How to Solve the Unstructured Data Problem

unstructured data

Unstructured data shouldn’t exist, but it will always be there.

Gaining access to it creates the revenue and insight needed for rapid innovation and cost reductions.

And if there’s one thing RPA needs, it’s structured data!

Power through complex manual workflows with an easy-to-use tool that everyone will love.

Maximize the Potential of Unstructured Data

If you need to structure data from text-heavy business documents or B2B electronic data (onboarding / EDI), you don’t need a data scientist or an artificial intelligence expert. The path has been paved and widely traveled.

Is your organization spending resources and missing new opportunities because of inefficient or manual unstructured data processing?

Turn the tide and integrate valuable information from any document, any file, and from any department – regardless of where it is stored:

  • Text extraction from natural language documents, contracts, & leases
  • Accurate text extraction through patented OCR processes
  • Import electronic files and extract contextual data
  • Integrate / migrate data into content management systems
  • Rapid and accurate abstraction
  • Unstructured data analytics for big data
  • No code / low code architecture
  • Invoices, forms, contracts, EDI, logs, email, exports, etc.
  • Complex documents like Mill Test reports (MTR)

“Grooper enabled Change Healthcare to lift data from very complex client EOB/EOB print files and transform the data into payment and print files.”

Challenges of Unstructured or Semi-Structured Data

semi structured data

All unstructured data projects have the same basic goal of moving information into structured databases – or delivering to an RPA tool.

However, they also each have nuances that provide challenges to overcome (we feel your pain!).

Challenges of unstructured data projects include:

  • Little or no control over source
  • Multiple document types
  • Variable / changing document layouts
  • Information in tables / nested tables
  • Data stored in unclassified repositories
  • Unexpected formats (dates, phone numbers, etc.)
  • Unique industry / proprietary terms
  • Unidentified PII
  • Badly formatted EDI
  • Text in images
  • Human-generated / handprinted text
  • Tables / paragraphs span multiple pages

The 5 Steps of How to Process Unstructured Data:

1. Lean on the knowledge of your subject matter experts.

data knowledge experts

The first step in any unstructured data project is to fully understand the five Ws of your information and the internal structure of your operations.

Subject matter experts are your workers who use the information and they are critical to the success of any unstructured data project. They are integral to the solution.






  • Who originates and who uses the information?
  • What business outcomes / processes depend on this information?
  • When does the information become available for integration?
  • Why is the data in the format it’s in – can it be changed?
  • Where is the data going to be stored / accessed?

These are the types of questions that provide the framework for an unstructured data solution. You will learn the requirements for data ingestion / extraction, and all business workflows.

2. Collect a good set of representative data for classification.

unstructured data classification

Unstructured data solutions use optical character recognition, machine learning, and natural language processing to recognize the information that’s important to your workflows. While you will need representative training data, in most cases you don’t need thousands or even hundreds of examples.

Document-based unstructured data extraction doesn’t need neural nets. Representative data is mainly only needed for classification (recognizing what information is represented on the document). This step is incredibly important because software will be expected to look for and extract specific data elements.

Once the document type is understood, the software knows what data to look for and how to find it.

Learn about Grooper document classification.

3. Build data extraction models.


Data extraction models are frameworks that structure unstructured data. They use dozens of different data sciences and logic-based approaches to add structure to data. The good news with modern unstructured data tools is that they don’t need a data scientist or programmer to use them.

Data models are built with collaboration between your subject matter experts (SME) and an experienced Grooper architect. The architect knows how to identify and extract data. The SME knows how to identify what and where that information is on the document.

Unstructured data extraction models in Grooper are built using machine learning and logic-based approaches. Complex documents absolutely require multiple data extraction methods. Choose an unstructured data tool made to work on natural language documents, semi-structured, and unstructured documents.

Learn about Grooper machine learning and extraction.

4. Build data validation workflows.

structured data validation

Data validation workflows are critical for unstructured data integration. These workflows are both automated and human-in-the-loop processes that ensure data accuracy.

