The Lifecycle of Information – Updated

Bill Tolson Information Governance , , , , ,

Organizations habitually over-retain information, especially unstructured electronic information, for all kinds of reasons. Many organizations simply have not addressed what to do with it so many of them fall back on relying on individual employees to decide what should be kept and for how long and what should be disposed of. On the opposite end of the spectrum a minority of organizations have tried centralized enterprise content management systems and have found them to be difficult to use so employees find ways around them and end up keeping huge amounts of data locally on their workstations, on removable media, in cloud accounts or on rogue SharePoint sites and are used as “data dumps” with or no records management or IT supervision. Much of this information is transitory, expired, or of questionable business value. Because of this lack of management, information continues to accumulate. This information build-up raises the cost of storage as well as the risk associated with eDiscovery. In reality, as information ages, it probability of re-use and therefore its value, shrinks quickly. Fred Moore, Founder of Horison Information Strategies, wrote about this concept years ago as the Lifecycle of Data. Figure 1 below shows that as data ages, the probability of reuse goes down…very quickly as the amount of saved data rises. Once data has aged 10 to 15 days, its probability of ever being looked at again approaches 1% and as it continues to age approaches but never quite reaches zero (figure 1 – blue shading).

Lifecycle of Data 1

Figure 1: The Lifecycle of Information

Contrast that with the possibility that a large part of any organizational data store has little of no business, legal or regulatory value. In fact the Compliance, Governance and Oversight Counsel (CGOC) conducted a survey in 2012 that showed that on the average, 1% of organizational data is subject to litigation hold, 5% is subject to regulatory retention and 25% had some business value (figure 2 – green shading). This means that approximately 69% of an organizations data store has no business value and could be disposed of without legal, regulatory or business consequences. The average employee creates, sends, receives and stores conservatively 20 MB of data per day. This means that at the end of 15 business days, they have accumulated 220 MB of new data, at the end of 90 days, 1.26 GB of data and at the end of three years, 15.12 GB of data (if they don’t delete anything). So how much of this accumulated data needs to be retained? Again referring to figure 2 below, the red shaded area represents the information that probably has no legal, regulatory or business value according to the 2012 CGOC survey. At the end of three years, the amount of retained data from a single employee that could be disposed of without adverse effects to the organization is 10.43 GB. Now multiply that by the total number of employees and you are looking at some very large data stores.

Lifecycle of Data 2

Figure 2: The Lifecycle of information Value

The above Lifecycle of Information Value graphic above shows us that employees really don’t need all of the data they squirrel away (because its probability of re-use drops to 1% at around 15 days) and based on the CGOC survey, approximately 69% of organizational data is not required for legal, regulatory retention or has business value. The difficult piece of this whole process is how can an organization efficiently determine what data is not needed and dispose of it using automation (because employees probably won’t)… As unstructured data volumes continue to grow, automatic categorization of data is quickly becoming the only realistic way to get ahead of the data flood. Without accurate automated categorization, the ability to find the data you need, quickly will never be realized. Even better, if data categorization can be based on the value of the content, not just a simple rule or keyword match, highly accurate categorization and therefore information governance is achievable.

“Move to Manage” versus “Manage in Place”

Bill Tolson Uncategorized , , , ,

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.

An Acaevo white paper on the subject can be downloaded here

Productivity and InfoGov; Are they Related?

Bill Tolson Information Governance , , ,

SymbiosisYes they are. Employee productivity is adversely affected by a lack of information governance (IG) in two ways. First, without IG, employees spend time “managing” their work files, contacts, emails and attachments. This management time includes reviewing content, deciding whether a particular file or email should be kept or deleted, deciding how long required emails will be kept and where, and finally, moving these files to their final storage location. Many research organizations and experts have stated that this content management time is estimated to consume anywhere from two to four hours per week. Consider a conservative example of two hours per week for this activity: this translates to 104 hours per year per employee or, for an organization of 5,000 employees, 520,000 hours per year devoted to individually managing data – that may or may not have been performed efficiently or effectively.

A second measure of lost employee productivity is in the number of hours per week that employees spend searching for information within the enterprise. Organizations without a centrally managed information management capability usually don’t actively manage employee file shares. When searchable central indexes are not available, employees fall back on simple keyword searches – which rarely produce the results the employee is looking for in a timely manner, if at all. In some cases, stored information might not be found due to weak or incorrect search terms, poor file naming, or the fact that the file wasn’t actually saved at all (i.e. the employee just thought it was).

This lack of information management can cost an organization a great deal and not even realize it.

Are Law Firms the Weakest Link in the Information Security Chain?

