QDiscovery is a premier provider of electronic data and document management solutions for law firms and corporate legal departments throughout the United States. Our leadership team and staff of life long electronic discovery specialists are ready and willing to become valued members of your case team. QDiscovery will treat your project as if it was our own.
In my article “Term Mutations: ECA and EDA Get Murky” published at LawTechnologyNews.com, I advocated for reclaiming the definition of early case assessment (ECA) as a process for developing an overall litigation strategy. ECA best understood incorporates early data assessment (EDA), but is not synonymous with it. Space considerations ruled out a detailed primer on ECA and EDA in the article. This post will cover the how-to of early data assessment and a future post will look at early case assessment.
New London, Conn. – March 25, 2015 – QDiscovery LLC, a premier provider of electronic discovery and forensic technology services, today announced that it has earned first place in the Hartford Business Journal’s Best Places to Work in Connecticut Awards. QDiscovery was recognized in the division of small businesses, placing first out of almost 20 companies.
QDiscovery had been named to the list earlier in the year, but only discovered its actual ranking – first place – at the awards gala held by the Hartford Business Journal on March 11. The full article and rankings published by the Hartford Business Journal can be viewed here.
By Helen Geib, Law Technology News
It’s a common misperception that predictive coding is only for big cases and big law firms. In reality, predictive coding has the potential to be just as valuable to small and mid-sized firms handling a wide range of matters.
The reason is simple: It’s not just big companies that are drowning in data, it’s every company.
Predictive coding has three potential objectives that reduce both the cost and staffing of attorney review, which is typically the most costly stage of e-Discovery. Pursued singly or in combination, these objectives can cut big data down to a manageable size.