Data verification is based on acceptable error percentages and are use-case dependent. With Grooper, you can set a required accuracy threshold for every data element, perform mathematical verification, and / or build external lookups to ensure data accuracy.

In Grooper, all classification and extraction is automated. So users will likely only ever need to interact with the human review part of the solution.

5. Integrate unstructured data.

data integration

Because all data is now structured, integrating it is simply a matter of transforming the labeled data into the format of your choice.

There are many options in Grooper for integration that range from:

  • Proprietary formats
  • Delivering up the data as a CSV file
  • RPA bots that move critical data to downstream applications or throughout subsequent business processes

One of the newest methods of unstructured data integration is with smart PDFs. These are standard-format PDF documents but with all extracted data contained in bookmarks, annotations, or as metadata within the PDF itself.

These are self-integrating documents that are easily consumable by external customers.

Learn more about Grooper’s data integration.

Nobody Should Have to Experience a Failed Unstructured Data Project

How do you avoid failure? Do not begin any project with the technology!

By starting with the 5 W’s of information listed above, you will ensure a successful unstructured data integration or RPA deployment.

After fully defining the problem you are solving – and establishing the metrics to measure a successful outcome – here’s what to look for in the technology:

  • Text file or page-based ingestion
  • Image processing and computer vision
  • Built-in natural language processing
  • Optical character recognition
  • Machine learning
  • Fuzzy data handling
  • SQL / NOSQL database integrations
  • Custom / external lexicon support
  • Document classification and separation
  • Labeled character extraction
  • Metadata creation
  • Multi-format data integration

Like You, We Were Frustrated by Software That Didn’t Perform.

That’s why we created Grooper, an intelligent document processing platform that powers through even the most difficult unstructured data projects.

unstructured data analysis software

Whether you need to feed RPA tools, analytics engines, provide more rapid insight for business decisions / intelligence, eliminate manual workflows, or bring new innovations to market, we’ve been there before and have the experience you need to do amazing work.

Are you ready to create big unstructured data wins and secure your place in the market?

If you aren’t, someone else is…

unstructured data companies

Case Study: 70% Better Unstructured Data Integration

Oklahoma Healthcare Authority was disappointed with their data extraction system. They dealt with time-consuming separator sheets, and still had to hand key much of the information because their system was not built to process unstructured data.

Sound familiar?

They implemented Grooper and quickly saved considerable time and money with:

  • Extraction so efficient that manual entry clerks were re-assigned to higher-level tasks
  • Unstructured data integration for all critical document-based workflows
  • New innovations for customer benefits

Download our unstructured data case study to learn more:


  • “Grooper has saved OSU hundreds of thousands of dollars and the ROI was seen in less than six months after going live. This product has taken data processing, document scanning, and import automation to a whole new level. It’s now in virtually every department including our president’s office.”

    Erin Girton, Database Administrator/Content Management & Capture Administrator, Oklahoma State University
  • “All of the information in some file some place is very hard to retrieve. We ought to be able to get it with the click of a button, and now we can. Using the system that Grooper provides to scan the documents and mine the data eliminates many manual processes. My belief is that in the long-term it doesn’t really cost us anything because it’s going to pay us dividends over and over again. I would be surprised if anybody has a system that’s any better than this.”

    Gary Ridley, former Oklahoma Secretary of Transportation, former Director of Oklahoma Dept of Transportation and former Director of Oklahoma Transportation Authority
  • “Grooper allows my staff to process ten times more volume than we could with our previous image capture solution; our office thinks Grooper is worth its weight in gold.”

    Marie Ramsey-Hirst, Court Clerk, Canadian County
  • “Practically every unit here within our agency uses Grooper on some level. Grooper has cut down on our indexing time by 70 percent.”

    Ryan Freeman-Smith, Manager, Oklahoma Health Care Authority
  • “Grooper will give us the access to more contract data than ever before by quickly extracting the data across thousands of lengthy contracts, allowing employees to spend time on value-adding data analysis rather than extraction.”

    Glena Brauer, Supervisor – Marketing Contracts and Compliance, Chesapeake Energy

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