Bill Tolson Information Governance, Law Firm , ,

Many law firms are unwittingly setting themselves up to be a prime target for cyber criminals. But it is not the firm’s data that hackers might be looking for – it is the huge volume of client data that law firms handle on a daily basis that make them so appealing for cyber criminals to target.

eDiscovery continues to generate huge, and ever-growing data sets of ESI for law firms to manage. Those data sets are often passed to the client’s law firm for processing, review and production. The end result is law firms are sitting on huge amounts of sensitive client data and if the firm is not diligent about managing it, securing it, and disposing of it at the conclusion of the case.  And absent serious reforms in the Rules of Civil Procedure, these data volumes will only continue to grow.

A 2014 ABA Legal Technology Survey Report found that 14% of law firms experienced a security breach in 2013 which included a lost or stolen computer or smartphone, a cyber-attack, a physical break in of website exploit event. That same survey reported that 45% of respondents had experienced a virus-based technology infection and boutique firms of 2 to 9 attorneys were the most likely to have experienced an infection. Law firms of 10 to 49 attorneys were the most likely to suffer security breaches.

A growing number of clients are demanding their law firms take data security more seriously and are laying down the law – “give us what we want or we will find another law firm that will…” Generally speaking, law firms have never been accused of being technology “early adopters” and while they still don’t need to be, they do need to take client (and firm) data security and management seriously and adopt technology and processes that will both satisfy their client’s rising expectations as well as their cyber insurance providers best practices.

At the end of the day, law firms should ask themselves a basic question: is my law firm prepared and equipped to protect our client’s data and if not, what’s the best strategy for my law firm going forward?

For more detail on this topic, download the Paragon white paper on this subject.

Email Use Policies: The beginning of the end?

Bill Tolson Information Governance , , , ,

A December 2014 National Labor Relations Board (NLRB) decision in reference to the Purple Communications, Inc. case might have started the decline of employer’s rights over how their property and systems can be used by employees.

In the 2007 Guard Publishing decision, the NLRB held that the National Labor Relations Act does not give employees the right to use an employer’s email system for union-related business, i.e., activity not related to the running of the business. Partly because of this decision, employers have regularly created and enforced email use policies that forbid the use of the employer’s email system for anything other than actual company business. This decision was supposedly based on the NLRB’s comparison of an employer’s bulletin board, telephone system, copy machines and PA systems to the employer’s email system. In other words, employees did not have carte blanche to utilize these other systems for non-business-related activities either.

The NLRB Purple Communications decision reversed the 2007 ruling and held that employees do now have the presumptive right to use their employer’s email system for non-work NLRB-protected purposes. But does this decision also reverse the practice of employers restricting the use of the other systems (copy machines, bulletin boards, etc.) to strictly business-related purposes?

There are several points to keep in mind before taking over your employer’s copy machine to print 1,000 garage sale flyers.

  •  The 2014 NLRB-Purple Communications decision was limited to email systems only.
  • The 2014 NLRB-Purple Communications decision was limited to actual employees of the company—not family members or anyone else.
  • The 2014 NLRB-Purple Communications decision relates to activities protected by the National Labor Relations Act, i.e., union-related activities only.
  • The NLRB invalidated the prior validity of prohibitions of the non-work use of company physical property such as the previously mentioned copy machines, bulletin boards, and telephone systems.

Another interesting fact from the 2014 case is that the NLRB (re)confirmed an employer’s right to monitor its email system for “legitimate management purposes” and that employees continue to have no expectation of privacy in their use of the employer’s email system. But the NLRB stated that the employer may not increase employee email monitoring during union-organizing campaigns or focus monitoring activities on “protected” conduct or union activists specifically.

Obviously, the NLRB decision was directed specifically to companies with union membership and activities. But this raises the question of the use of employer equipment and systems for non-union-related activities. Will this decision be used to erode employer restrictions on the use of company property in the future?

InfoGov: Productivity Gains Equal Revenue Gains

Bill Tolson Information Governance , , , , ,

A great deal has been written on lost productivity and the benefits of information governance. The theory being that an information governance program will raise employee productivity thereby saving the organization money. This theory is pretty well accepted based on the common sense realization and market data that information workers spend many hours per week looking for information to do their jobs. One data point comes from a 2013 Wortzmans e-Discovery Feed blog titled “The Business Case for Information Governance – Reduce Lost Productivity!” that states employees spend up to nine hours per week (or 1 week per month or 12 weeks per year) looking for information. The first question to consider is how much of that time searching for information could be saved with an effective information governance program?

