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eDiscovery Specialist

Create analytics and visualizations for case team

Enhances✓ Available Now

What You Do Today

Build email communication maps, timelines, concept clusters — help case team understand millions of documents through visual analysis

AI That Applies

AI-generated analytics automatically cluster documents by topic, map communication networks, and identify key custodians and time periods

Technologies

How It Works

For create analytics and visualizations for case team, the system draws on the relevant operational data and applies the appropriate analytical models. The analytics engine aggregates data across sources, applies statistical analysis to identify significant patterns and outliers, and presents the results through visualizations that highlight what needs attention. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Case team gets immediate visual understanding of the data; AI-generated topic clusters and communication maps reveal patterns within hours of loading

What Stays

Interpreting analytics for the case team — translating data patterns into case strategy insights

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for create analytics and visualizations for case team, understand your current state.

Map your current process: Document how create analytics and visualizations for case team works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Interpreting analytics for the case team — translating data patterns into case strategy insights. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Relativity Analytics tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long create analytics and visualizations for case team takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your general counsel or managing partner

Which of our current reports are manually assembled, and how much time does that take each cycle?

They set the firm's AI adoption posture

your legal technology manager

What questions do stakeholders actually ask that our current reporting doesn't answer?

They manage the tools and can show you capabilities you don't know exist

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.