Make Better Sense of Your Data: How Knowledge Graphs Can Enhance AI Models in the Enterprise
How Knowledge Graphs Can Enhance AI Models in the Enterprise
You’re playing a game of Connections in your New York Times app. Four of the words or phrases listed are enterprise, artificial intelligence, decision-making, and integrity. What’s the common thread?
Data.
It’s no secret that large enterprises store a lot of data. Each team across the 1,000+ person organization has its own set of unique data, which is meant to help employees make informed decisions about their business. The far-too-common problem, however, is that this data is too disparate and too voluminous to be helpful without costing an exorbitant amount of time and money to organize and analyze.
While artificial intelligence (AI) has proven to be an effective lever to pull for consolidating and making sense of this data, it too requires high-quality, clean data to operate efficiently, and so we often hit a roadblock again.
Last year, IBM surveyed enterprise-scale companies on their AI adoption and found that too much data complexity was a key barrier for 25% of organizations. Fast forward nearly a year and still, when Glasswing’s Founder and Managing Partner, Rick Grinnell, speaks with chief information security officers (CISOs), concerns around data integrity and availability prevail.
Knowledge graphs, or representations of interconnected data, present one solution to this common problem. By capturing facts and recognizing the relationships between them, knowledge graphs enable AI systems to derive meaning and context from vast amounts of data and draw more accurate conclusions.
Three AI Concerns Knowledge Graphs Address
Since the beginning of AI’s mass consumerization in 2022, three major concerns have dominated the industry: hallucinations, data integrity and availability, and explainability. By connecting different data sets and identifying relationships between them — similar to how a human brain connects certain locations with people and memories — knowledge graphs help alleviate each of these concerns.
1. Hallucinations
When an AI system is trained on one distinct set of data, there is a higher propensity for that AI system to hallucinate, or present a false answer as true, because of its limited resources and understanding. When a knowledge graph is added to the system, the standard rows and columns of data on which AI is trained turn into a dynamic web of nodes and edges. With this web, the AI can identify the data that is most relevant to the question being asked and produce the most relevant and correct response as a result.
2. Data Integrity and Availability
The success of any AI model is largely dependent on the data it is trained on. It’s why even content generation tools like ChatGPT have developed versions that allow businesses to train and refine the model for more customized and accurate responses. In order to successfully train AI models on business data, however, the data needs to be accurate and accessible — something knowledge graphs can help with. By consolidating all of the valuable data that exists across an enterprise organization into one format for the AI model to reference, knowledge graphs ensure AI models have access to a centralized, up-to-date, source of truth that helps them more accurately understand the relationship between different data entities without burdening data science teams.
3. Explainability
In elementary school math class, we were told to show our work so teachers could identify where we went wrong if our answers were incorrect. Similarly, AI models’ outputs need to be explainable so that humans can quickly troubleshoot and improve the models. By providing traceable connections and clear data lineage between different entities, knowledge graphs allow for this transparency, increasing accuracy and trust in the AI models as a result.
3 Ways Knowledge Graphs Can Improve the B2B Experience
When an AI model has fewer hallucinations, is trained on a robust set of accurate data, and is transparent in how it draws conclusions, the benefits for both your business and your buyers are plentiful. Here are three ways knowledge graphs can provide better B2B experiences:
1. More Personalized Buying Journeys
By making sense of a variety of real-world entities, knowledge graphs help AI models put together holistic stories that provide human sellers with a more comprehensive understanding of their buyers. If a buyer is returning to a website after talking with the sales team, engaging with content, and attending an event, for example, a knowledge graph can help the AI model make sense of all these touch points and suggest the best next step. Not only does this result in a more streamlined experience for the potential buyer, but it also relieves the sales team of spending hours sifting through data points, trying to put together their own story.
2. Data-Driven Decision Making
By gathering data into one holistic view, knowledge graphs help enterprise teams make sense of the hundreds, even thousands, of data sets generated org-wide. Not only does this diminish the pressure put on data science teams, but it also streamlines workflows and increases transparency in decision-making. In taking the leg work out of data interpretation, teams are better enabled to move projects forward without having to wait for back-and-forth feedback.
3. Increased Cross-Functional Efficiency
By linking disparate data silos across departments, knowledge graphs can save hours of meetings and dollars spent on analysis. With knowledge graphs, when a potential buyer takes a marketing action, for example, rather than a marketing team member sending a message to the appropriate sales rep and hoping it doesn’t get lost in a cluttered inbox, that data point is automatically sent over to the sales dashboard and added into the holistic view the sales rep has of their buyer. With this information in hand, sales reps can be more efficient in their conversations by knowing exactly where the buyer is on their journey and what their next steps should be.
The Glasswing Perspective
Like any element of artificial intelligence, deciding whether to include knowledge graphs in your AI model requires a keen understanding of the problem you are trying to solve. While knowledge graphs are proven to increase accuracy and handle larger computational loads, doing so comes at a cost. Implementing a knowledge graph into an AI system tends to be more expensive than a traditional retrieval-augmented generation (RAG) data model and is slower to set up. At the same time, the increased accuracy predicted to come with knowledge graphs could reduce the risk of customer churn or costly support hours. When you evaluate your use case against your pre-existing resources, you will be able to decide if knowledge graphs are right for your AI model.