When is a Data Migration Report typically generated in Relativity processing workflows?

Explore why the Data Migration Report is produced after a processing set is published, what it covers (moved data, metadata, issues), and why preliminary steps like filters or inventory don’t finalize migration records. A practical look at publishing workflows and data integrity checks.

Multiple Choice

After which action would you typically generate a Data Migration Report?

Why a Data Migration Report usually follows a published processing set

If you’ve spent time with Relativity Processing Specialist workflows, you know there’s a rhythm to moving data from one place to another. There’s a sense of momentum that builds as you transform raw information into something clean, searchable, and ready for insightful analysis. The moment the processing set is published marks a real turning point in that journey. It’s when the curtain goes up on what’s been prepared, and the Data Migration Report steps into the spotlight as the definitive record of what just happened.

Let’s unpack why this report shows up exactly at that moment, what it contains, and how it helps teams stay on top of the data they’re moving—and why it matters beyond the moment of publication.

What a processing set is really doing

Think of a processing set as a kitchen where the ingredients of your data are cleaned, normalized, and organized. You’re applying filters, tagging documents, extracting fields, and performing quality checks. The aim isn’t to alter the data into a different creature, but to shape it so you can find what you need, when you need it, and with confidence that you’re looking at the right stuff.

Before publishing, the work sits in a staging area. Filters might be applied to focus on specific custodians, date ranges, or file types. Inventory might be checked to confirm what was found and what remains. File size summaries can give a quick read on the scope of the project. Each of these steps matters, but they’re all part of the preparatory phase—the stage where you verify that the data looks reasonable and behaves as expected.

Publishing as the finale of the data hygiene ritual

Publishing a processing set is the formal moment when all those preparatory steps are finalized and handed over to downstream workflows. It’s the point at which the data becomes part of the formal dataset that others will rely on for investigations, reporting, and decisions. In that sense, publishing is like sealing a document, stamping it with a date, and saying, “This is what we’re claiming as the truth of this batch.”

That’s exactly why the Data Migration Report lands here. It’s not a running log of every little tweak made along the way. It’s a consolidated narrative of what has been moved, transformed, and prepared for use. It captures the essential outputs: what documents were included, how fields were mapped, what metadata was added or normalized, and what, if any, issues arose during the processing steps. It’s the milestone you reference when you say, “Here’s what we published, and here’s what it means for the next stage.”

What lives in a Data Migration Report

If you were to open a Data Migration Report, you’d expect to see a clear, structured summary that serves both technical and managerial audiences. Here are the kinds of elements that typically appear, and why they matter.

Scope and lineage: A concise description of what the processing set covered, including the date range, custodians, and file types. This helps stakeholders understand the boundaries of the migration and trace back to the original sources if questions come up later.
Transformations and rules applied: A high-level map of the transformations you performed—field extractions, date normalizations, de-duplication, and any tagging criteria. People who didn’t participate in the processing can still grasp why the data looks the way it does now.
Counts and volumes: Documents processed, total size, and any notable outliers. A quick glance at numbers gives confidence that the scope matches expectations and helps detect surprises early.
Quality indicators: Any checks that were run and their results. This isn’t about micromanaging every anomaly; it’s about signaling whether the data passed key reliability tests or if there are cautions to consider.
Issues and resolutions: If something didn’t go perfectly, the report notes what happened and how it was addressed, or what remains unresolved. This transparency is essential for risk awareness and for planning the next steps.
Metadata and attributes: A snapshot of the metadata that accompanies the data after processing. You’ll see what’s been added, changed, or standardized, which is crucial for later filtering and discovery.
Access and visibility notes: Who can see what, and where the data will live. This part is often as practical as it is political—permissions, locations, and any calendarized handoffs.
Validation checkpoints: Any downstream checks that confirm the data behaves as intended in the new environment. This might include checks against reference datasets or spot checks in downstream systems.

All of this together gives a durable, shareable record. It’s not just about the numbers; it’s about establishing trust in the migration process so the data can be relied upon for real-world tasks.

Why the report matters after the publish

You might wonder if the report is just a ceremonial document. In practice, it’s much more than that. Here’s why it tends to matter long after the processing set is published.

Accountability and traceability: In any data project, knowing who did what and when is invaluable. The Data Migration Report documents the decisions and steps that led to the published data, making it easier to reproduce or audit if needed.
Operational readiness: If downstream teams (analytics, reporting, case management) need to know what’s available and how it’s structured, the report provides that context. It’s a bridge between the data team and the people who will use the data.
Issue management: When problems surface later, the report helps you pinpoint where to look. Was an anomaly tied to a specific transformation? Was a field mapped differently than expected? The answers are often found in the documented details.
Compliance and governance: In regulated environments, having a clear record of processed data, transformations, and approvals is part of good governance. A well-crafted Data Migration Report supports that posture without turning governance into a bureaucratic maze.
Change management: Future changes to the data model or processing rules can benefit from the historical view. The report serves as a baseline against which updates can be measured.

A few practical tips to make the report truly useful

Keep it readable: Think “story with numbers.” Readers should understand what was done, why it matters, and what to watch for without needing a cryptic decoder ring.
Use plain language alongside technical details: You don’t have to oversimplify, but avoid drowning readers in jargon. Pair a high-level summary with the precise data points that matter.
Include visuals where helpful: A small chart or table showing counts by document type or status can convey a lot quickly. Don’t overdo it, but a visual cue can save a dozen lines of text.
Be explicit about next steps: If the data move will be followed by validation in a different system, or if users should expect a future update, say so. Clear forward guidance reduces confusion.
Standardize the template: A consistent Data Migration Report format saves time across projects. A familiar structure helps readers know where to look for the information they need.

A moment to reflect on the broader workflow

Publishing a processing set and generating a Data Migration Report isn’t a single, isolated act. It sits inside a broader cadence: prepare, verify, publish, report, review. The report is the capstone that makes the entire effort legible to others and protects the integrity of the data you’re moving.

As you go through the steps, you might notice overlaps with other areas you’re juggling. For instance, the act of filtering and inventory checks isn’t wasteful overhead; it’s the guardrails that keep the migration from veering off into chaos. And the file size summary? It’s a quick sanity check to ensure the scope aligns with what you intended. Each piece plays a role in the final published dataset and the accompanying narrative.

A few thought-provoking analogies to keep in mind

Publishing is like sending a shipment: The data is packaged, labeled, and handed off to the transport system. The Data Migration Report is the courier receipt that details what’s in the box, where it’s headed, and any notes the recipient might need.
It’s a recipe card for future cooks: The report lists ingredients (data sources), steps (transformations), and results (the final dataset). If someone else wants to recreate the dish, the card makes it possible without guesswork.
It’s the quiet checkpoint in a busy day: You’ve done a lot of work, and the publish moment is when you pause, look at the outcome, and document it. That pause is not a sign of doubt; it’s a professional discipline that pays off later.

A closing thought

In the end, the Data Migration Report isn’t a gatekeeping artifact or a ceremonial flourish. It’s a practical, thoughtful snapshot of what’s been accomplished when a processing set is published. It ensures clarity, accountability, and readiness for whatever comes next—whether that’s deeper analysis, operational deployment, or governance review.

If you’re navigating a Relativity workflow, remember this: publication marks a transition from preparation to perception. The Data Migration Report is what you’ll lean on to explain, defend, and build upon the work that’s just been set in motion. And as you grow more fluent with the language of processing and migration, you’ll find this report not just useful but essential—a reliable map in the evolving landscape of data handling.