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Mining Unstructured Data for Competitive Intelligence
In today’s global and fast-paced business world, it’s imperative for a business to not only keep track of its key performance indicators but also to monitor its competitor landscape closely. Organizations that keep track of their competition can better prepare for ongoing and upcoming challenges. However, for an organization to formulate an effective strategy early on, it needs to be alerted to any new developments as soon as the information is available.
Unstructured data sources such as news, social media, and public filings routinely report the actions and decisions made by companies, corporations, and businesses alike, as well as early signs of new developments in the market. When an organization monitors these unstructured information sources, it’s able to identify which competitors are announcing an acquisition, which may be merging, which are putting a new product on the shelves, what top executives are changing companies, and how the public is reacting to it all.
The Challenge of Unstructured Data
However, unstructured data presents its own challenges. The volumes of unstructured data are staggering and unstructured data itself can be very noisy and messy. Relevant unstructured data may involve many different sources and multiple languages. According to the International Data Corporation, only up to 1% of all data is ever analyzed. With so much unanalyzed data, it can be difficult for an organization to be alerted to new developments early on and formulate an effective plan of action. Here is where Entity Extraction software comes into play.
Entity Extraction for Competitive Intelligence
Entity Extraction software turns unstructured data into actionable insights by automatically identifying relevant information in text.
At the basic level, Entity Extraction software recognizes named entities such as names of organizations and people as well as monetary expressions and dates. Named entities are useful for locating relevant documents and to calculate spikes in buzz around competitors.
At the advanced level, Entity Extraction software identifies a vast array of links and events along with the entities involved. Links may include an organization’s subsidiaries, partners, and locations or a person’s employer, title, and associates. Events may include bids, mergers, acquisitions, new hires, management succession as well as adverse events like bankruptcies, product recalls, lawsuits, fines, plant closings, etc.
Link and event extraction is especially relevant and necessary for competitive intelligence. A typical application allows users to set alerts not just for any document mentioning a competitor, which may return too many hits, but for events of interest involving a competitor.
NetOwl’s Entity Extraction software uses sophisticated computational linguistics and natural language processing technologies in order to perform named entity as well as link and event extraction with state-of-the-art accuracy and scalability and in multiple languages. NetOwl Extractor offers a broad semantic ontology covering over 100 types of entities and over 150 links and events.
With a broad semantic ontology covering a large set of entities, links, and events, NetOwl Extractor is the software your organization can rely on for optimal unstructured data analysis. For more information on NetOwl Extractor, contact NetOwl today.