Data Hubs, Not Dashboards: Building the Pipes for AI‑First Businesses
The “boring” plumbing decides whether your AI exponentially scales…or spectacularly fails.
📌 THE POINT IS: AI doesn’t “bottleneck”—it chokes on bad, incomplete, or stale data. To fill gaps it hallucinates data or it answers with old data and assumptions. At scale, that’s a governance, brand, and financial nightmare waiting to happen. You can proof against that by investing in your Trusted Data Environment, leaning into high-velocity data plumbing that's governed, observable, discoverable, and powered by an intelligent semantic layer.
The AI boom has captured every boardroom’s imagination. Executives are funding pilots and demos, believing that generative models will give their organizations an “intelligence” advantage. Yet most implementations still sit on top of brittle spreadsheets and siloed dashboards. When outputs look plausible, we assume they’re correct. When they’re wrong, we call it hallucination. In reality, bad data is choking AI. Models trained on incomplete or outdated data make up answers to fill gaps. Many organizations don’t even know their data is bad—81 % of surveyed companies trust their AI results despite fundamental data inefficiencies, and the average firm loses 6 % of its annual revenue as a result.
Why AI chokes on bad data
Generative models are designed to answer questions even when context is missing. When underlying data is incomplete, biased or stale, models guess to fill the void and fabricate “facts.” A recent analysis noted that language models trained on incomplete datasets, biased sources and outdated information generate hallucinations that compromise business operations. When important details are missing, the model tries to “fill in the blanks,” producing plausible‑sounding but fabricated outputs. These problems show up not just in chatbots but also in code generation, marketing content and internal analytics.
Poor data quality isn’t a niche problem. A 2025 survey found that 84 % of IT leaders consider a configuration management database (CMDB) essential for decision‑making, yet only 17 % say theirs is fully accurate; 64 % of IT teams still haven’t adopted AI because of data quality and security concerns. At the same time, junior data workers report that nearly half of their time is spent cleaning data and fixing broken pipelines. More than two‑thirds of technical executives admit that their teams struggle to access all the data needed for AI programs (69 %) and to cleanse it into a usable format (68 %). No wonder 42 % of respondents experience data‑driven hallucinations.
Data stack complexity slows everyone down
Even when data exists, it’s often stuck in a tangle of tools. A 2024–25 report on data architecture found that 85 % of data teams cite tool integration as a top challenge, and 63 % spend more than one day a week maintaining their stack instead of delivering value. Teams manage five to ten tools just to move data around; more than 40 % of their time is spent switching between platforms. As the report notes,
“architecture complexity is the invisible force slowing down most data teams”.
Because current stacks focus on storage and movement rather than context and governance, teams must add lineage and metadata manually. The same study reports that 65 % of professionals believe combining strong data models with business‑ready data products is essential for compressing time to insight.
The result is a vicious cycle: messy data leads to hallucinations; hallucinations erode trust; engineers spend more time firefighting than innovating; AI adoption stalls.
Real‑time data hubs: beyond dashboards
Most enterprises still think of data as something you analyze in batches. But the high‑speed digital economy requires real‑time, data‑responsive systems. In 2025, companies must move beyond static dashboards and reports and build fast‑velocity data hubs that ingest, process and publish information as it is generated. Analysts at Value Innovation Labs argue that real‑time data solutions are now the foundation for smart enterprises because they enable companies to react instantly to opportunities and risks.
My CIO friends out there are thinking: “Real-time data flows for everything? But that's so incredibly expensive. What is the business case for this? How can we afford it?” That's super fair and in today's reality, maybe enterprises do need to prioritize which workflows warrant this level of streaming. But the days of real-time transaction processing, powered by AI, are coming and they're coming fast. Companies that are at least prepared for this architectural shift will be far better off when they integrate more and more AI agents into their workforce.
Traditional batch processing is no longer sustainable; data must be collected and acted on the moment it is produced. Real‑time platforms comprise ingestion pipelines, stream‑processing engines (such as Kafka or Flink), analytics layers, automated triggers and AI‑driven decision engines. These hubs support operational use‑cases (yes you read that right) such as:
Customer experience: Live data lets brands personalize interactions on the fly and detect service issues as they occur.
Operations and logistics: Real‑time routing and proactive inventory management reduce waste and improve service.
Finance and risk: Continuous monitoring detects fraud instantly and improves forecasting.
Cybersecurity: Live threat signals trigger automated defenses, which is essential as AI‑driven attacks accelerate.
These just‑in‑time data flows are critical if AI agents are to do more than make recommendations. Agents need high‑context, up‑to‑date data to take actions autonomously and avoid hallucinations.
A related topic that my colleagues and I have been talking about is the inevitability that AI will use data hubs like this for some workflows, but they may be interacting with live, operational systems for true, real-time agentic capabilities. There's still a strong case for prioritization today, but as companies' financial profiles shift in future years, making trade-offs to build more real-time pipelines will become more feasible.
