Time again to dust off our crystal ball and give you our guaranteed predictions of what you will see happening in e-discovery in 2017:
1. The Cloud
We have already started seeing a move away from on-premise e-discovery installations. With kCura’s recent move to hosting on Microsoft Azure, combined with Microsoft’s rollout of Canadian data centres earlier this year, we expect to see more law firms ditching their current in-house installations and moving to cloud based e-discovery software.
2. More Options for Canada
We predict a greater acceptance that cloud based software is just as secure, if not more secure, than in-house installations. We have been told by several south-of-the-border e-discovery software vendors that they have plans to launch Canadian data centre hosted installations in the coming year. We can’t divulge who they are (that would spoil the surprise), but look for more options next year.
3. More Discovery Plans
The OBA Civil Litigation group has prepared a paper questioning the value of the current rules relating to discovery plans. We see an increasing need for effective discovery planning. E-discovery is not going away as data volumes increase. Different forms of communication are developed all the time. As awareness of discovery plans and their usefulness for even small cases increases, more and more lawyers will start to use them, and start discussing scope and exchange of documents sooner, exactly the purpose of the discovery plan.
4. Better Search
Although keywords will remain the dominant form for searching for relevant information, we expect to see better workflow surrounding keyword searches, and more use of other search technologies, such as conceptual categorization, assisted review, and machine learning. We are already past the adoption curve, and 2017 will be the year TAR becomes mainstream in Canada.
5. Better Organization
Information Governance has become a catch phrase. Most boards now discuss how they can better govern their information. While the growth has mainly been due to an increased awareness in cybersecurity, e-discovery will benefit from better organization of the information and less ROT to sift through to find what we need.
After a couple of years of lackluster innovation and somewhat slow growth, we expect 2017 to be a watershed year for e-discovery in Canada.
 We guarantee that our predictions might or might not come true
Commonwealth countries continue to approve technology-assisted review (TAR) for e-discovery. In mid-2016 we started to see cases coming from the UK courts endorsing the use of TAR; recently, Australia followed suit.
In one case, the Australian Federal Court ordered one party to disclose several specific aspects of its TAR workflow to the opposing party. Meanwhile, the Supreme Court of Victoria went so far as to endorse the use of TAR to review 1.4 million records, and appointed a special referee to manage the process (see McConnell Dowell Constructors v. Santam Ltd.).
We already know that the use of TAR and predictive coding is prevalent in the United States (how else could the FBI have reviewed 650,000 emails in only 9 days?). In fact, US judges have not only given TAR their seal of approval, but have actually directed parties to use it. In Canada, however, our courts have not been so bold. We know that parties to litigation are slowly starting to accept and use TAR, but to date we have not seen any orders from Canadian courts requiring it, or specifically endorsing its use.
With so many matters being case managed, this is the perfect opportunity for Canadian courts to weigh in. Let’s hope 2017 is the year that TAR becomes trendy in Canada too.
We all know the outcome. Donald Trump will be the next US president, despite almost all polls predicting he had practically no chance of attaining that post. Does this mean that statistics don’t work? Hardly.
Almost all the polls indicated that Hillary Clinton had between a 70% and 98% chance of winning the election. Only one poll, The Los Angeles Times Daybreak poll, said that Donald Trump was more likely to win. In the so-called swing states, these same polls were way off.
The polls were based on sampling a small percentage of the population, and then using statistical probability analysis to predict how the whole population would vote. This is a similar method used by TAR (Technology Assisted Review) – the fancy acronym that encompasses predictive coding and other cost-saving e-discovery techniques. So, what do the election results say about the validity of using TAR?
Based on what we have gathered so far, it appears the polls failed because the pollsters did not use representative samples. Statistical probability assumes that the sample will be made up of an even cross-section of the whole population. When dealing with voting patterns, this means the sample needs to include representatives from all different social groups: males, females, young, old, ethnic minorities, gay, straight, etc. More importantly, the pollsters needed to consider whether the people polled would actually vote, since those who would not vote should not have been included.
The unrepresentative nature of the samples is where the pollsters failed. There were many reasons for this. One was the way the samples were taken. Many of the polls were conducted online. Older people generally don’t use computers, or at least are less likely to answer polls online. The polls were also conducted primarily in populated areas. The results show that the percentage of people in urban areas who actually voted was lower than the percentage of people in rural areas. This means that, for the samples to be representative, more rural people and fewer urban people should have been included.
When sampling discovery data, the same representative sample requirement holds true. Statistics tell us that, if you have a large enough collection and the different records are somewhat evenly distributed throughout, a random selection of a few thousand will likely give you a representative sample. Unlike the election, in e-discovery we have a way to make sure our sample is representative – validation. After we run our sample and separate our records into relevant and not relevant, we can then go back and sample the not relevant set to see if we missed anything. If our initial sample was not representative, our second validation sample will very likely show up relevant records.
The theory behind statistics and probability have been proven to be valid. When used correctly, they will return defensible results. Even though the election results may have surprised you, there is no reason to worry about the value of TAR.
Vote for our Blog - Click Below