NLP to deal with Unstructured Data in the Enterprise is the latest salvo to deal with the deluge of data inundating companies.

Chances are good that you are sitting on an enormous amount of valuable information, the scope of which your organization either doesn’t realize exists or doesn’t know how to analyze and use effectively.

Computer scientists refer to this kind of information as unstructured data. In the enterprise environment, approximately 80% of data is unstructured, according to Accenture.

For context, unstructured data comes from sources including email and text messages, social media updates, contracts, legal reports, and research documents along with voice mail messages and conference recordings.

Unstructured data is generated naturally and organically for the most part and can come in a variety of forms, from a fragmented speech in a conversation to paragraphs of details from an angry customer sounding off in a Facebook post about your products or services. Structured data, on the other hand, is much easier for automated systems to analyze and process, with a common example being spreadsheets.

The problem for enterprises is to leverage all of this unstructured data. What’s needed is Natural Language Processing and Natural Language Understanding, which is made possible through advances in artificial intelligence, machine learning, and computer vision.

Natural Language Processing (NLP) in Action

An enterprise’s main focus will be on leveraging all the unstructured data, using artificial intelligence approaches to process computer vision and facilitate document understanding.

“NLP breaks down words into their simplest form and identifies patterns, rules, and relationships between them,” said Walt Kristick, who serves as Senior Vice President of Applied and Advanced Technology at Apexanalytix, as cited by CIO. He added, “It uses computer algorithms to parse and interpret written and spoken natural language to allow systems to learn and understand human languages.”

Your workers have already been exposed to very simple versions of NLP, such as when a word processor provides spell checking or when an email client automatically suggests responses for them to send. They’re primed for accepting the use of NLP and NLU systems on a much wider scale. Working with more advanced natural language processing tools will help them grow even more efficient in their own work as well as when collaborating with other members of the team.

By scanning and processing the myriad of documents being produced, stored, and reviewed inside your enterprise, such as PowerPoint decks, PDF files, and email messages, you can discover patterns that lead to greater insight into your processes. This kind of information is more difficult to process as compared to traditional databases, but NLP and NLU systems bring the data into reach.

Consider what happens when one employee asks another for some information to solve a problem, and then receives a detailed voicemail response to that question. That answer might be the same advice many other employees require, but none of them know about it because the solution is locked inside of a voicemail that only the recipient would benefit from.

But with natural language processing applied to archived voicemails, a treasure trove of information becomes available for all workers to benefit from. Maybe the answer to a vexing challenge brought to your company by a major customer has already been answered, with the details left in a PowerPoint presentation from the previous year to a similar customer’s query. This means you don’t have to reinvent the wheel when a solution has already been formulated. But doing so depends on deploying natural language processing.

There is an unwritten corporate history of sorts to be synthesized about your organization that you can access, process and use to all workers’ benefit once you bring natural language understanding into your workflow. They’ll have a much bigger and nuanced knowledge base to refer to.

And it’s not as rigid as a standard set of structured information, such as what’s found in a database, so you have the opportunity to ask open questions instead of devising careful database queries.

Another important NLP task uses optical character recognition or OCR for capturing data from documents that are locked in print form, with no digital copies available. Then, intelligent document analysis and text interpretation from machine learning tools enable the enterprise to take action on this data.

For example, you use NLP to verify you are adhering to compliance requirements in your industry, or are managing risk according to the thresholds agreed to by fellow stakeholders.

“Applying these improved techniques to a variety of unstructured text data sources — such as emails, outbound marketing material, internal memos, chat transcripts, complaint logs and legal documents — is an effective way to enhance and automate regulatory review,” noted EY.

The result is improved internal operations thanks to the efficient use of previously unstructured data.

NLP to deal with Unstructured Data in the Enterprise

Organizations are already using Natural Language Processing and Natural Learning Understanding today.

One important area that’s ripe for NLP technology is in your human resources department. With an avalanche of applications, ordinary humans are inadequate to the task of filtering and evaluating each potential recruit.

It’s possible that when you place notices for job opportunities at your organization, you’re unable to effectively manage all of the incoming resumes and CVs. There is a limit to the number of resumes an HR staffer can handle in a day, after all. But you can allow for better filtering to identify promising candidates with natural language understanding applied to the documents coming in.

There are additional ways NLP and NLU can improve how organizations hire new recruits. At LinkedIn, the company now categorizes search results using natural language processing, to improve engagement with users based on search results.

“It has helped increase engagement metrics from search results, such as connecting to a person on the professional network or applying for a job—by 3% overall,” said LinkedIn’s principal staff engineer Ananth Sankar in Fortune. The click-through rate on query results in the online help center rose 11%.

Remember that more unstructured data is also being generated by users outside of your enterprise. For that, you’ll want to implement sentiment analysis. This has your artificial intelligence solution set to work on mining opinions to determine how various demographics think about topics, such as reviews of your products or services.

NLP will therefore boost your market research efforts and can have an impact on product development, with the added bonus of improving your customer retention rates.

Considerations for Implementing NLP

As you get ready to deploy Natural Language Processing in your organization, there are some considerations to go over with your team. Begin by examining your current processes, to see which kinds of tasks require manual work the most, which would be candidates for automation and machine learning.

With your IT department, verify whether your computational infrastructure has sufficient capacity now to handle all of the additional data throughput and processing involved in NLP and NLU systems. At this point, you will also be gauging whether you have the proper talent in-house to implement NLP or if you need to arrange for external resources to launch this effort.

What Will Your Organization Do With NLP and NLU?

Implementing tools for Natural Language Processing and Natural Language Understanding will be essential to your business going forward if you are serious about making sense of the vast amounts of unstructured data that you generate, process, and store on a daily basis.

It’s not just the information created in-house that you need to monitor and evaluate, though. There is plenty of data in the public space to process as well, from online reviews about your company and employees to incoming resumes and CVs that arrive in numbers too great for mere humans to process in any reasonable amount of time.

Suffice it to say that the more your organization grows, the more you’ll need to use NLP and NLU tools to get the most value out of the data.

Is your enterprise using NLP to deal with Unstructured Data?