The more data a business needs to collect and use, the more knowledge graphs become necessary. But is there a business justification for the value an organization can expect from the knowledge graph? Enterprise companies need to assess the costs, risks, and return on investment (ROI) of the technology before purchasing.
Validating Value
Forrester Consulting’s most recent Total Economic Impact (TEI) studies measured the value of knowledge graph technologies in specific data analysis projects. These studies reported that the use of knowledge graphs by pharmaceutical, telecommunications, automotive, and banking firms have typically resulted in:
- 3x faster development of data analytics applications
- Three-year ROI values of 300 to 400%
- Data infrastructure cost savings of 5 - 20%
- Data science computation time reductions of 50 - 95%
- 30 - 50% reduction in data security efforts
- Digital transformation cost savings from IT modernization and rationalization
Forrester’s TEI studies of several graph technologies concluded that mid-sized enterprises taking full advantage of knowledge graphs could realize up to $10 million in benefits over a three-year period, driven by improved analytics insight, reduced DataOps cost, greater data scientist productivity, manufacturing, supply chain, compliance, and operational risk reduction, and increased revenue. Those are outstanding benefits!
As Forrester Consulting addresses in their TEI studies, the criteria for success in graph computing are the values Katana Graph embraces in the design of Katana Graph Engine. As a result, Katana Graph brings enterprise data into sharp focus faster than ever before – without disruption, overspending, or overhaul. Enterprises can slash time to market and innovate in ways never imagined before with a breakthrough graph intelligence platform.
Collaboration is Key to Ingenuity
Katana Graph delivers outstanding benefits and leads the innovation in graph computing. Alongside Intel, Katana Graph is developing a Python API and MetaGraph plug-in that expands opportunities for open core community and more data insights.
"We are proud to partner with Intel on this initiative within the open-source Python community," said Keshav Pingali, co-founder and CEO of Katana Graph. "At Katana Graph, we are dedicated to empowering data scientists with tools to derive deep value from their data. Through this collaboration with Intel, we are accelerating how end users can write their algorithms with a Python API."
At the heart of Katana Graph's solution is the Katana Graph Engine with its partitioner, communication, virtualization, and storage technology modules. This software, along with the culmination of more than a decade of advanced research in graph technology and high-performance computing, will expand the role of graph computing across the technology industry. "We are proud of our collaboration with Katana Graph on this graph analytics library," said Wei Li, Intel Vice President and General Manager of Artificial Intelligence and Analytics. "Our customers and the data science community will benefit from having this open-source package for analyzing linked datasets."
Click here to learn more about the Katana Graph and Intel collaboration.
Katana Graph is dedicated to empowering data scientists with tools that let them derive deep value from their data. Whether structured or unstructured data, Katana Graph can greatly improve the insights and opportunities uncovered from your organization’s data. Click to schedule a meeting.
