Most companies collect more data than they ever put to use. Knowledge graphs solve that problem by connecting scattered facts into a single, searchable map of entities and relationships, turning disconnected records into insights a team can actually act on.
The gap is bigger than most people assume. Forrester research has found that between 60% and 73% of enterprise data never gets used for analytics, which means most of what an organization collects sits idle instead of informing a decision.
In this article, we’ll break down what a knowledge graph is, how it works, where it differs from an ontology, and how research and insights teams can put one to use.
What is a knowledge graph?
A knowledge graph is a structured way of organizing data that connects real-world entities, such as people, places, products, or events, through their relationships instead of isolated rows and columns. Rather than storing facts in separate tables, a knowledge graph links them so software and people can see how everything fits together.
Every knowledge graph is built from three core pieces:
- Entities (nodes): The actual things being described, such as a customer, a product, or a survey response.
- Relationships (edges): The connections between entities, such as “purchased,” “works for,” or “responded to.”
- Semantic metadata: Labels and context that tell a system what each entity and relationship actually means, not just that it exists.
Unlike a static spreadsheet, a knowledge graph is dynamic. It can be built from scratch by domain experts, learned from unstructured text using machine learning, or assembled from several existing knowledge management systems. As new data arrives, the graph updates itself instead of requiring a full rebuild, which is what makes it useful for fast-moving research and analytics work.
That distinction matters more than it sounds. A relational database needs someone to redesign its tables whenever the business asks a new kind of question. A knowledge graph absorbs new entities and relationships as they appear, so the structure grows alongside the data instead of constraining it.
How does a knowledge graph work?
A knowledge graph works by tagging raw data with identifying metadata, mapping that data against a defined schema, and returning connected results whenever a query runs. The process generally moves through four stages.
- Collection and tagging.
Raw data gets pulled from surveys, CRMs, support tickets, or public sources and tagged with identifying metadata, project details, and timestamps. - Schema and ontology mapping.
The tagged data is matched against a schema, built using the data management framework an organization already follows, so every entity is classified consistently. - Query and inference.
When someone runs a query, the system matches the request against known schemas and traces the relationships between matching nodes, often using natural language processing to interpret the question itself. - Output and visualization.
Results return as a connected graph rather than a flat table, showing not just the answer but how that answer relates to everything around it.
This structure is what lets a knowledge graph recalibrate itself over time with little manual intervention, unlike a traditional relational database that needs to be redesigned every time the underlying data changes shape.
Knowledge graph vs ontology: What is the difference?
An ontology is a formal, rule-based model that defines the categories of things in a domain and how they relate, without containing any specific data. A knowledge graph is what you get once you populate that model with real information.
The confusion is understandable since both use nodes and edges to represent structure. The difference sits in what each one actually holds.
| Aspect | Ontology | Knowledge graph |
|---|---|---|
| What it is | A schema or blueprint | The populated, real-world instance |
| Contains actual data | No | Yes |
| Example | “Book,” “Author,” and “Publisher” as generic classes | A specific book, its actual author, and its actual publisher, all connected |
| Changes over time | Rarely, since it defines rules | Constantly, as new data is added |
| Best described as | Ontology plus data | The result of applying an ontology to real records |
Picture a library. An ontology would define that “books,” “authors,” and “publishers” exist as categories and that a book has an author. A knowledge graph takes that same structure and fills it with an actual title, its actual writer, and its actual publisher, then connects all three so a query can move between them instantly.
Types of knowledge graphs
Not every knowledge graph serves the same purpose. Most fall into one of four categories.
- Enterprise knowledge graphs: Built from a single organization’s internal data, such as customer records, product catalogs, or research repositories.
- Domain-specific knowledge graphs: Focused on one field, like healthcare or finance, using specialized vocabularies such as SNOMED CT for medicine.
- Open or public knowledge graphs: Freely available structures like Wikidata, built and maintained by broad communities.
- Temporal knowledge graphs: Designed to track how relationships change over time, useful for trend analysis and forecasting.
Most organizations start with an enterprise knowledge graph focused on one department, such as research or customer support, before expanding it to pull in data from across the business. Trying to model everything at once tends to slow a project down rather than speed it up.
Real-world knowledge graph examples
Knowledge graphs already sit behind several tools most people use every day, even when the technology itself stays invisible.
Google Search
A question like “how many matchsticks would fit in an Olympic-sized pool” has no single webpage with the answer. Google’s knowledge graph correlates multiple data sources and known relationships to construct a reasoned response instead of matching keywords alone.
Netflix recommendations
Netflix builds a knowledge graph from what a person watches, rates, and skips, then connects that behavior to broader viewing patterns across similar audiences. The result is a recommendation that reflects both individual habits and wider trends.
Retail supply chain management
Large retailers connect historical purchase data, seasonal demand, and regional shopping behavior into a single graph. That connected view helps them forecast inventory needs and adjust supply chains before a shortage happens, rather than reacting after the fact.