Knowledge graphs: the Rosetta Stone of AI operations
As organizations wire up hundreds of systems, semantic consistency becomes the hardest problem. Fields labeled “subscriber,” “customer” and “account” might refer to the same entity, but AI cannot infer that without guidance. Modern knowledge graphs and ontologies solve this by providing a shared semantic layer that translates between systems. Totogi’s telecom ontology demonstrates how a knowledge graph built by AI can act as a digital Rosetta Stone for enterprise systems, automatically mapping disparate data and business processes into a common model. Once connected, every system interoperates through this ontology, allowing AI agents to understand that different fields refer to the same concept.
Knowledge graphs don’t just unify data; they also power orchestration. On top of the ontology layer, Totogi deploys AI “workers” that read documentation, write code and call APIs across systems. These agents compress release cycles from weeks to days by automating testing and coordination. More broadly, ontology‑based AI allows systems to communicate with each other and infer relationships, functioning as a Rosetta stone that provides richer context for decision making.
For businesses outside telecom, the same principle applies. Seth Earley, a knowledge‑engineering expert, describes ontologies as the “knowledge scaffolding” of the enterprise. By capturing relationships among products, services, roles and processes, an ontology becomes master data management for AI. It not only connects data models but also workflows and business logic, enabling AI to orchestrate tasks end‑to‑end.
Investing in the plumbing: an urgent priority
The sobering reality is that most organizations are not ready for AI. A survey cited by Databricks reveals that only 22 % of organizations believe their current architecture can support AI workloads without modification. As companies race to build AI agents, infrastructure will be the biggest investment area. Effective agents need to work across diverse data sources and models, and they require an end‑to‑end data platform that unifies data, governance and model evaluation.
Robin Sutara, Databricks’ Field CDO, notes that:
“A successful AI strategy starts with a solid infrastructure. Addressing fundamental components like data unification and governance through one underlying system lets organizations focus their attention on getting use cases into the real world”.
Investing in the unglamorous plumbing will pay dividends as your company grows its AI capabilities. It will reduce maintenance costs, accelerate deployment and ensure that AI acts on high‑quality data. Executives are beginning to recognize the link between governance and AI reliability; unifying metadata and governance across data and AI assets ensures models take actions based on trustworthy, up‑to‑date information.
Conclusion: build the pipes before chasing magic
AI will continue to transform business processes, but it cannot do so on a foundation of bad data. Organizations are losing millions and wasting talent because they treat data plumbing as an afterthought. Modern data stacks are too complex, slow and context‑poor to power AI‑first operations. Building a trusted data environment, high‑velocity data hubs, underpinned by knowledge graphs that harmonize semantics across systems, is essential. This plumbing may not make headlines today, but it is the difference between AI that hallucinates and AI that drives profitable decisions. Invest in the pipes now so that when AI agents take the wheel, they are navigating with clean, contextual, real‑time data.
References
1. Socialnomics article explaining how poor data quality—such as incomplete datasets, biased sources and outdated information—causes hallucinations in language models.
2. Socialnomics article describing how models fill gaps when training data is missing, leading to fabricated outputs.
3. BetaNews report showing that IT leaders view CMDBs as essential but acknowledge that poor data quality hinders AI adoption; 64 % have not adopted AI because of data quality and security concerns.
4. CDOTrends survey revealing that 81 % of organizations trust AI despite data inefficiencies, losing 6 % of annual revenue; data scientists spend 67 % of their time cleaning data; 69 % struggle to access data; 68 % struggle to cleanse data; 42 % experience data‑driven hallucinations.
5. Modern Data Company report noting that 85 % of data teams cite tool integration as a top challenge and 63 % spend more than 20 % of their time on maintenance.
6. Modern Data Company commentary stating that architecture complexity slows data teams and more tools create integration headaches.
7. Modern Data Company observation that AI needs data enriched with context, lineage and governance; a unified layer ensures data arrives business‑ready.
8. Modern Data Company finding that current fragmented approaches require manual context; 65 % believe combining strong data models with data products compresses time to insight.
9. Value Innovation Labs article describing how real‑time data solutions enable enterprises to react instantly and provide continuous, up‑to‑the‑moment intelligence.
10. Value Innovation Labs article emphasising that the shift from batch processing to real‑time insights is necessary in a high‑speed digital economy.
11. Value Innovation Labs article outlining the components of real‑time data platforms: ingestion pipelines, stream‑processing engines, analytics layers, automated triggers and AI decision engines.
12. Value Innovation Labs article listing operational use‑cases for real‑time data—customer experience, operations, finance and cybersecurity.
13. Totogi blog illustrating a telecom ontology that acts as a digital Rosetta Stone by mapping disparate systems and processes into a common model.
14. Totogi blog describing how AI agents on top of the ontology layer can read documentation, write code, call APIs and compress release cycles.
15. Earley Information Science article explaining that ontologies allow systems to infer relationships and act as a Rosetta stone, providing richer context and enabling systems to communicate.
16. Databricks blog noting that only 22 % of organizations believe their architecture can support AI and that infrastructure will be the biggest AI investment area; a unified data platform is needed for agents.
17. Databricks blog highlighting the link between data governance and AI reliability—unifying metadata ensures models act on high‑quality data and reduces operational costs.