An organization’s staff is its most valuable asset, and the human resources function is critical to the stability and growth of business operations.
“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.
The complexity of data management and advanced analytics is daunting to many organizations, but a new emerging class of software enables companies to spend less time managing their data and more time acting on the insights they provide.
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.
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.
Richard Mallah, our Director of Advanced Analytics recently participated at the The Twenty-ninth Annual Conference on Neural Information Processing Systems (NIPS), focused on machine learning and computational neuroscience. Richard was a panelist at the symposium higlighting The Societal Impacts of Machine Learning. This symposium aimed to turn the attention of Machine Learning researchers to the present and future consequences of their work, particularly in the areas of privacy, military robotics, employment, and liability.
When I went to Text Analytics World in San Francisco earlier this month, I was struck at how many of the presenters, particularly consultants, ended their talks describing future directions of text analytics as something that sounded so familiar. They described what would be possible once there's advanced maturity in ontologies, the breaking down of siloes, entity and relationship resolution by multiple methods, and automated linking of it all together into semantic network models of knowledge: flexible exploration of the relevant. They made it sound like a bit of a stretch, almost pie in the sky, but what they briefly described as this destination was curiously similar to what was shown concretely in the last presentation of the conference, my own.