Our Chief Technology Officer Sean Martin and Vice President of Engineering Barry Zane will be speaking at the Enterprise DATAVERSITY: Data Strategy and Analytics Forum next week in Chicago, September 19 - 22, 2016. The event brings developers, business executives, analytics and data professionals together to discuss the key data strategies necessary to create a successful, modern data analytics organization.
The evolution of big data over recent years has resulted in a new role that has been gaining industry traction – the Chief Data Officer (CDO). As executives begin to recognize the business value residing in their data, they are focusing now more than ever on finding ways to manage and govern this data. The CDO is emerging as a key player in the enterprise by helping to establish and maintain data governance, quality, architecture and analytics, enabling firms to more productively harness information to manage risk, reduce cost and drive innovation.
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?
Cambridge Semantics’ #SmartDataChat on Twitter May 25, 2016 provided an engaging conversation with several well-respected industry observers regarding data governance in the era of big data discovery and analytics. In addition to our own resident expert Marty Loughlin (@mloughlin) , the #SmartDataChat participants were a “Who’s Who” of today’s leading big data and IT journalists. Below is a sampling of some key Tweets that were posted:
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®.
Exploratory analytics represents the evolution—and perhaps culmination—of conventional analytics and business intelligence options. It’s a combination of real-time data discovery and ad-hoc, graph-aware analytics that provides automatic, self-service insight for end users on all their data.
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.
Unstructured data is all around us: in news stories, web pages, journal articles, social media posts, patents, research reports, presentations, and a variety of other sources. These items are unstructured in that they don’t start out with a predefined, explicit schema or structure. Historically, these documents have been read by humans looking to find information relevant to their particular tasks or roles. In today's deluge, however, the need for scalable reading, repeatability, traceability, and speed has driven the advent of text analytics platforms.
Data lakes are no longer anomalies. Consolidating all of an organization’s data—unstructured, semi-structured, and structured—into a single repository for integration, access, and analytics purposes is rapidly emerging as the preferred way to manage big data initiatives.
Recent developments in big data technologies have significantly impacted the prowess of contemporary analytics; the most profound of these involves the deployment of semantically enhanced semantic data lakes. These centralized repositories have revolutionized the scope and focus of analytics by enabling organizations to analyze all data assets with a specificity and speed that wasn’t previously available. The value derived from such an approach improves the analytics process at both the granular and macro levels, expediting everything from conventional data preparation to informed action.