by Brett Hurt

How knowledge graphs create data-driven cultures

Apr 17, 2019
IT LeadershipIT StrategyTechnology Industry

Isn't it time for you to get as smart as other data-driven industry leaders?

FinTech > financial technology [abstract image]
Credit: Polygraphus / Getty Images

Everyone has had this experience: you’re sitting in a bar or the airport and your head starts bobbing to a catchy song. Type a few lyrics into Google and framed at the top of the first results page you get the song name, plus contextual information like the artist, genre, release date, related songs people search for, music videos you can play without leaving the page, and links to listen on Spotify and other music services. That experience is powered by a knowledge graph. And it can be the backbone of data-driven culture within enterprise companies.

Most businesses have more data than they know what to do with and much of it buried in silos and hard to access. When speed is the ultimate competitive advantage, it follows that your teams should be able to quickly surface useful data while it’s still useful. But at most companies, this takes way too long. Usefulness declines as time slips away.

Your business won’t thrive on Big Data if it’s not useful. Knowledge graphs help make data valuable to more people. They are the secret to speed and scale because they enhance the way people collect, use, and understand data. Amazon, Facebook, Goldman Sachs, Google, and other heavyweights have figured this out already—now it’s your turn.

Let’s start at the beginning. What exactly is a knowledge graph?

When Google introduced its knowledge graph in 2012, it described it like this:

“The Knowledge Graph is a huge collection of the people, places and things in the world and how they’re connected to one another. With this technology, Google can get you the best possible answers and help jump start your discovery.”

Knowledge graphs are large networks of entities and their semantic relationships. Compared to other knowledge-oriented information systems, the distinctive features of knowledge graphs lie in their special combination of knowledge representation structures, information management processes, and search algorithms. Google’s knowledge graph allowed users to search for things, people or places, rather than just matching strings in the search queries with those in Web documents—”things, not strings.” 

Right now, knowledge graphs are changing data integration, search, analytics, and recommendations.  Underneath the hood, you’ll find Linked Data. Linked Data connects data using the same architecture that powers the web. The technology has had extensive academic R&D over the last couple decades and is already successfully deployed within large organizations that amass huge data assets —Goldman Sachs, among others. As Sir Tim Berners-Lee said in his powerful 2009 TED Talk:

“When you connect data together, you get power in a way that doesn’t happen just with the web, with documents.” —Sir Tim Berners-Lee

This means big changes for enterprise data and employees

At the 2019 Gartner Data & Analytics Summit, I was happy to hear so much talk of data literacy and data-driven culture. As a business leader, you have a responsibility to make your data as useful as possible. In my last CIO article, I talked about the need for Chief Data Officers to have more power in their organizations so they can empower all employees instead of just the elite few. Knowledge graphs can help.

Creating a Google-like experience with better search, discovery, and smart suggestions doesn’t require huge, expensive changes to your existing data stack. In a modern data catalog, data and analysis flow into a knowledge graph that traces the connections and relationships across your data, teams, and processes. The knowledge graph keeps data, analysis, and everything people need to find, understand, and use data quickly together at all times. You can see how it has been used, who uses it, what questions people have asked, what queries and visualizations have been shared by teams, and more. It makes data interoperable, so you can join and query across different datasets, sources, even filetypes. It means you can search many other dimensions of data, not just the filename or cell values.

By making data more usable to more people, you’ll grow its value faster. The more you use the knowledge graph, the more connections and relationships will be found. As the knowledge graph gets smarter, so do your people.

Bridging the data divide

Knowledge graphs should be a data executive’s secret weapon. It’s time for you to get as smart as Amazon, Facebook, Goldman Sachs, Google and other data-driven leaders. It’s time to end the data divide. It’s time to unite your people with a knowledge graph and watch collaboration and knowledge increase exponentially.