InfoGov Productivity Savings

Three months out of every year spent looking for information seems a little high… so what would a more conservative number be for time spent searching for information? In my travels through the archiving, records management, eDiscovery, and information governance industries, I have spoken to many research analysts and many, many more customers and have generally seen numbers in the 2 to 4 hours per week range thrown around. Assuming the four hours per week estimate, the average employee spends 208 hours per year (26 working days or 5.2 weeks) looking for information. Let’s further assume that an effective information governance program that would capture, index, store, and manage (including disposal), of all ESI per centralized policies would save 50% of the time employees spend looking for information (not an unrealistic estimate in my humble opinion), or 104 hours per year (13 days or 2.6 weeks). To bring this number home, let’s dollarize employee time.

Table 1 lays out the assumptions we will use for the productivity calculations including the average annual and hourly salary per employee.

Blog 08082014 t1

 

 

 

 

 

 

Table 2 below shows the calculations based on the assumptions in table 1 for weekly and annual time periods.

Blog 08082014 t2

 

 

 

 

 

Assuming a work force of 1000 employees at this company, the total annual cost of search is $7.5 million. Assuming a 50% increase in search productivity gives us an estimated $3.75 million saving from recovered employee productivity. In most cases, a $3.75 million annual savings would more than pay for an effective information governance program for a company of 1000 employees. But that potential savings is only a third of the recoverable dollars.

Another productivity cost factor is the amount of time spent recreating data that couldn’t be found (but existed) during search. Additional variables to be used for calculations include:

Blog 08082014 t3

 

 

 

Most employees will agree that a certain percentage of their search time is spent looking for information they don’t find…until well after their need has passed. This number is very hard to estimate but based on my own experience, I use a percentage of 40%. The other important variable is the amount of time (as a percentage) spent actually recreating the data you couldn’t find. In other words, the percentage of time (200%) of hours spent searching for information but not finding it (table 3).

Blog 08082014 t4

 

 

 

 

Table 4 above lays out the calculations showing the total hours wasted recreating data that should have been found of 166,400 across the entire company or $6 million. The assumption is that this wasted time spent recreating data not found would be reduced to zero with an effective information governance program.

So far the estimated saving based on recovered productivity (if they adopted an information governance program) for this company of 1000 employees is $3.75 million plus $6 million or $9.75 million (table 5).

Blog 08082014 t5

 

 

 

The last (and most controversial) calculation is based on the revenue opportunity cost or in other words; what additional revenue could be generated with a productivity recovery increase in employee hours? For these calculations we need an additional number; the annual revenue for the company. Divide this by the number of employees and you will get the average revenue per employee and the average revenue per employee per hour (table 6).

Blog 08082014 t6

 

 

 

 

How Does Productivity Affect Revenue

The last variable that needs an explanation is the “discount factor for revenue recovery” (table 6). This discount factor is based on the assumption that every recovered hour will not equal an additional (one for one) average revenue per employee per hour. Common sense tells us this will not happen but common sense also tells us that employees that are more productive generate more revenue. So in this example, I will use revenue recovery discount factor of 60% or 40% of the above $101.92 per hour number. This is met to impose a degree of believability to the calculation.

To calculate the total (discounted) recoverable revenue from improved information search we use the following formula: Estimated recoverable productivity hours for wasted search time * (the average revenue per hour per employee – (1 – the revenue recovery discount factor)) or 104,000*($101.92*(1-60%)) which equals $4,239,872 or $4.24 million.

Calculating the (discounted) recovered revenue from productivity gains from recreating data not found we will use the following formula: Estimated total hours spent recreating data not found * (1 minus the revenue recovery discount factor * the average revenue per employee per hour or (166,400*(1-60%)*$101.92) equals $6,784,000.

So to wrap up this painful experiment in math, the potential dollar savings and increased revenue from the adoption of an information governance program is:

Blog 08082014 t7

 

 

The point of this discussion was to explore the potential of using the concept of recovered revenue from increases in productivity from the more effective management of information – information governance. You may (probably) disagree with the numbers used, but I think the point of calculating an InfoGov ROI using recovered revenue due to productivity gains… is realistic.

 

Dark (Data) Clouds on the Horizon

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

Dark Cloud

 

There have been many definitions of “Dark Data” over the last couple of years including: unstructured, unclassified, untagged, unmanaged and unknown electronic data that is resident within an organization’s enterprise. Most of these definitions center on unstructured data residing in an enterprise. But with the advent of BYOD and employees use of personal clouds, this definition should be expanded to include any corporate owned data, no matter where it resides.

