Earlier this month, the Cambridge Semantics team set off for Grapevine, Texas, for the Gartner Data & Analytics Summit 2017 to join more than 3,000 big data industry customers, Gartner analysts and solution providers in the continuing discussion on driving business intelligence and analytics forward.
With compelling business use cases, adoption of open industry standards and enterprise deployment of high performing scalable platforms, 2016 was a breakout year for the adoption of semantic graph technology and the Smart Data Lake® in Financial Services.
Topics: Smart Data Lake
The data lake is a modern and rapidly evolving data architecture. It promises ubiquitous access to enterprise data with compelling benefits in terms of cost and agility. Yet, in many cases, this promise has not been realized.
"So what?" you might say. Another hyperbole-fueled headline in tech is hardly a notable event. To answer, let's start with what we did.
The challenge for analysts seeking trading opportunities that outperform the market is not a lack of information. It is an over-supply of information from widely disparate sources. How do you sift through the overwhelming flow of reports, news feeds, articles, blogs and social media posts to find the sentiments, relationships, patterns, unique insights and powerful nuggets of information that drive performance?
We are at an inflection point in the financial services industry. The evolving and overwhelming demands of regulatory compliance have forced organizations to acknowledge the need for data governance and most are developing their strategy.
State Street Bank, The EDM Council, Dun & Bradstreet, Wells Fargo and Cambridge Semantics completed an engagement to harmonize State Street's Interest Rate Swap data with Dun & Bradstreet's entity hierarchy data using the Financial Industry Business Ontology (FIBO) and Cambridge Semantics' Anzo Smart Data Lake®.
On my flight back to Boston from Grapevine, TX, where we spent three exhausting and exhilarating days at Gartner BI & Analytics Summit, I am reflecting on the great interest shown in Cambridge Semantics Smart Data Lake at this event.
Many data lake projects achieve their IT objective: cheap storage of all enterprise data in raw form, but fail in their business objective to deliver value from this data. Why? Because making the data accessible and usable for business users is hard.
Legacy applications that have exceeded their useful life can be expensive to maintain. They often require specialized skills and old versions of software and hardware to support. But, they can also contain very valuable data that needs to be retained for business or compliance purposes.