What is NLP and how can it support business?
Imagine a curious executive walking down a path that explores how Natural Language Processing (NLP) can help their business by uncovering insights hidden in the available and pertinent data to make better and more timely decisions. The executive is often faced with the problem of having vast amounts of company data, and not a lot of ways to take action on it. Not to mention the risk of not knowing what’s hiding in the data.
Steps one and two
As the journey begins, the executive takes steps one and two, Ideate and Identify. The executive asks “What do I want to know?” and follows up with “Where do I find the answers?” Whether it is customer attitudes toward a business, how it compares with competitors, or almost anything else decision-makers, analysts and owners would want to know, a quest for knowledge is the beginning. The next consideration is where to find answers to these questions. Identifying those data sources — internal purchase information, call center logs, product descriptions and reviews, social media posts, customer survey results, etc. — is the “where” that the answers will be found.
Steps three and four
The next steps, three and four, are Connect & Ingest and Transform, where the executive might ask, “How do I find the answers?” extracting text from both external sources and the company’s unstructured internal data mentioned in step two (Identify). In Transform, the executive asks, “How do I use NLP and AI?” focusing on Named Entity Recognition (NER), a sub-task of information extraction. It identifies and classifies named entities mentioned in unstructured data into predefined categories such as given and surnames, affiliated organizations, geographic locations, time expressions, etc. It also includes question & answer, classification, topic modeling, relationship extraction, sentiment analysis and other methods of processing the ingested information into something useful.
Steps five and six
Next up, Integrate and Explore, steps five and six, come after data has been scanned and processed. At the Integrate step, the executive could ask, “How do I combine insights from NLP and AI with my own data and analytical models?” To sharpen the results of NLP, companies often have pre-existing internal mathematical models, analytics and projections that can be combined. Once completed, the executive at the Explore step asks, “What answers do I have?” and looks at the patterns and unearthed relationships that can be converted into action plans.
Steps seven and eight
Operationalize and Realize & Repeat are steps seven and eight. Once the executive has answers from previous steps, the question is “How can I use this information?” The Operationalize step adds these new insights into a workflow. This can include replacing labor-intensive and often mundane tasks like manually compiling mass volumes of data with automation, contextual routing, summarized analysis, and creating intelligence dashboards.
The last step is what keeps the process going, which seems counter-intuitive, but the executive learns it is a feature of NLP. Once new insights are put in place to realize achievable outcomes, this new data is used to expand on and repeat for further analysis that will result in a deeper understanding.
While these concepts require a basic understanding of NLP, the eight steps succinctly sum up the process. The executive has developed a better understanding of how NLP can positively impact their bottom line.
Primer strives to help the world understand the power of NLP and what it can do to help businesses make better decisions and gain a competitive advantage. The “8 Steps to Get Started with NLP” is one of myriad efforts to pique interest, start conversations, and educate the business community.
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