An organization’s staff is its most valuable asset, and the human resources function is critical to the stability and growth of business operations.
Real-world events demonstrate our inability to understand rapidly and accurately what we already know. In other words, we cannot answer questions completely, despite the fact that we may hold the requisite data. For example, if someone attempted to enter the United States (US) at an airport, and US officials initiated a query to the “system” and found nothing, that person may enter the US erroneously. This might occur because US officials asking a question such as “What do we know about this person?” cannot assuredly answer it – and not in a timely fashion.
“If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.” - Sun Tzu
Most Competitive Intelligence (CI) and Business Strategy practitioners are likely to have come across this immortal advice from Sun Tzu. One does not cease to be amazed at how this simple fact underlies the multi-million dollar intelligence gathering industry.
In this webinar Steve Hamby, Managing Director Government, discusses semantic graph technology to help Federal Government CIOs and their agency staff that are researching enterprise data management and mining tools understand how Smart Data Lakes can be a superior mechanism for addressing their top data priorities. Here are the slides from his presentation.
As we approach the end of 2016, we sat down with our CEO, Chuck Pieper, to discuss the future of big data and get his predictions for 2017. Here are his thoughts.
In September 2016 Cambridge Semantics attended Strata+Hadoop World 2016 in New York, NY. While we were there, Marty Loughlin, our VP of Financial Services, spoke to a gathering of attendees about who we are and what our platform does. Here is his presentation.
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.
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?
Enterprise text analytics is an exciting and powerful area gaining traction recently. When one moves on from purely departmental solutions to a perspective of an enterprise text analytics fabric, new possibilities emerge to empower a wide array of roles and functions.
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.