According to the film’s premise, Jessie worries that she’s “losing Bonnie to this device” as traditional toys compete with tablets and digital entertainment for attention. Legal tech is having a similar moment.
AI is getting the spotlight, and deservedly so. Tools like aiR for Review, aiR for Case Strategy, and aiR for Privilege are exciting additions to the eDiscovery toy box. But that does not mean Relativity’s classic analytics tools should be forgotten.
Structured Analytics and Conceptual Analytics still do incredibly practical work. They help teams organize messy document sets, reduce duplicative review, find related materials, and uncover information that keyword searches may miss.
At Page One, our project managers average 10+ years of experience, which means we know how to use both the shiny new tools and the reliable classics.
Five Structured Analytics Toys
Structured Analytics is especially useful when a team needs to make a large, unfamiliar document set more reviewable.
- Email Threading groups related email conversations together so reviewers can focus on the most complete or inclusive messages instead of reading every reply, forward, and duplicate fragment. This can dramatically reduce redundant review and help teams understand communications in context.
- Textual Near Duplicate Identification finds documents that are almost the same but not identical. That is helpful when teams need to compare versions of agreements, reports, presentations, or repeated communications and quickly spot what changed.
- Repeated Content Identification identifies recurring text, such as disclaimers, footers, confidentiality notices, and other boilerplate. By isolating repeated content, teams can focus on the substance of a document instead of noise that appears across the collection.
- Language Identification helps route documents by language, making it easier to plan translation needs, assign reviewers, or separate materials for specialized workflows.
- Name Normalization standardizes variations in sender and recipient names, so “Robert Smith,” “Bob Smith,” and “rsmith@company.com” can be analyzed more consistently.
Think of Structured Analytics as the toy bin organizer. It does not care who started the mess. It just knows someone needs to separate the Buzz Lightyears from the loose crayons and the mysterious sticky thing.
Five Conceptual Analytics Toys
If Structured Analytics helps organize what is visibly similar, Conceptual Analytics helps find what is meaningfully related.
- Clustering groups conceptually similar documents together, giving teams an early map of what topics exist in the data. This is especially useful at the beginning of a matter when no one fully knows what is in the collection yet.
- Concept Searching allows teams to search by ideas, not just exact words. For example, documents discussing pricing pressure, customer complaints, or internal risk may not use those exact phrases. Concept Searching can help surface documents that discuss the same subject in different language.
- Find Similar Documents is one of the most useful tools once a key document has been identified. If a reviewer finds something important, they can use it as a starting point to locate other documents with similar conceptual content.
- Keyword Expansion helps strengthen search strategy by suggesting related terms that may not have been obvious at the outset.
- Categorization uses example documents to help identify other documents that fit similar concepts or issues. This can support prioritization, issue tagging, or targeted review workflows.
These tools are powerful because they help legal teams focus on meaning rather than terminology. Unfortunately, not every important conversation announces itself with a giant neon sign. Sometimes it’s buried inside “Project Pizza Planet,” “Operation Infinity,” or some other code name that sounded a lot more clever before Conceptual Analytics showed up.
I Knew It, You Knew You: AI and Analytics Are Better Together
This is not an AI-versus-analytics conversation.
The best workflows don’t choose between AI and Analytics. They use both.
AI can help summarize, classify, and analyze documents in powerful ways. Analytics can help organize the population, reduce redundant review, identify patterns, and surface related materials before or during that process.
Used together, they can make review smarter, faster, and more defensible.
And that is where experienced project management matters. Knowing which tool to use, when to use it, and how to build it into a workflow is not automatic. It comes from years of working with real data, real deadlines, and real review teams.
To Infinity, Beyond, and Back to Basics
The legal tech world should be excited about AI. We certainly are.
But the classic analytics tools still deserve a spot on the shelf.
Structured Analytics and Conceptual Analytics remain some of the most practical ways to reduce review burden, uncover related documents, and make large datasets easier to understand.
So yes, bring on the new technology.
Just don’t forget Woody, Buzz, Jessie, and the rest of the analytics crew.
They still have a job to do. And unlike Bonnie’s tablet, they are not here to steal screen time — just reduce review time.