Enterprise research repositories
Research teams use knowledge graphs to link survey responses, past studies, and customer feedback into one searchable structure. Instead of digging through old reports, a researcher can query the graph directly and get a connected answer in seconds.
LinkedIn’s professional network graph
LinkedIn connects people, companies, job titles, and skills into a knowledge graph that powers its search, job matching, and “people you may know” suggestions. A single profile update ripples through thousands of related connections almost instantly, which would be difficult to model in a traditional relational database.
Why knowledge graphs matter for market research and insights teams
Knowledge graphs matter to research teams because they turn scattered studies and survey data into a searchable, connected resource instead of a graveyard of old files. That shift directly supports market research work, where past findings often go unused simply because no one can locate them.
The clearest benefit is eliminating tribal knowledge. Institutional know-how stops living in one person’s head and becomes part of a searchable structure, which lowers the risk tied to tribal knowledge walking out the door when someone leaves. Once that knowledge is connected rather than scattered, teams also stop reconciling conflicting spreadsheets, since every entity ties back to one consistent record instead of several competing versions.
Time to insight improves as a direct result. A researcher who once needed hours to manually cross-reference old studies can instead query the graph and see connected findings in seconds, often by simply typing a plain-language question rather than writing a formal query.
Momentum behind this shift is measurable. MarketsandMarkets projects the global knowledge graph market to grow from roughly $1.07 billion in 2024 to nearly $6.94 billion by 2030, driven largely by demand for AI-ready, connected data. North America leads that growth, which tracks with how many US-based research and analytics teams are already restructuring their data practices around connected, query-ready formats rather than static spreadsheets.
Common mistakes and risks when building a knowledge graph
Most knowledge graph projects fail for structural reasons, not technical ones. A few mistakes come up repeatedly.
- Skipping the ontology step.
Teams that jump straight to data without defining a schema end up with inconsistent labels and duplicate entities. - Treating it as a one-time project.
A knowledge graph that isn’t maintained goes stale fast, since relationships and entities change constantly. - Ignoring data quality at the source.
A graph built on messy, inconsistent source data just represents that mess in a more connected format. - No clear ownership.
Without a named owner, nobody keeps tagging standards consistent, and the graph slowly drifts out of sync with reality. - Overlooking data governance.
Sensitive or personally identifying information needs the same access controls in a graph that it would need anywhere else.
None of these mistakes are unique to knowledge graphs. They are the same failure points that undermine most data projects, just harder to spot once the data is presented in a visually convincing, connected format.
How to evaluate a knowledge graph tool
Choosing the right knowledge graph tool comes down to how well it fits the data you already have and the questions your team actually needs answered. A short checklist helps narrow the field.
- Integration with existing systems.
The tool should connect cleanly to the CRMs, survey platforms, and databases already in use, rather than requiring a separate data migration. - Natural language query support.
Stakeholders outside the data team should be able to ask questions in plain English and get a usable answer. - Scalability.
The tool needs to handle growing data volumes without a full rebuild every few months. - Security and governance controls.
Role-based access and audit trails matter as much here as in any other data system. - Ease of ongoing maintenance.
A tool that requires a dedicated engineering team to keep running will struggle to get adopted broadly.
Tools built directly into a market research platform tend to score well here, since the survey data, past studies, and analysis all already live in one connected system rather than needing to be stitched together after the fact.
Where QuestionPro InsightsHub fits in
QuestionPro InsightsHub applies the same underlying idea as a knowledge graph to a research team’s own archive. Rather than leaving years of work scattered across folders, it connects:
- Past survey data and study results
- Qualitative feedback and open-ended responses
- Reports and analysis generated from earlier projects
That connected structure sits alongside QuestionPro’s broader survey software, which is where most of that underlying data originates in the first place.
Making connected data work harder
A knowledge graph is only as useful as the questions it can actually answer. Teams that treat it as a living structure, not a one-time build, get faster answers and fewer duplicated projects the longer they use it. The technology itself matters less than the discipline of keeping the underlying data connected, tagged, and current.
Frequently Asked Questions (FAQs)
No. A traditional database stores records in tables with predefined columns. A knowledge graph stores the same information as connected entities and relationships, which makes it better suited to answering questions about how things relate rather than simple lookups.
Google launched its Knowledge Graph in May 2012, shifting search results away from keyword matching and toward showing information about the actual “things” a query is about, like people, places, and organizations, alongside traditional links.
Smaller teams can benefit too, especially research or customer-facing teams juggling scattered data across tools. The value shows up whenever disconnected records make it hard to find an answer quickly, regardless of company size.
Retrieval-augmented generation is a method that lets AI models pull in outside information before answering a question. Knowledge graphs are increasingly used as that outside source, since they let an AI model retrieve verified, connected facts instead of relying only on its training data.
Timelines vary by data volume and quality, but most organizations see a working first version within a few months once the ontology is defined. Ongoing maintenance and expansion continue well beyond that initial build.
