How DataScava’s Intelligent Approach Adds Value to Unstructured Data

Check out this article written by our CTO John Harney about how DataScava mines unstructured textual data using our Non-Semantic Domain-Specific Parser and patented “Weighted Topic Scoring”:

“Real-time mining of unstructured textual content isn’t simple. Available solutions don’t work well unless they’re fine-tuned to meet your specific needs and address the unique quirks in your company’s information. To truly add value, your applications, whether AI-driven or not, require a vocabulary that captures the definitions, context and nuance of your business.

“DataScava’s unstructured data miner provides fast and effective solutions for leveraging the explosive growth of unstructured data. The sheer volume of information—even some small businesses must manage thousands of pages each day—challenges companies to find the most efficient and accurate way to read data, index it, extract what they need and quickly route it to its proper destination.

“Conventional wisdom says that taking full advantage of information requires a “data-driven system” based on “artificial intelligence, “machine learning” or some other application. But without modification, these technologies often process information quickly, but incorrectly. Needless to say, machines that use inaccurate data for machine learning, AI insights, business decisions, or that pass data to the wrong destination, don’t do anyone much good.

“That’s where DataScava comes in.” Read the full article here.


Let’s Admit It: We’re a Long Way from Using Real ‘Intelligence’ in AI

With the growth of AI systems and unstructured data, there is a need for an independent means of data curation, evaluation and measurement of output that does not depend on the natural language constructs of AI and creates a comparative method of how the data is processed. Here’s an excerpt from a blog post on this subject written by our CTO John Harney published by big data site KDNuggets:

“For anyone worrying about machines taking over the world, I have reassuring news: The idea of artificial intelligence has been overcome by hype. I don’t mean to belittle AI’s promise or even its existing capabilities. The technology allows organizations to put data to use in ways we could only imagine not that long ago.

“It’s revolutionized the way executives approach strategic planning. But very often lately—when I’m in meetings, reading research papers or listening to an expert’s presentation—I can’t shake the feeling that to many people, terms like “AI,” “machine learning” and “cognitive computing” have become answers unto themselves.

“Today, solutions providers put statements like “AI-driven” or “harnessing the power of machine learning” at the core of their sales pitch. The buzzwords are certainly getting through. One colleague tells the story of a client calling “to make sure AI was included” in their data analysis project. Business people have been sold on the notion that today’s cutting-edge systems analyze data in a black box, then spit out reliable insights. How? They just do.”

Read the full article here:’s Admit It: We’re a Long Way from Using Real ‘Intelligence’ in AI


Busting A Buzzword: Semantic Search

DataScava’s Non-Semantic Search was featured in a piece by the renowned RecruitingTools news blog written by Katrina Kibben.

“What we really need, and I only know one company that does this (shout out to TalentBrowser, powered by DataScava, and founders Janet Dwyer and John Harney) is a completely customizable white box ‘profile’ search built on input and personalized rules that you the user control, not a black box semantic search engine that thinks it knows what you ‘really mean.’ Profile search allows you to specify many individual topics in a search, with thresholds (minimums) to be met by each topic. This twofold process bubbles the best candidates right to the top.”

Here’s the full report