Document Management 3.0: Big Data, IoT And Infonomics

Document management is sometimes used synonymously with records management by some organizations and record managers. For this article document management is the capture, indexing, valuation, storage, modification and sharing of digital files within an organization. Regardless of how an organization decides what encompasses records or document management, a digital file remains an object that needs to be indexed, valued and shared. Document management has become more than a simple matter of just being part of the lifecycle management in the traditional sense (creation, use, and final disposition), especially when most organizations keep most of what they create. But even beyond the reality that most information (records, documents or data) is kept beyond its retention, is the arrival of Big Data and IoT. Big Data and IoT has permanently changed the information landscape. By 2030 most digital devices created will be in some way connected to the internet and therefore be able to create and receive data. No doubt this information will find itself in digital files or will somehow be attached to data (created from these devices), just as digital files are attached to email today. But this ability to capture data from all devices (soon to include your clothes and brain) will enhance what is considered a digital file. For example, a digital file could be several data points from several IoT devices that are about a subject or person. What does this mean for document management? Simply, that document management will need to be managed with its purpose to understand its value and what it entails beyond its current understanding to include probable and possible future business needs. This evolving information landscape will increase the risk/reward information dialectic. What is this risk/reward information dialectic that I speak about?

It is continuous tension that exists between keeping information to discover some possible or potential business value, especially with Big Data and IoT, and deleting it because of the risk it holds either from legal eDiscovery, data breach or privacy concern. These two divergent positions are represented by two opposing camps. One group we can dub the “compliance hawks” (actually the term Nazi has been used instead of hawk by different departments in my experience) and the other by the term “creators” (or “data hippies” as some in the compliance field have been known to call them).

The following departments tend to be divided in the following manner. On one side of the divide sits the information governance, compliance, privacy and security staff, while the other side knowledge management, business intelligence, and Big Data scientists hold the “keep nearly everything” position. (To their defense and credit that usually means keeping data that has some form of value related to the business, what that maybe is still debated especially how that is resolved). This retention tension is only rising with the increasing data breaches that have occurred over the last several years: Target, Equifax, Facebook, Under Armour, and Fed Ex. These retention issues with the corresponding rise of devices connected to the internet along with the shift and understanding, that all information is an asset, is leading to a continued form of conflict. 

This conflict does have a resolution but it requires a different approach to evaluating information.

Retention of information (records and documents being the area most effected) is developed by following the classical taxonomy in appraising and classifying information according to its operational, administrative, fiscal and legal value, with legal being the most paramount. Business needs are addressed, but usually only the perceived immediate value of the information which sometimes results in the decision to keep everything (or delete everything but usually that is not the case), regardless of the retention policy. This often occurs because there is lacking a methodology or means of quantitative measurement. So how can we address this problem that is currently increasing with the introduction of Big Data and IoT?

Information as an asset is not a new concept, but organizations have encountered difficulty in attempting to understand how to value it. In 2011, Douglas Laney, an analyst at Gartner, introduced a new understanding of information: Infonomics. Infonomics is the economic theory of information as a new asset class, and the discipline of accounting for, managing, and deploying information just as any other enterprise asset. Infonomics has a set of principles and quantitative formulas that allow for its measurement and that may be adopted by document management: information is an asset that has potential, probable, and realized value; and its value can be discovered and qualified beyond its immediate use.

According to Laney, Realized value relates to current capabilities and solutions that information may convey; and what usually concerns document management immediately. Probable value describes how information may be used; and potential value refers to how information may be applied to any business process or need regardless of its (realized) immediate value. The gap between these values is known as the information value gap. These values offer new ways of seeing and valuing information. In essence, all information may have some form of business value, even if it may not be immediately known, this value needs to be understood because potential value may be discovered and shared with different business areas that may create new insights and lead to improved decision making.

The arrival of Big Data and IoT requires new principles in management of information, which includes rethinking how it is valued and shared. Infonomics allows for the ability to discover through a quantitative methodology possible and potential business value that may be able to address the risk/ reward dialectic and bring together departments in a common goal.