Do You Have A “Leaver” Data Problem?

Bill Tolson Archiving , , ,

Everyone leaves the company eventually. Better opportunities, reduction in workforce actions, termination, or your manager has the IQ of un-popped popcorn…, no matter the reason, everyone eventually leaves. In the UK, these people are referred to as “leavers.” In the U.S. they’re called departing employees or ex-employees, and depending on the circumstances, more colorful names. However, the way company handles these departing employees can mean the difference between business as usual or major customer satisfaction issues, project delays, higher eDiscovery costs, and higher costs.

When an employee is terminated or informs the company they are leaving, the HR organization usually has a checklist of things to do before the employee departs. This includes (but is not limited to):

  1. Return credit cards
  2. Turn in all expense reports
  3. Turn in laptop
  4. Turn in external hard disks
  5. Turn in cell phone
  6. Returning building and office keys and access cards
  7. Removing access/User ID to all electronic systems

Pretty standard stuff to ensure the employee does not walk off with company equipment or confidential information. However, this process does not address the most valuable company asset…information.

Is Departing Employee Data Valuable?

At its base level, companies employ people to create, process, and utilize information. What happens to the GBs of data the employees create and store over their time at the company? True, much of that information is stored on the employee’s laptop but how long do those laptops sit around before they’re re-imaged and re-tasked? In a blog last month, I touched on this specific problem

“Not long ago I received a call from an obviously panicked ex-coworker from a company that I had left 6 months prior. They were looking for the pricing/ROI calculator that I had developed more than a year prior. A large deal was dependent on them producing a believable ROI by the next morning. I told the ex-coworker that it and all of my content should be on my laptop and even suggested a couple of keywords to search on. Later that day, the same person called back and told me that the company’s standard process for departing employee’s laptops was to re-image the hard disk after 30 days and distribute it to incoming employees – the ROI model I had spent over a man-month developing was lost forever.”

Now consider the numerous other places an employee can store data; file shares, cloud storage accounts (OneDrive, Dropbox), cell phones, SharePoint, One Note, PSTs, etc. Now also consider how you would find a specific file containing a customer presentation in a short period of time…

If not managed as a valuable company asset, much if not all of that expensive employee data is, if not lost, is extremely difficult if not impossible to find when needed.

Chaotic Data Management Makes You a Target

Let’s address another problem associated with ex-employee data… eDiscovery.

You’re a General Counsel at a medium sized company and you receive an eDiscovery request one afternoon asking for all responsive data around a specific vendor contract between Feb 4, 2009 and last month. Several ex-employees are named as targets of the discovery.

This is a common scenario many companies face. The issue is this; when responding to discovery, you must look for potentially responsive data in all possible locations, unless you can prove that data could not exist due to existing processes. The legal bottom line is this: if you don’t know for sure that data doesn’t exist somewhere, then you must search for it, no matter the cost. Opposing Counsel have become very adept at finding the opposing parties weakness, especially around data handling, and exploiting it to force you to send more money so that you will settle early.

Discovery response also carries with it a time constraint. This time required to respond has caused many companies to spend huge amounts of money to bring in high priced discovery consultants to ensure discovery is finished in time.

Both of these issues can be readily addressed with new processes and technology.

Process Change and Technology

Worthless data can be extremely valuable when you can’t find it. Most companies I have worked for were very good about the employee exit process. But so far I have never had an HR (or other) person ask me specifically for all of the locations my data could be residing.

The laptop and cell phone are turned in and quickly re-imaged (losing all data), file shares with work files and PSTs are eventually cleaned up destroying data, and email accounts are closed. Very quickly, all of that employee data (intellectual property and know-how) is lost.

In reality, all it takes to solve this problem is first to develop an exit process that ensures the company knows where all employee data is before they leave, and second, migrate all of that ex-employee data to a central repository for long term management. Many companies are finding that a low cost “cool” cloud archive is the best and lowest cost answer.

Leavers2.jpg

Just because an employee has departed doesn’t mean their intellectual property has to as well. Keep that ex-employee information available for business use, litigation, and regulatory compliance well into the future.

The Industry’s First “Leaver” Archive

Microsoft Azure is that low cost cool data repository.   Archive360’s Archive2Azure provides the management layer for Azure to allow this departing employee data to be migrated into Azure, encrypted, retention/disposition applied, and custom indexing processes enabled to provide centralized ultra-low-cost cool storage so that grey, low touch, ex-employee data can be managed and searched quickly.

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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.