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.
Data originates everywhere in the healthcare industry – in laboratories, within insurance agencies, in Electronic Medical Record (EMR) systems, etc. – and each of these sources represents a valuable set of information. Healthcare professionals need a comprehensive method that collects and transfers data across systems in a way that is reliable, secure and compliant. But because healthcare system formats are rarely ever the same, it becomes challenging to securely and efficiently share data between vast and diverse data and information sources.
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 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.
In the pharmaceutical world, clinical trials are essential to any medical research study, such as testing the benefits and risks of a specific medical treatment or drug. But because massive amounts of data is collected, analyzed and reported, investigators and doctors running these trials require a comprehensive set of standards to support the exchange of confidential information.
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.
There is no simple way to define the Association for Cooperative Operations Research and Development (ACORD), or its significance to the insurance industry. It is simultaneously a global community of insurance professionals and a certifying agency, an established semantics framework and standards-based approach to data management, the reference architecture of choice and a burgeoning body of knowledge for business domains and the data landscape.
Historically, understanding the contents of unstructured text has required a great deal of time and effort by experts to read stacks of documents and manually extract key information that is then consolidated in spreadsheets or structured databases. By using sophisticated semantic processing to enrich text analytics results, however, business analysts can set up text processing pipelines that can automatically analyze text content by extracting entities and relationships, analyzing sentiment and summarizing or classifying documents, emails, social media, websites and more.