InfoGov: Productivity Gains Equal Revenue Gains


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.

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

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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:

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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).

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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).

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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).

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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:

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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.

 

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