Search as a Transformational Enabler

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Search as a Transformational Enabler

For years search has had a bad rap. Sometimes valid, sometimes due to mismanagement of content, and sometimes because of end user difficulties. All in all, it’s a combination of external factors that has made enterprise search, well, fail. I’m not talking bells and whistles, I’m talking about the ability to balance precision and recall, to retrieve only the relevant information associated with the query, the first time, and all the time.

There is obviously a strong business case for enterprise search. But search is intertwined with analytics, text mining, data discovery and classification, protection of privacy and sensitive information, and more. Without search, we are just lost at sea. So improving search outcomes, regardless of application, should be an organizational priority. Typically, it’s not. Search becomes a business enabler and transformative when insight and intelligence can be found, extracted, understood, and applied based on content assets.

Although analysts seem to think the only way to achieve this is through machine learning (ML), natural language processing (NLP), or artificial intelligence (AI), I would argue that you don’t have time. These are not easy undertakings. AI experts point to 2047 as the time when AI will be a viable technology. But Concept Searching can address these challenges today.

Since our technology generates multi-term metadata that has meaning – think concepts, topics, subjects, and entities – and then auto-classifies content against one or more taxonomies, the search engine index can use the multi-term metadata to enable concept-based searching, dramatically improving search results. Since the technology understands concepts, inter-related or intra-related content will also be identified, even if it does not contain the original search string. We also offer a high-performance search solution, conceptSearch, often used by government for intelligence and cybersecurity.

Using unstructured or semi-structured data in analytics is known to have been a significant problem. Most vendors use a structured database approach, which is a poor substitute for deriving meaning and nuances in unstructured content as it simply moves data into a field. But it no longer has to be an issue. Using Concept Searching technology, a finely honed data set can be created and then used by any business or artificial intelligence application, such as Power BI, Birst, Click, Azure Machine Learning, or Cortana Analytics Suite. I mention these specifically as one of our clients has them all installed.

A client used our technology to evaluate investment in oil fields, with great success – read the case study here. It also benefited from using the technology extensively following a tragic oil spill, to match invoices with contracts and sub-contracts, which was a massive undertaking.

Our webinars also address the topics explored in our blogs. Access all our webinar recordings and presentation slides at any time, from our website, in the Recorded Webinars area, via the Resources tab.

Concept Searching