You Can Save Time and Money by Streamlining Complex Data Research

While the job of a data research professional has always been challenging, now the work may be more difficult than ever.

To truly leverage their large archival collections of data that sometimes span 75-100 years, researchers must take on the daunting task of quickly and accurately digitizing and categorizing those artifacts. Moreover, researchers still need to add newer digital information assets to archives. However, effective use of these new resources can be impeded by inadequate or missing metadata, and unstructured content that cannot be effectively searched or analyzed.

And the data keeps coming. That’s why organizations with research and analysis missions are constantly seeking new solutions to swiftly find new insights from all forms of data artifacts.

Is there a solution?

Some organizations manually tag their records to provide complete, accurate results when searched. The problem there is that manual tagging is such a slow, laborious process that it could actually take hundreds of years to tag a typical amount of records within an organization. The classified library at Los Alamos National Laboratory, known as the National Security Research Center, wrote about this situation in their article “The archives of the future”.

The article’s author sums up the issue by stating: “The Center’s microfiche and microfilm number in the hundreds of thousands and contain well over 50 million pages of information. Using our current, non-AI/ML-capable equipment, software, and processes, it would take us an estimated 90-some years to digitize the microfiche collections, and more than 2,000 years to digitize our microfilm collection.”

Then there are those organizations that don’t bother with manual tagging, but instead rely upon basic automated metadata generation to tag their records and hope that technology yields complete, accurate search results for their complex queries. Unfortunately they might not even realize what search results they are missing due to the inherent inadequacies of that approach.

So are there more alternatives to solving complex search queries? Yes. Newly evolved search technology now generates more accurate, complete, useful results, and can be up and running quite quickly.

The revenue-generating power of AI/ML search technology

By using an artificial intelligence / machine learning (AI/ML) solution to generate metacognitive records, your organization can find critical value in your data faster and more precisely than if you were to rely on manual tagging or basic automated metadata generation.

And there is no question that making better use of your data trove can potentially add millions of dollars to your bottom line. There is money to be made by using the newly discovered data to make business decisions to increase revenue or further reduce overhead. Plus there are salary hours saved when your team is freed from manually tagging data records. Then your staff could use that time on other projects.

Here are some details about the power of AI/ML search technology. By applying an AI/ML-based, natural language processing / natural language understanding (NLP/NLU) technology to analyze and make sense of content, metacognitive records are generated. This NLP/NLU technology can perform additional processing too, such as building associations between data points, images, PDFs, recordings, videos, charts, tables, handwriting, and many other forms of records.

This process enables the researcher to:

• Skim results faster – Researchers can quickly go from perhaps hundreds of very broad results to very specific nuanced results. The inclusion of automated ontology and taxonomy as interconnected metadata descriptors also helps the researcher rapidly filter through a document series to locate specific knowledge areas within a singular page.
• Reduce wrong turns – By resolving language ambiguity, content is placed into the proper context. For example, results on a search for “Harry Truman” will yield individual searches for Truman (the President) and the USS Harry S. Truman (the aircraft carrier). NLP/NLU technology classifies the term as both a main phrase and as a military facility. A user can filter their search on one or more of the tags.
• Overcome inadequate name searches – For instance, a search for Tom Hanks would also catch records where the name is listed as “Hanks, Tom”. NLP/NLU analyzes text and identifies concepts and entities, including people, so it can associate the different forms or styles associated with a person’s name in a text, including name variations and the anaphora and cataphors (“he” and “him”) associated with a name. As a result, all the references to a name with the corresponding text offsets are in the metadata output, and can be leveraged by search engines to provide a powerful content-level search capability for users.
• Catch multiple names for the same item – NLP/NLU leverages sophisticated knowledge graphs and tailored linguistic concepts within a Studio development environment. The researcher can then search upon an official name or any of the variations. For example, the “Affordable Care Act” or “Obamacare” would both come up in a search.

To sum up the effect of using AI/ML to implement NLP/NLU technology, the author of the Los Alamos National Laboratory article states, “AI/ML is new, yet proven. The Laboratory needs to embrace this advancement, which is really the only solution to making its one-of-a-kind collections searchable to its researchers. Investing in AI/ML saves countless hours and many millions of dollars, while directly contributing to the Lab’s mission success and our nation’s security.”

Start enjoying the benefits of better research

If you would like to learn more, ask us how our Compendia Data Platform can be your gateway to all of the revenue-generating results described above. Compendia offers a premier Extract, Transform, & Load (ETL) tool with the ability to ingest hundreds of types of structured and unstructured binary documents, forms, and images. This market-leading capability goes beyond traditional vendors in the type of content it can extract, enrich, and describe.

Compendia’s NLP/NLU engine massively scales without loss of precision, assuring consistency across deployments, while Compendia’s NLP/NLU development environment offers you the ability to organically develop and adjust domain specific taxonomies or entity extractions.

This combination of technologies effectively minimizes the need for human review, enabling your employees to focus on their true jobs while Compendia’s ETL algorithms tackle the time-consuming tasks associated with metadata generation.

The team behind Compendia has a broad pedigree of success leveraging unique AI/ML technologies in the domains of metadata generation, data enrichment, precision search and recall, linguistics, business and consumer consulting, solution design, and customer experience.

Compendia is flexible, extensible, and scalable to support a wide range of customer data curation and discovery challenges, including a diverse array of industry and government solutions.

Contact us at info@titantechnologies.com today to see how Compendia can be customized for your organization.