The problem with volume
Large data sets are expensive and slow to review if every item is treated equally. Much of the volume is duplicate, irrelevant or outside the relevant period. Processing exists to reduce that volume responsibly before review begins.
The goal is not simply to make the set smaller — it is to make it the right set: complete enough to meet disclosure obligations, focused enough to review proportionately.
Techniques that make volume manageable
Deduplication removes repeated copies of the same item. Date filtering limits the set to the relevant period. Keyword and custodian filtering narrow further. Document-family handling keeps related items together. Each step is recorded so the filtering can be explained and, if challenged, justified.
These techniques are applied transparently, so the parties can understand and agree how the set was reduced.
Keeping it defensible at scale
At high volume, discipline matters even more. Consistent processing, accurate metadata and clear records mean that decisions about what was included and excluded can be explained. That is what keeps a large review both efficient and defensible.
Key takeaways
- Most volume in a large set is duplicate, irrelevant or out of period.
- Deduplication, date, keyword and custodian filtering reduce it responsibly.
- Filtering decisions are recorded so they can be explained and justified.
- Discipline and consistent metadata keep large reviews defensible.
Frequently asked questions
How much can processing reduce a data set?
It varies by matter, but deduplication and filtering often reduce review volume substantially. The aim is the right set, not simply the smallest.
Is filtering defensible?
Yes, when it is transparent and recorded. The parties should be able to understand how the set was narrowed.
Can you handle very large volumes?
Yes. The approach is built around disciplined, repeatable processing designed for high-volume matters.
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