Want to reduce the cost of eDiscovery? Re-think the approach
Have you ever bought something where the price was great, but in the long run it ended up costing a lot more? Last year, I was planning for a trip and found a great deal for my flight. The ticket fit my schedule perfectly and the price was $200 less than everything else, so I wasn’t overly concerned that I hadn’t flown this airline before.
After making the trip, I received an education on the difference between price and cost. My education started when I got to the airport and realized this was a no-frills airline, over the next 30 minutes I paid $10 to print my ticket, $35 to select my seat, $50 to check my bag, and $55 for my carry-on. Thankfully, they didn’t have cash operated restrooms on the flight, but I guess at $3 for a can of Coke they could afford to throw something in for the cost of a ticket.
The moral of the story is that cost is an unintended consequence of not fully understanding what you’re buying and once you’ve paid for it, your options are limited. We see companies fall into the pricing trap when it comes to eDiscovery all the time. There is a price for each step and if you don’t know how to maximize the spend at one step, the cost of the higher priced next step increases.
eDiscovery Pricing Opportunity
Success in eDiscovery tends to be defined by how effectively an organization can negotiate pricing and measured by how the final costs compare to the projected budget. This approach isn’t much different than my flight experience, I got a great price for my flight and after racking up an additional $325 in costs I realized I should have considered a different path.
The advantage of the eDiscovery pricing model is that price is driven by data volume and the impact of early decisions around data volume drive the downstream cost. The opportunity this presents is in the decision points that are available throughout the process that provides cost predictability and guides the appropriate next steps. An organization’s ability to get their legal counsel to an understanding of the case facts while the data volume is the largest is the most effective way to eliminate downstream cost.
While it may seem crazy to leverage the highest price per hour legal teams on the largest volume of data, consider that you are hiring counsel for their legal expertise and their ability to interpret the strength or purpose of your case. Involving counsel at this stage isn’t about a formal review of the documents, it’s about researching the purpose of the case and the supporting information required to present the right decisions.
The Race to Review
Often times the process is driven with the mindset that there is no time to do the early research into the data, there are deadlines looming, millions of documents to review and several levels of document decisions to make. The objective is to identify relevant documents to produce and privilege documents to withhold within the required timeframe, from there counsel can begin building out the case strategy.
The race to review approach typically starts with collected data being sent to a legal service provider and starts with an ingestion cost of $50 per gigabyte. The $50 per gigabyte gets the data loaded to an eDiscovery platform and volume is reduced 80% by removing certain file types, identifying duplicates, and applying keyword searches. Decisions are driven by the volume of data that will move to the next stage and whether this volume falls within budget. Once confirmed, the remaining 20% will be prepared for document review at $150 per gigabyte and then $20 per gigabyte to host each month. Next relevance review will take place at around $1 per document and relevant documents are routed to legal counsel for confirmation and determining post-production strategy.
|Data Filtering||100 Gigabytes||$500|
|Data Prep for Document Review||20 Gigabytes||$3,000|
|Document Review Hosting||20 Gigabytes||$400/Month|
|Document Review Services||200,000 Documents||$200,000|
Total Cost to Counsel
A Smarter Path to Discovery
In the race to review, we eliminated 80% of the data moving into review for a pretty low price and at face value, this is a successful step. However, if only 10% of the review population is produced our cost to filter non-relevant documents in doc review were $180,000. Taking this example a step further, let’s say that Counsel identifies a series of emails in the production that change the direction of the case and instead of moving towards trial the case settles. Getting to this decision late in the process ends up costing considerably more than necessary.
Consider an approach that provides you with the ability to get the maximum value from each stage and then focus on how you can discover more and review less. Here is an example where the price is higher, but the approach is designed to impact your downstream cost. Let’s say instead of $50 per gigabyte to load and $150 to load to doc review, this provider charges a flat $150 per gigabyte. For the $150 per gigabyte you are provided a visual analytics technology paired with a data mining consultant that can work as an extension of your legal team to target relevant data.
|Data Filtering||100 Gigabytes||$15,000|
|Document Review Hosting||5 Gigabytes||$100/Month|
|Document Review Services||50,000 Documents||$50,000|
Total Cost to Counsel
As you can see, the price difference has driven the cost of data filtering up significantly when compared to the traditional model, but this eliminated 15% more documents. By improving the analysis of the data earlier in the process, we not only improved our time to decision, we were able to save $140,500 on a single matter.
We’ve seen this approach work effectively time and time again. Yet, we still continue to see organizations move forward with the first model we laid out above. Why is that? I’d love to hear your thoughts.
Discover More. Review Less.™