A picture is worth a thousand words they say, and this remains true for data discovery and analytics. For centuries, graphs and maps have been used to help us simplify, understand and convey large amounts of data in a universal manner. And with the evolution of technology, we can now visualize increasingly complex information at lightning-fast speeds.
Conventional data analytics utilizes dashboards, visualizations, search, and other tools to determine appropriate data for integrated, targeted use cases. Smart data analytics techniques, on the other hand, leverage linked data graphs, comprehensive data models, and a semantic standards-based approach to publish results to those same popular tools.
We wish to take this opportunity to introduce Steve Hamby, recently announced as Managing Director Government here at Cambridge Semantics. In this newly created position, Steve serves Cambridge Semantics’ federal government customers seeking insights from big data discovery, analysis and data management solutions to provide more timely, accurate and customizable information to staff, citizens, media and businesses.
In recent years, government agencies have developed an interest in semantic-based smart data discovery and analytic solutions. From enabling more transparent relationships with the public to improving productivity, government organizations are learning the value of big data analytics.
Comprehending semantic technology is no longer an arduous task for the back offices of data-savvy organizations. Business users and C-level executives are starting to comprehend the basics of the technologies that are increasingly impacting their jobs.
The cliches are well known by now: data scientists spend the majority of their time simply preparing data for analytics, inheriting the responsibilities of IT teams that traditionally took months to process simple query results.
Conventional data discovery utilizes dashboards, visualizations, search, and other tools to determine appropriate data for integrated, targeted use cases. Smart data discovery techniques, on the other hand, leverage linked data graphs, comprehensive data models, and a semantic standards-based approach to publish results to those same popular tools.
In this webinar Barry Zane, our Vice President of Engineering and an industry expert in Database technologies, discusses Semantic Graph databases as the next step in the evolution of Relational databases.
Analytics has profoundly altered the retail industry. The combination of real-time, predictive analytics—in conjunction with traditional historic analytic capabilities—has reconfigured the way organizations facilitate customer engagement, supply chain management, and inventory in both e-commerce and brick and mortar settings.
Data lakes are quickly becoming a hot topic as enterprises determine how best to organize and access the large volume of data they have been generating. Data Lakes are attractive for several reasons, including their ability to expand data across the enterprise while maintaining trust and security with data governance.