Throughout history, humans have always shared knowledge, from cave drawings in 15,000 BC to transcribing books and encyclopedias or simply passing down life lessons from generation to generation. With the advent of printing presses, libraries and the internet, it became easier to search for and discover specific information.
Today, we can easily find documents and video content on any topic. But what if you want to speak with a person about specific questions and concerns rather than wade through oceans of chat channels, documents, videos, and social media posts? Finding verified experts almost instantly is an invaluable asset in the face of information overload and a competitive global business landscape.
According to the Panopto Workplace Knowledge and Productivity Report, the average large US business lost $47 million in productivity annually due to inefficient knowledge sharing. Workers spent so much time looking for information, waiting on colleagues or recreating existing expertise that it led to project delays, missed opportunities and frustration.
That’s why easy-to-use knowledge management systems for internal and external expertise can give businesses an edge. At Lynk, Head of AI and Data Products Brian MacDonald, Chief Data Scientist Koushik Pal and their team have been building a robust people-centric search engine using a branch of artificial intelligence (AI) called Natural Language Processing (NLP), which helps computers interpret text or speech, measure sentiment, identify valuable takeaways, generate summaries and assign categories, tags and topics.
“The difference between Google and Lynk is that Google helps you search for text, something that’s written on a web page or document,” says Pal. “But Lynk helps you search for people who have the best, most relevant answers.”
With its sophisticated technology, Lynk can help companies build an internal corporate knowledge management hub and a bank of external expertise. Here’s how it works:
Before identifying experts, MacDonald and Pal first collect data from a wide range of credible sources of information — academic publications, educational background, work history, websites, and third-party data — to understand people, their history, background, behaviors and experience.
“We try to answer questions, such as: What role did they have? Is there meaningful potential that they're actually an expert? How would we determine that through our research?” explains MacDonald. “That's the core of what we're trying to do — deeply understand and gain perspective on people — and then turn it into something useful.”
Using NLP, Lynk can then process the historical data and recent documents to extract meaningful information and characterize experts within a specific area. If they are researching healthcare experts, for example, the results may include primary practitioners, as well as research scientists, teachers, hospital administrators, and so on, with timely experience.
In a corporate context, Lynk may look for people who have recently held senior leadership roles. “More often than not, experience is a nice proxy for expertise,” says MacDonald. “We can assume if you have been a Director or Vice President, have a long history in that industry, and held the role currently or recently, you will probably have valuable expertise.”
Drawing from the data, Lynk creates an individual profile that looks more like a knowledge graph (an interconnected graph of people, things, activities and concepts) than a linear, static CV. Suppose a person works in the medical field. In that case, their profile would not only include their certifications and educational background but also less obvious knowledge gleaned from documents, publications, related topics, implied knowledge, and person-to-person conversations. Most importantly, knowledge graphs capture semantics — implied meaning based on associations between a person’s role, relationships and perspectives.
Once an individual begins conducting consultations, sharing documents and responding to Q&As via Lynk’s knowledge platform, the system uses SpeechToText algorithms to generate transcripts, and then uses NLP to parse those transcripts and identify keywords, topics, named entities, summary, context and sentiment. The information is then assigned or linked to the profile, expanding the knowledge record over time.
“The more we know and the more data sources we have — articles that you have written or calls you have taken — the more data points we have and the more robust your profile becomes,” says MacDonald. “We want to create a living record of expertise that’s totally verifiable, grounded in work experience and history.”
Naturally, as a person’s knowledge record evolves, they also become more searchable in the system. “From a searchability perspective, your value increases as the amount of information attached to you increases — it's a human-centric discovery and search mechanism that brings together all of these capabilities,” adds MacDonald. “It also creates an interesting motivational situation where, as a professional, you may want to build your expertise profile.”
Whether looking for meaningful consumer insights, an outside perspective on a new investment strategy or seeking insights from a colleague internally, the ability to find the right expert in a pinch can make all the difference amid fast-moving global markets. Here are a few promising use cases:
1. Discovering consumer insights experts and deeper customer insights
Understanding the evolving needs of your customers can fuel long-term growth and resilience. By using a knowledge bank, strategy, insights, marketing and product research teams can gain valuable feedback on key decisions, from price points to customer segmentation, branding and go-to-market strategies. In addition, by aligning the insights teams with experts within your company’s target demographic, the feedback loop can be direct and tailored for the outcome.
With a deep understanding of the company's background and experts’ experience, a knowledge bank can suggest ideal matches between insight seekers and insight providers. And the more you use a bank for conversations, Q&As and long-term data sharing, the more meaningful the accrued knowledge becomes over time.
2. Custom advisor networks for private equity and venture capital firms
Venture capital and private equity firms provide another interesting example. With fluctuating markets, many niche VCs and portfolio management companies need to dig deeper to discover new opportunities for investment or risk mitigation — a knowledge bank brimming with internal and external insights can help them get that competitive edge.
3. Knowledge hubs for businesses that need quick, easy solutions to better manage employee knowledge
Building an internal knowledge bank for your company is as essential as discovering external experts. “Much of the information in a corporate environment or any other environment lives in somebody's head, rather than on paper,” says Pal. All too often, medium- and large-sized companies struggle to manage and share knowledge — after all, it’s challenging to identify knowledge, codify it, share it, and build upon that bank.
By using Lynk’s solution, companies can build their internal knowledge hubs so that colleagues can find the right person with the right knowledge in their network. This is especially important with hybrid and remote work on the rise, where employees can’t simply pop down the hall to ask questions or seek guidance. But a robust internal knowledge bank can save time and resources while empowering employees and boosting productivity.
“This problem has existed ever since humanity has existed. We have always looked for people to answer our questions,” says Pal. “Now, we finally have the technology and data to democratize knowledge and make it easier to find who knows what.”