A modernized model should reflect connected workflows without obscuring distinct risks.
On September 1, EDRM published the final EDRM 2.0 model, its first substantive redesign of the familiar electronic discovery framework. The update reflects how dramatically data, technology, regulation, and discovery practice have changed since the original model became the industry’s shared visual language.
Several changes are notable. Information Governance now sits beneath the full discovery lifecycle as a foundation. Disposition is represented as a discrete phase. Analysis spans the model rather than appearing as a single stop in the process.
The change that deserves the most scrutiny, however, is the new Data Acquisition framework.
EDRM 2.0 groups Identification, Preservation, Collection, and Processing under that umbrella, reflecting EDRM’s position that these activities increasingly operate together rather than as strictly sequential steps.
Operationally, that is understandable. Modern matters are iterative. Teams identify sources, refine scope, preserve data, test collection methods, and prepare information for review in overlapping cycles. The old model’s neat left-to-right progression never captured every real-world matter perfectly.
But a model does more than describe workflow. It shapes how people understand priorities, assign resources, and evaluate risk. When four materially different functions are placed inside one larger category, the danger is not that practitioners will forget they exist. The danger is that organizations will treat them as interchangeable parts of a technical acquisition process.
They are not.
Identification is where downstream cost begins
Identification is foundational because decisions made at this stage carry disproportionate weight throughout the matter.
Which custodians are relevant? Which devices, applications, accounts, and data types may contain responsive information? What date ranges and participants are appropriate? Which sources are duplicative, inaccessible, ephemeral, or outside the lawful scope of collection?
Weak answers create compounding consequences. Over-identification drives over-preservation. Over-preservation creates pressure to over-collect. Over-collection increases processing volumes, hosting costs, review burdens, privacy exposure, and the amount of irrelevant personal or business information an organization must secure.
At ModeOne, we see this as more than a theoretical concern. Without rigorous diligence at the Identification stage, organizations risk over-preservation, over-collection, and a downstream explosion in litigation costs.
That is why Identification should remain visibly distinct. Its output is not acquired data. Its output is a defensible understanding of where potentially relevant data exists and what the matter actually requires.
Preservation and Collection share a boundary, but not a purpose
Preservation and Collection are closely connected, particularly when mobile and cloud data can change, disappear, or roll off quickly. Yet they solve different problems.
Preservation protects potentially relevant information from loss. Collection moves selected data into the discovery process. One asks what must be kept available. The other asks what must actually be taken.
That distinction matters because collecting everything is not the only way to preserve defensibly. With targeted remote mobile collection, for example, legal teams can collect defined applications, date ranges, and participants while leaving unrelated personal content on the device. Scope is enforced at collection instead of deferred until review.
This is where privacy, proportionality, and cost stop being abstract principles. The collection method determines how much data crosses the line into the organization’s custody and control. Once unnecessary data has been collected, faster processing or AI-assisted review may make it cheaper to examine, but neither can reverse the privacy exposure or eliminate the obligation to protect it.
Preservation and Collection therefore deserve clear treatment within any modern model, including explicit attention to data-loss risk, defensibility, scope, and the technical realities of each source.
Processing is preparation, not acquisition
Processing presents a different structural question.
Its purpose is generally to make collected information usable for review and analysis. That can include extraction, normalization, filtering, deduplication, and other preparation. Although processing follows collection operationally, its function is more closely tied to what reviewers and analytical systems need next than to deciding what data should be acquired.
Grouping Processing with Identification may suggest a conceptual unity that does not exist. One defines the universe of potentially relevant information. The other prepares acquired material so its contents can be evaluated.
A stronger structure would keep Identification distinct, give Preservation and Collection clearer subcategories focused on loss and defensibility, and align Processing or Preparation more closely with Review and Analysis.
The value of disagreement
EDRM 2.0 was developed through a significant collaborative effort, and the final model reflects the judgment of its project participants. Challenging one structural choice does not diminish the importance of that work.
In fact, the debate proves why EDRM still matters.
The model gives courts, corporate legal teams, law firms, service providers, and technologists a common way to explain discovery. Because that shared language influences real decisions, the industry should continue testing whether each visual relationship communicates the right operational priorities.
The central question is not whether Identification, Preservation, Collection, and Processing interact. They clearly do.
The question is whether placing them beneath one Data Acquisition label reflects those connections without flattening the distinctions that determine scope, privacy, defensibility, and cost.
Our answer is that the distinctions deserve more visibility.
What is yours?