Uncovering Data Connections for Competitive Advantage in 2020
During a recent presentation, Rita Sallam, Distinguished Vice President Analyst at Gartner Research, presented the Top 10 Trends in Data and Analytics Technology for 2020. These trends will have significant disruptive potential over the next three to five years, according to Gartner.
To be included on that distinguished list a technology needs to be intelligent, emergent and scalable. These attributes are necessary for businesses, which are awash with data due to digital transformation, to be able to identify what is most important and what actions to take (or avoid).
Graph analytics is on Gartner’s list of the top 10 technology trends.
Gartner predicts that graph technologies will facilitate rapid contextualization for decision making in 30% of organizations worldwide.
Graph indisputably surpasses the bar for being intelligent, emergent and scalable. There is no other technology capable of looking deep inside huge datasets to uncover the connections that enable businesses to make better informed and more intelligent decisions.
Act Now. Don’t Be Left Behind
In her presentation, Rita had some encouragement for business leaders, “Don’t just react to trends as they mature…educate and engage with other leaders about business priorities and where data and analytics can build competitive advantage.”
Here are three business leaders who have been proactive about exploring graph.
“We deal with a humongous amount of data, electronic medical records, and comprehensive health records,” according to Vishnu Maddileti, director of data services and analytics at Amgen. “With 5 billion vertices and 20 billion edges, it’s huge data and finding inferences in that data is not easy, but TigerGraph has scaled for us.”
Martynas Sukys, product owner at Pagantis said, “Our customers expect a rapid, friction-free on-boarding experience and we are committed to providing it to them. TigerGraph’s latest performance and usability enhancements will help us maintain our competitive advantage and outperform in a crowded marketplace.”
“As we were doing the MDM implementation, we realized we needed a better tool for relationships, to find how one individual relates to another,” says Ely Turkenitz, Information Systems Manager at Santa Clara County. “The questions that graph databases answer are hard to come by in RDBMS or it takes forever. We needed a better tool to find relationships and TigerGraph was just that.”
Don’t Settle for Less
There’s never been a better time to upgrade to graph. Businesses who have done so are beginning to outperform their competition. You can read reviews by people just like you who already using our technology here.
You can also take us for a test drive here or consider becoming a Certified TigerGraph Associate – learn more about how to do that here.
Gartner predicts that graph technology will “enable more complex and adaptive data science” but, as we have seen, this will only be possible with third-generation graph technology. TigerGraph’s technology is transforming businesses – don’t be left behind using a legacy system.
Coming to a City Near You
Connect with TigerGraph at a future Graph Gurus event near you for an in-depth graph workshop and networking. These hands-on workshops will take attendees through TigerGraph Cloud, demonstrating seven key data science capabilities and key use cases with TigerGraph’s intuitive GUI, GraphStudio and GSQL queries. For more information and registration, please click here. Upcoming events include:
Dr. Jay Yu is the VP of Product and Innovation at TigerGraph, responsible for driving product strategy and roadmap, as well as fostering innovation in graph database engine and graph solutions. He is a proven hands-on full-stack innovator, strategic thinker, leader, and evangelist for new technology and product, with 25+ years of industry experience ranging from highly scalable distributed database engine company (Teradata), B2B e-commerce services startup, to consumer-facing financial applications company (Intuit). He received his PhD from the University of Wisconsin - Madison, where he specialized in large scale parallel database systems