.
Because data remains trapped in instead of being modeled as connected knowledge, AI pipelines struggle with incomplete and inconsistent information inputs. Breaking down silos and unifying data strategy is more than a technical fix — it’s essential to unlocking reliable, enterprise-scale AI.
The data dilemma: why traditional approaches fail AI
Traditional data architectures impose significant penalties, extending even beyond silos. Enterprises spend around 40% – 60% of their annual IT budgets as a “Bad Data Tax” — the ongoing cost of poor quality, reconciliation, and cleanup of fragmented data. Even then, 40% of enterprise data is incomplete or unavailable, creating a ripple effect of missed opportunities and high-risk decisions that cost millions annually.
Part of the problem is that most data platforms are inadequate for AI’s needs. For example, Relational databases were not built for interconnected information. Nested JOIN operations reveal data relationships, which become expensive as data volume and relationship depth increase. Complex SQL statements turn into maintenance challenges and create bottlenecks for AI pipelines. Classic data lakes centralize raw data but provide no inherent method to model semantics or relationships. As a result, you end up adding fragile Extract-Transform-Load (ETL) jobs late in the process, which introduces latency and errors that hinder real-time AI use cases.
Every schema change, such as adding a new source or entity type, requires costly migrations and pipeline rewrites. Because these systems treat data as isolated records rather than as a web of knowledge, AI models are trained on fragmented or mismatched data. This leads to models with poor reasoning capabilities and that are prone to hallucinations.
The stakes of this architectural mismatch are significant. Companies face delayed product launches, misguided marketing efforts, and operational setbacks without data readiness. In an AI-driven environment, these shortcomings cost businesses revenue and weaken their market position, underscoring the urgent need for a new approach.
Why knowledge graphs are the answer
Knowledge graphs represent a modern evolution in data management — a way to connect all your data with context and meaning. A knowledge graph treats data as a network of entities and relationships. It is a semantic data layer that connects your enterprise assets while providing them with defined meaning.
Knowledge graphs provide:
- Explicit relationships— While relational tables require complex JOIN operations to link data, a knowledge graph stores entities and their relations as a network of nodes and edges. The graph engine follows those edges natively, so you add new entity types or relationship kinds without rewriting existing queries or changing your core data model.
- Semantic meaning — Knowledge graphs use ontologies, which are simple rules that define what your data means, like linking products to customer preferences. These rules add context, helping AI accurately understand and reason about your data.
- Unified context — A knowledge graph is a single hub for all data, its relationships, and metadata. It integrates data from databases, documents, APIs, and more into one coherent model. It interlinks and contextualizes disparate data by providing consistent semantic metadata and links.
These capabilities create a context-rich, connected data network. AI systems can navigate this network like a domain expert, ensuring each model query taps all relevant information. In effect, knowledge graphs turn fragmented data silos into a unified fabric of knowledge — the very foundation modern AI needs.
How Graphwise fixes your data strategy
Graphwise is a platform that addresses the core challenges of modern data strategies, such as data silos, poor data quality, and delivers AI-ready data. It helps to build a single knowledge graph for the enterprise — a semantic layer where all data and content exist under consistent governance. Graphwise connects your fragmented data silos, integrates structured, semi-structured, and unstructured data sources, and enriches with domain-specific context.
Key features of the Graphwise platform include:
- Unified data integration — Graphwise connects data across silos in real time. It uses enterprise Ontotext GraphDB™ clustering for scalability and high availability. Multiple APIs and connectors, including Kafka for streaming, allow you to ingest and query data from existing systems on demand. Instead of relocating data, Graphwise lets you use it directly from its original source
- Consistent semantics and governance — Graphwise ties all data to shared ontologies, taxonomies, and metadata. This ensures that terms are used consistently and that the meaning of each data element is clear enterprise-wide.
- Graph modeling and AI enrichment— Graphwise automates creating and maintaining the knowledge graph. For example, its taxonomy advisor uses large language models to suggest new categories and relationships. This AI-assisted approach helps you build and expand the graph faster, keeping the structure accurate and reliable.
- Knowledge-powered AI (GraphRAG) — Graphwise extends retrieval-augmented generation by embedding the semantic layer into AI pipelines. Its GraphRAG approach enriches generative models with graph metadata and domain knowledge. This minimizes errors like hallucinations and delivers reliable, contextually relevant results. Unlike traditional methods that falter with disconnected data, GraphRAG ensures your AI outputs are accurate and trustworthy
- Lower total cost of ownership —Graphwise consolidates data management into one platform, reducing the need for separate ETL tools and bolt-on systems. This includes standalone data cataloging, metadata management, or data integration platforms. This integration cuts development and infrastructure costs, making AI projects more cost-effective over time.
With Graphwise, you can transform your data strategy, shifting from a fragmented, inefficient system to a strong, AI-ready foundation.
The initial impact: moving towards AI confidence
Adopting a graph-based approach changes how organizations manage and use data. Graph technology enables more intuitive and efficient data exploration by focusing on the relationships between data points. This shift streamlines data integration and governance and lays a robust foundation for reliable AI applications.
Reducing the bad data tax
The Graphwise knowledge graph approach consolidates fragmented data cleaning, reconciliation, and enrichment into one repeatable workflow. Anchoring records to shared ontologies eliminates duplicates and resolves conflicts at the source, cutting time spent chasing clean data.
Faster access to contextual data
Instead of waiting on ETL processes or complex joins in siloed systems, new data added to the graph connects instantly. Queries run in seconds, giving business users and analysts live access to trusted, connected data.
Unified AI foundation
All AI applications, whether chatbots or recommendation engines, tap into the same trusted graph. This approach removes redundant data preparation and ensures consistent, contextual results.
Trustworthy AI outputs
Knowledge graphs maintain data lineage and transformation history, enabling full traceability. Users can verify what informs an AI output. This helps meet governance standards and builds confidence in every result.
Conclusion
The gap between AI’s promise and reality usually comes down to data strategy. Legacy silos and rigid databases leave enterprises with fragmented information and hidden costs. The only way forward is a transition to data focused on knowledge graphs – a semantic layer that unifies all data under a single context. Knowledge graphs connect your data points in a network, giving AI the context to perform reliably.
Graphwise delivers this transformation in a unified platform, combining integration, governance, and AI-centric enrichment. Moving your enterprise data into Graphwise’s graph will break the bad data tax and unlock a consistent, trusted foundation for AI. In doing so, you finally turn your data into the asset it should be – powering confident, high-value AI across the organization.
Want to learn more about the adoption, use cases, and challenges of integrating LLM and Graph RAG in organizations? Download our white paper, Preparing your knowledge organization for generative AI.
Next in this series: learn how organizations can build a strategic groundwork for AI readiness by adopting a more innovative and cohesive approach to enterprise data architecture.
