Does your organization utilize Office 365 for email? Is your organization required to journal email for compliance, legal, or business requirements? Do your Attorneys complain about the time it takes to find information for an eDiscovery request? If the answer is yes to any of these questions, then keep reading. Continue reading
Dammit Jim, I’m a Doctor, not an AI – Healthcare and Machine Learning
Healthcare costs continue to skyrocket. In 2016, healthcare costs in the US were estimated to be almost 18 percent of GDP. The healthcare industry is seeing an unprecedented and accelerating growth of ESI. This avalanche of data is being generated from the digitization of healthcare information, EHR systems, precision and personalized medicine, health information exchanges, new imaging technologies (DICOM), new regulations, IoMT (Internet of Medical Things), and other major technology developments. Continue reading
The Privacy Shield Scheme and the GDPR
The EU/US, Safe Harbor scheme, was struck down by the Court of Justice of the European Union (CJECU) in October of 2015 putting companies on both sides of the Atlantic in a difficult position – not having a process for legally transferring data out of the EU to the US. Continue reading
My Healthcare Data is Where?
According to IDC, healthcare data is one of the fastest growing segments of the digital universe – growing from 153 exabytes in 2013 to an estimated 2,314 exabytes in 2020, a 48% annual growth rate. So where will the healthcare industry put all of this critical and sensitive data and how long must it be held?
“Move to Manage” versus “Manage in Place”
Traditional approaches to information management are generally speaking no longer suitable to meet today’s information management needs. The legacy “move-to-manage” premise is expensive, fraught with difficulties and contradictory to modern data repositories that (a) are either cloud-based, (b) have built-in governance tools, or (c) contain data that best resides in the native repository.
In reality, traditional records management and ECM systems only manage a small percentage of an organization’s total information. A successful implementation is often considered 5% of the information that exists. What about all the information not deemed a “record”?
Traditional archiving systems tend to capture everything and for the most part cause organizations to keep their archived information for much longer periods of time, or forever. Corporate data volumes and the data landscape have changed dramatically since archiving systems became widely adopted. Some organizations are discovering the high cost of getting their data out while others are experiencing end-user productivity issues, incompatible stuns or shortcuts and a lack of support for the modern interfaces through which users expect to access their information.
The unstructured data problem, along with the emerging reality of the cloud, have brought us to an inflection point; either continue to use decade-old, higher-cost and complex approaches to manage huge quantities of information, or proactively govern this information where it naturally resides to more effectively identify, organize and advance the best possible outcomes for security, compliance, litigation response and innovation.
Today’s enterprise-ready hardware and storage solutions as well as scalable business productivity applications featuring built-in governance tools are both affordable and easily accessible. For forward-thinking organizations, there is no question that in-place information management is the most viable and cost-effective methodology for information management in the 21st century.
Cloudy, with a chance of eDiscovery
In the last year there has numerous articles, blogs, presentations and panels discussing the legal perils of “Bring Your Own Device” or BYOD policies. BYOD refers to the policy of permitting employees to bring personally owned mobile devices (laptops, tablets, and smart phones) to their workplace, and to use those devices to access privileged company information and applications. The problem with BYOD is company access to company data housed on the device. For example, how would you search for potentially relevant content on a smartphone if the employee wasn’t immediately available or refused to give the company access to it?
Many organizations have banned BYOD as a security risk as well as a liability when involved with litigation.
BYOC Equals Underground Archiving?
Organizations are now dealing with another problem, one with even greater liabilities. “Bring your own cloud” or BYOC refers to the availability and use by individuals…
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Tolson’s Three Laws of Machine Learning
Much has been written in the last several years about Predictive Coding (as well as Technology Assisted Review, Computer Aided Review, and Craig Ball’s hilarious Super Human Information Technology ). This automation technology, now heavily used for eDiscovery, relies heavily on “machine learning”, a discipline of artificial intelligence (AI) that automates computer processes that learn from data, identify patterns and predict future results with varying degrees of human involvement. This interative machine training/learning approach has catapulted computer automation to unheard-of and scary levels of potential. The question I get a lot (I think only half joking) is “when will they learn enough to determine we and the attorneys they work with are no longer necessary?
Is it time to build in some safeguards to machine learning? Thinking back to the days I read a great deal of Isaac Asimov (last week), I thought about Asimov’s The Three Laws…
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Information Governance and Predictive Coding
Predictive coding, also known as computer assisted coding and technology assisted review, all refer to the act of using computers and software applications which use machine learning algorithms to enable a computer to learn from records presented it (usually from human attorneys) as to what types of content are potentially relevant to a given legal matter. After a sufficient number of examples are provided by the attorneys, the technology is given access to the entire potential corpus (records/data) to sort through and find records that, based on its “learning”, are potentially relevant to the case.
This automation can dramatically reduce costs due to the fact that computers, instead of attorneys conduct the first pass culling of potentially millions of records.
Predictive coding has several very predictable dependencies that need to be addressed to be accepted as a useful and dependable tool in the eDiscovery process. First, which documents/records are used and who chooses them to “train the system”? This training selection will almost always be conducted by attorneys involved with the case.
The second dependency revolves around the number of documents used for the training. How many training documents are needed to provide the needed sample size to enable a dependable process?
And most importantly, do the parties have access to all potentially relevant documents in the case to draw the training documents from? Remember, potentially relevant documents can be stored anywhere. For predictive coding, or any other eDiscovery process to be legally defensible, all existing case related documents need to be available. This requirement highlights the need for effective information management by all in a given organization.
As the courts adopt, or at least experiment with predictive coding, as Judge Peck did in Monique Da Silva Moore, et al., v. Publicis Groupe & MSL Group, Civ. No. 11-1279 (ALC)(AJP) (S.D.N.Y. February 24, 2012, an effective information management program will become key to he courts adopting this new technology.