e-discovery 2.0

It’s not just in New Jersey that corruption is in the news. It feels like everywhere you go, the authorities are investigating white collar crime and thus have an increasing need for electronic discovery technology.

Earlier this month, as those of you who follow my Twitter feed will know, I was visiting customers and partners in Germany. In virtually every meeting, data privacy and corruption investigations were top of mind, and with good reason. Following the Siemens case last year, German investigators have become much more active and it was easy for my hosts to list example after example of recent cases. There was the Deutsche Bahn case of management spying on its own employees, in violation of German privacy laws; the Deutsche Bank case of management spying on its own board; and, the Deutsche Telecom case of management phone tapping employees to find leaks. There were stories of price collusion among cable car companies in the Alps, and corruption investigations into the activities of German companies in Eastern Europe.

A similar focus on anti-corruption exists closer to home. I have written before about the increase in FCPA investigations and that’s been reflected in recent headlines. As the Wall Street Journal reports, Sun and Shell have recently come under the microscope, according to their public filings. And Frederic Bourke, a founder of the accessories firm Dooney & Bourke, was recently found guilty of conspiracy to violate the Foreign Corrupt Practices Act, which may result in jail time.

All indications are that the U.S. Department of Justice and its counterparts overseas are just warming up. It’s not a good time for white collar crime, wherever you are in the world.

Maybe this will show my age, but I’ve been around the electronic discovery business since the days when pricing was both simple and very expensive. Terabytes were at the mythical high-end of the spectrum and gigabytes of “e-docs” (not “ESI”) cost $3,000 – $4,000 to process. Understandably (and fortunately for most), pricing models have evolved, thanks in part to more educated consumers and initiatives such as Sedona’s RFP + Vendor Panel.

Leaving the WABAC machine and moving into present times, we’ve starting to see some variance from traditional pricing models that primarily focus on data “into” the processing machine. More and more companies (such as Kroll Ontrack) are moving to models that price on data “out” of the process. Since that’s a bit nebulous, an example might illustrate:

Traditionally, in a somewhat simplified fashion, an electronic discovery project would be priced by the amount of data in the initial corpus (say 100 gigabytes) and processing would be priced at $500 a gigabyte (for round numbers purposes). Leaving out the sometimes significant caveat that the 100 gigabytes would likely increase due to expansion of compressed files, this would mean that the bulk of the project expenses would be $50,000 ($500 x 100), plus relatively nominal costs for monthly hosting and user access rights.

At the end of the day, after elimination of system files, deduplication and application of search terms (reducing the initial corpus by say 70% collectively) there would be 30 gigabytes remaining for hosting and possible production, both of which are most often priced separately.

Given rampant commoditization there’s an arms race underway among certain service providers where they’re now changing the above model to give away initial processing as a loss leader – pricing only on the data that comes out the end of the processing/search step. In this approach the above workflow would largely stay the same, but the vendor would charge a higher rate for what ultimately is hosted on the back-end. If this back-end fee was $2,000 per resulting gigabyte and the same 30 gigabytes was seen out the back end, then the customer would pay $60,000 for the project. But, if the deduplication, searching, culling, etc. was more effective (at say 80%) then the resulting 20 gigabytes would only cost $40,000.

The question then, as Clint Eastwood would put it, is: “Do you feel lucky?” This pricing model forces attorneys and litigation support managers to guesstimate what culling, search, and de-duplication rates they’ll likely get on the data corpus. Guess right and they save the end client money, guess wrong and they’re way over budget.

The dynamics of this purchasing decision are a bit atypical because the buyer (usually counsel) doesn’t pay the bills, so the decision can often be more vexing than most. When a direct consumer gambles on pricing things will ideally balance out over time, with money being saved in some instances and some being overspent in others. But, when the buyer doesn’t pay the bills the motivation is less clear.

Thoughts run to Maslow’s hierarchy of needs to determine which pricing model is ultimately more compelling: (a) price certainty/adherence to budget, or (b) cost variability and the opportunity to save money. While it’s never good to understate the upside of saving money (Esteem), I think ultimately there’s a more fundamental need (Safety) to stay within budget and avoid the painful (sometimes client imperiling) call to discuss how a given e-discovery project has gone way over budget.

This calculation is made further vexing because it not only pits the purchasing party against unknown data culling/searching rates, but it also puts the vendor in an ethical bind where they make less money if they’re supremely effective at data reduction, whereas if they’re either intentionally or accidentally beneficiaries of relatively little data reduction then they stand to make a ton of upside.

It’s like you went to Vegas to gamble your kid’s college fund and on top of the already questionable house odds you knew that the dealer stood to profit by your losses. So, as for myself, no, I don’t feel lucky.