Graph databases are the fastest growing category for a reason. We called this the Year of the Graph Database long before the Gartner’s of the world, and the market seems to stand behind this. Its success can be put down to many factors with the underlying reason being that they have simplified vast and complex information into intuitive relations that are easy to read and represent visually.
Considering how databases are ever-growing, contain difficult to understand and interpret information; the graph database transforms that data into easy to work on, evolve, as well as traversable data that can be multi-collaborative with colleagues.
Graph databases can succeed where others can’t, especially in scenarios that involve leveraging connections and powering semantics.
A generation of fast, scalable graph databases, is opening up a world of business insight and performance. Some of the use cases Victor Lee, Director of Product Management at TigerGraph, explored were IceKredit, an innovative FinTech transforming the near-prime and subprime credit market in United States, and wish.com, delivering real-time personalized recommendations to increase eCommerce revenue.
“Using graphs to not only describe their operation but to know about your customers 360 [degrees]…describing all the things you know about your customers and what other connections they have.
“[By] merging multiple sets of data and then performing complex analytics powered by the real-time operational [graph] analytics.”
Combining data from multiple sources
To make decisions, businesses have to combine their databases with non-proprietary data. But combining diverse data from multiple sources is a complex task. Technology developed by Ontotext can help build Big Knowledge Graphs and apply cognitive analytics to them to provide entity awareness across several industries such as biopharmaceutical company AstraZeneca, the world’s largest engineering institution, IET and U.S defence contractor, Raytheon.
The Ontotext CEO Atanas Kiryakov, also mentioned how their technology was used to predict the Brexit vote:
“Back in June 2016, we predicted Brexit based on an analysis of a million tweets…based on impressions, interactions and influencers…it was clear cut that the support for Brexit alone was twice [as high].”
George Anadiotis, Connected Data London co-organiser, ZDNet contributor and Knowledge Graph practitioner, noted that this technology can be leveraged to predict other outcomes as well.
“The key lies in the ability to combine quantitative and statistical bottom-up analysis with qualitative analysis based on expert-provided knowledge.
“Although this technology has been available for a long time, he went on to add, popularizing it and using it at scale has been the key challenge to its adoption.
“Today, however, we see the leaders in technology doing precisely that, aided by techniques such as machine learning. This shows the way forward, and with adopters such as Airbnb, eBay, Salesforce, Uber, and Zalando, we can say 2018 was the year knowledge graphs went mainstream.”
Managing content, collaboration and delivery
Celum is a leading cloud software manufacturer. Their Content & Collaboration Cloud optimizes the complete life cycle of digital content and the interaction of people in teams. With over 800 customers in 35 countries, Celum set out to develop its own architecture to provide the best user experience. Rainer Pichler, Software Architect at Celum, shared how they did it using Graph Databases.
“Our mission focuses on two aspects: to help customers manage digital content, in order to find it again and want to publish it to other platforms [such as] content delivery networks. [Second,] to change how people collaborate and work together.
“Regarding content, the users are themselves data modellers…every company has different metadata attached that is important to them.”
It’s becoming clear that the success of graph databases is due to its applicability to every major industry from pharmaceutical to political and FinTech to mainstream media.