Today’s workplace is filled with examples of Natural Language Processing (NLP), many of which go unnoticed. Applications like predictive text, autocorrect, email filtering, language translation, and text dictation have become a part of our everyday lives. But the real leverage of NLP comes when it is used to read, analyze, and understand the immense quantities of unstructured data created by day-to-day operations.

By more effectively and efficiently analyzing existing unstructured data, companies—regardless of industry type-–can save money and time while creating safer work environments and more satisfied employees. By automating workflows of unstructured data, NLP helps to streamline high-value business decisions, minimize operating costs, reduce human error, and gain visibility and insight into process issues. 

Most company data is unstructured

The principal challenge to achieving these goals is that only about 20% of available data on equipment performance, safety incidents, etc. is in structured format (e.g. spreadsheets or databases) that lends itself to straightforward analysis. The rest is in unstructured forms like handwritten text, incident/injury reports, technical manuals, emails, social media posts, technician notes, contracts, and countless other document types that require time-intensive manual analysis to extract meaningful insights. These documents can also exist in a wide variety of formats, e.g., Word, PDF, handwritten, or even audio. Due to the volume (which is estimated to rise at about 39% each year for the foreseeable future) and unstructured nature of this valuable data, it is largely invisible to analytical teams. Humans can understand this data, of course, but analysis driven entirely by humans doesn’t scale to large operations, opens up the risk of human errors, and is a waste of time and resources. Human beings may be phenomenal at understanding language and images, but they’re not equipped to handle data on the order of zettabytes.

SparkCognition Deep Nlp meets the challenge 

SparkCognition Deep NLP uses machine learning to create structure from Natural Language documents, enabling faster discovery of insights and more effective decision-making. By organizing these documents into logical groupings or collections, they can be more easily searched to find answers to specific questions. 

Deep NLP evaluates these document collections through a variety of lenses, e.g. topic, author, time, location, etc., to maximize the likelihood of locating actionable insights quickly. Also, Deep NLP understands industry-specific jargon and can leverage this understanding to find answers without the need for programming or additional training.  

The trick to all of this is interpreting information in relevant, context-specific ways that are intelligent enough to understand the meaning of the request while omitting redundant or ambiguous uses of terms not relevant to a particular query (e.g., producing instances of “foot” that relate to distance when the search is, in fact, for injuries involving the foot).  

In a recent Harvard Business Review article, the authors stated that NLP can be used for simple analytics tasks, such as classifying documents and analyzing the sentiment in blocks of text, as well as more advanced tasks, such as answering questions and summarizing reports. More advanced systems, like the much-written-about GPT-3, can even do programming and solve math problems. Due to their potential to transform the nature of cognitive work, economists expect that models of this type may eventually affect every part of the economy and could lead to increases in economic growth similar to the industrial revolution.

Real-world examples

SparkCognition’s Deep NLP solution has successfully addressed a wide range of real-world industrial challenges, including :

• Developing an advisory tablet application for front-line aircraft staff. This application allowed maintenance technicians to conduct machine-to-human dialogue for troubleshooting asset failures and mechanical issues with high accuracy, assessing faults using queries in natural language, and optimizing their workflow, delivering relevant documentation more quickly. This solution lowered the cost of maintenance and improved asset availability for operators by up to 10%.
• Enabling digitization of compliance processes for a major bank with 900,000 contracts under management and a daily global transaction volume of $3Tn. Each transaction requires access to a wide range of document types, many of which are unstructured. Using machine learning and NLP, SparkCognition extracts information and classifies financial documents in support of compliance processes, with a goal of increasing accuracy and reducing transactions.

Other far-reaching applications of NLP include:

• Improving safety by automatically analyzing incident/injury reports
• Increasing the efficiency of aircraft and other large asset maintenance processes by automatically gleaning insights from maintenance logs and other manual documents
• Enhancing customer experience by reviewing voice-of-the-customer communications like social media posts, emails, and customer feedback documents
• Identifying strategic business opportunities by ingesting analyst reports, partner contracts, news coverage, press releases, etc.
• Automatically evaluating patient records, clinical guidelines, and diagnostic procedures in healthcare companies 

Transform your operations with NLP

By analyzing vast amounts of unstructured data with the sophisticated NLP models developed by SparkCognition, our customers have realized tremendous improvement in operational efficiency and safety. Deep NLP discovers the meaning embedded within the data and makes it available to operators and managers, unlocking the insights contained within your data. Deep NLP brings structure and intelligence to documents, allowing users to extract valuable facts and figures that can drive improved profitability and operational safety. Organizations can tap the value previously hidden in large bodies of data to reduce operating cost and optimize safety and revenue generation. 

Powerful generalizable language-based AI tools are here today and are being used by industry leaders worldwide. But these are just the tip of the iceberg; multimodal foundation model-based tools are poised to transform business in ways that are still difficult to predict. To get prepared now, begin by understanding your existing text data assets and the variety of cognitive tasks and roles in your organization that could benefit from leveraging all of the insights contained in those assets.

To learn more about SparkCognition’s Natural Language Processing technology and how it can transform your operations, check out our NLP solution sheet or contact us to schedule a demo.  

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