Graph databases: Beyond recommendation engines

July 21, 2015



Graph databases allow businesses to draw powerful connections between many data points to make better use of big data.


Disparate data is streaming through the digital doors of corporations from every direction and it’s common knowledge that businesses are struggling to make sense of it. It’s not just the volume, variety and velocity of the data that is daunting, but the uncertainty around where to start.

Today’s data is like a grab bag comprised of everything from user-generated content to sensor data. Businesses must dig into the data and understand it before they can make use of it. Therefore, the ability to freely and effectively explore the data is as valuable as the data itself.

The marketplace’s response to Big Data’s unsettling uncertainty is data lakes. Dump data into a database and task smart people to tag it as they trek through it. In my opinion, here’s where things get interesting.

With graph databases, businesses can pull out the identified data and draw powerful connections between the data points: people, places and things. For example, who is going where, why and when and with whom? What are the hidden ties that bind them? If data lakes are the taxonomy of Big Data, graph database technology is the social sciences.

Once you experience the dynamic nature of exploring a graph database with the ability to query things such as “locate all executives interested in graph database technology who live in New Mexico and play racquetball,” it’s hard to return to the rigid world of relational databases.

Relational databases store data in tables and the relationships must be pre-defined before they can be queried. You must foresee ahead of time exactly how you plan to use the data. Relational databases are not conducive to experimenting with data in today’s diverse, untamed, unpredictable data environment. Relational databases are for reporting, not exploring.

Ironically, graph databases are more relational than relational databases. They are architected to support how humans naturally think in terms of connections between objects, people, places, and actions. Graphs are the reason you are able to connect with long-lost friends over your social networks, what products you might be interested in based on your previous purchases, and who may or may not be your soul mate.

The ability to employ graph databases expands far beyond recommendation engines and is only limited by the imagination. For example, graphs are being used to:

  • identify ideal strategic partnerships based on customer interests
  • monitor employee behavior to determine influencers
  • determine which charities to award donations
  • expand the number of target customers
  • ferret out money launders
  • predict the flow of venture capital investments in emerging technology

The disparity of our data is a reflection of our fragmented world. The ability to illuminate and cultivate connections between people, places and things will translate into valuable currency for corporations. That’s why graph database technology is fast moving from an emerging technology to a disruptive one.



Chris Curran

Principal and Chief Technologist, PwC US Tel: +1 (214) 754 5055 Email

Vicki Huff Eckert

Global New Business & Innovation Leader Tel: +1 (650) 387 4956 Email

Mark McCaffery

US Technology, Media and Telecommunications (TMT) Leader Tel: +1 (408) 817 4199 Email