Dark data, especially dark data stored outside of the company’s infrastructure (and awareness that it even exists) is an obvious liability for eDiscovery response, regulatory compliance, and corporate IP security.

Is BYOC a good idea?

Much has been written on the dangers of “Bring Your Own Device” (BYOD) but little has been written on the dangers of “Bring Your Own Cloud” (BYOC) otherwise known as personal clouds. Employees now have access to free cloud storage from many vendors that give them access to their content no matter where they are. These same personal clouds also provide automatic syncing of desktop folders and the ability to share specific documents or even entire folders. These personal clouds offer a fantastic use model for individuals to upload their personal content for backup, sharing and remote availability. In the absence of any real guidance from employers, employees have also begun to use these personal clouds for both personal and work purposes.

The problem arises when corporate-owned data is moved up to personal clouds without the organization’s approval or awareness. Besides the obvious problem of potential theft of corporate IP, effective eDiscovery and regulatory compliance become impossible. Corporate data residing in personal clouds become “Dark Clouds” to the organization; corporate data residing in repositories outside the organizations infrastructure, management or knowledge.

Dark Clouds and eDiscovery

Organizations have been trying to figure out what to do with huge amounts of dark data within their infrastructure, particularly when anticipating or responding to litigation. Almost everything is potentially discoverable in litigation if it pertains to the case, and searching for and reviewing GBs or TBs of dark data residing in the enterprise can push the cost of eDiscovery up substantially. But imagine the GBs of corporate dark data residing in employee personal clouds that the organization has zero awareness of… Is the organization still responsible to search for it, secure it and produce it? Depending on who you ask, the answer is Yes, No, and “it depends”.

In reality, the correct answer is “it depends”. It will depend on what the organization did to try and stop employee dark clouds from existing. Was a policy prohibiting employee use of personal clouds with corporate data in place; were employees alerted to the policy; did the organization try to audit and enforce the policy; did the organization utilize technology to stop access to personal clouds from within the enterprise, and did the organization use technology to stop the movement of corporate data to personal clouds (content control)?

If the organization can show intent and actions to ensure dark clouds were not available to employees, then the expectation of dark cloud eDiscovery search may not exist. But if dark cloud due diligence was not done and/or documented, all bets are off.

Regulatory Compliance and Dark Clouds

Employee personal clouds can also end up becoming the repository of sensitive data subject to regulatory security and privacy requirements. Personally identifiable information (PII) and personal health information (PHI) under the control of an organization are subject to numerous security and privacy regulations and requirements that if not followed, can trigger costly penalties. But inadvertent exposure can occur as employees move daily work product up to their personal clouds to continue work at home or while traveling. A problem is many employees are not trained on recognizing and handling sensitive information; what is it, what constitutes sensitive information, how should it be secured, and the liabilities to the organization if sensitive information is leaked. The lack of understanding around the lack of security of personal clouds and the devices used to access them are a related problem. Take, for example, a situation where an employee accesses their personal cloud while in a coffee shop on an unsecured Wi-Fi connection. A hacker can simply gain access to your laptop via the unsecured Wi-Fi connection, access your personal cloud folder, and browse your personal cloud through your connection (a password would not be required because most users opt to auto-sign in to their cloud accounts as they connect on-line).

As with the previous eDiscovery discussion, if the organization had taken the required steps to ensure sensitive data could not be leaked (even inadvertently by the employee), they leave themselves open for regulatory fines and more.

Reducing the Risk of Dark Clouds

The only way to stop the risk associated with dark clouds is to stop corporate data from leaving the security of the enterprise in the first place. This outcome is almost impossible to guarantee without adopting draconian measures that most business cultures would rebel against but there are several measures that an organization can employ to at least reduce the risk:

  • First, create a use policy to address what is acceptable and not acceptable behavior when using organization equipment, infrastructure and data.
  • Document all policies and update them regularly.
  • Train employees on all policies – on a regular basis.
  • Regularly audit employee adherence to all policies, and document the audits.
  • Enforce all breaches of the policy.
  • Employee systematic security measures across the enterprise:
    • Don’t allow employee personal devices access to the infrastructure – BYOD
    • Stop employee access to personal clouds – in many cases this can be done systematically via cutting specific port access
    • Employ systematic enterprise access controls
    • Employ enterprise content controls – these are software applications that control access to individual content based on the actual content and the user’s security profile.

Employee dark clouds are a huge liability for organizations and will become more so as attorney’s become more educated on how employees create, use, store and share information. Now days, discoverable and sensitive data can migrate out of the control of an enterprise in the blink of an eye. The question is what the organization does to prevent it…