machine learning

You Don’t Know What You Don’t Know

Bill Tolson Information Governance , , , , , , , ,

Blog_06272014_graphicThe Akron Legal News this week published an interesting editorial on information governance. The story by Richard Weiner discussed how law firms are dealing with the transition from rooms filled with hard copy records to electronically stored information (ESI) which includes firm business records as well as huge amounts of client eDiscovery content. The story pointed out that ESI flows into the law firm so quickly and in such huge quantities no one can track it much less know what it contains.  Law firms are now facing an inflection point, change the way all information is managed or suffer client dissatisfaction and client loss.

The story pointed out that “in order to function as a business, somebody is going to have to, at least, track all of your data before it gets even more out of control – Enter information governance.”

There are many definitions of information governance (IG) floating around but the story presented one specifically targeted at law firms: IG is “the rules and framework for managing all of a law firm’s electronic data and documents, including material produced in discovery, as well as legal files and correspondence.” Richard went on to point out that there are four main tasks to accomplish through the IG process. They are:

  • Map where the data is stored;
  • Determine how the data is being managed;
  • Determine data preservation methodology;
  • Create forensically sound data collection methods.

I would add several more to this list:

  • Create a process to account for and classify inbound client data such as eDiscovery and regulatory collections.
  • Determine those areas where client information governance practices differ from firm information governance practices.
  • Reconcile those differences with client(s).

As law firms’ transition to mostly ESI for both firm business and client data, law firms will need to adopt IG practices and process to account for and manage to these different requirements. Many believe this transition will eventually lead to the incorporation of machine learning techniques into IG to enable law firm IG processes to have a much more granular understanding of what the actual meaning of the data, not just that it’s a firm business record or part of a client eDiscovery response. This will in turn enable more granular data categorization capability of all firm information.

Iron Mountain has hosted the annual Law Firm Information Governance Symposium which has directly addressed many of these topics around law firm IG. The symposium has produced ”A Proposed Law Firm Information Governance Framework” a detailed description of the processes to look at as law firms look at adopting an information governance program.

Infobesity in the Healthcare Industry: A Well-Balanced Diet of Predictive Governance is needed

Bill Tolson Information Governance , , , , , , , , ,

Fat TwitterWith the rapid advances in healthcare technology, the movement to electronic health records, and the relentless accumulation of regulatory requirements, the shift from records management to information governance is increasingly becoming a necessary reality.

In a 2012 CGOC (Compliance, Governance and Oversight Counsel) Summit survey, it was found that on the average 1% of an organization’s data is subject to legal hold, 5% falls under regulatory retention requirements and 25% has business value. This means that 69% of an organization’s ESI is not needed and could be disposed of without impact to the organization. I would argue that for the healthcare industry, especially for covered entities with medical record stewardship, those retention percentages are somewhat higher, especially the regulatory retention requirements.

According to an April 9, 2013 article on ZDNet.com, by 2015, 80% of new healthcare information will be composed of unstructured information; information that’s much harder to classify and manage because it doesn’t conform to the “rows & columns” format used in the past. Examples of unstructured information include clinical notes, emails & attachments, scanned lab reports, office work documents, radiology images, SMS, and instant messages. Despite a push for more organization and process in managing unstructured data, healthcare organizations continue to binge on unstructured data with little regard to the overall health of their enterprises.

So how does this info-gluttony, (the unrestricted saving of unstructured data because data storage is cheap and saving everything is just easier), affect the health of the organization? Obviously you’ll look terrible in horizontal stripes, but also finding specific information quickly (or at all) is impossible, you’ll spend more on storage, data breaches will could occur more often, litigation/eDiscovery expenses will rise, and you won’t want to go to your 15th high school reunion…

To combat this unstructured info-gain, we need an intelligent information governance solution – STAT!  And that solution must include a defensible process to systematically dispose of information that’s no longer subject to regulatory requirements, litigation hold requirements or because it no longer has business value.

To enable this information governance/defensible disposal Infobesity cure, healthcare information governance solutions must be able to extract meaning from all of this unstructured content, or in other words understand and differentiate content conceptually. The automated classification/categorization of unstructured content based on content meaning cannot accurately or consistently differentiate the meaning in electronic content by simply relying on simple rules or keywords. To accurately automate the categorization and management of unstructured content, a machine learning capability to “train by example” is a precondition. This ability to systematically derive meaning from unstructured content as well as machine learning to accurately automate information governance is something we call “Predictive Governance”.

A side benefit of Predictive Governance is (you’ll actually look taller) previously lost organizational knowledge and business intelligence can be automatically compiled and made available throughout the organization.