In common with most types of business oriented software, DAM systems need to have business case that can demonstrate an opportunity to gain a positive ROI (Return On Investment). While all this is obvious stuff that you don't need an MBA to understand, proving an ROI case is harder than it might superficially appear.
In this article I am going to critique one of the more popular methods that is often used and discuss alternatives which I believe are more realistic and useful.
Statistical Follies And Quantitative Farces
The kind of ROI data that corporate bean counters like is typically numerically oriented and reads as though someone has gone away and scientifically measured activities using a precise and rational method. Statements like "we can save x per employee" usually go down well.
Most pre-implementation statistical evaluations will be composed of some ROI propositions — with search related activities being prevalent (as you would expect for DAM). A common method is to then work backwards from each of these benefits and estimate how long they take as an average. For example:
Average Search Time Per Hour x Employee Cost Per Hour = Current Hourly Cost To Business
Theoretically, this tells you how much it costs to search for assets without a DAM. Various analyst supplied stats about how much time you can save by using a mythical generic DAM system then get quoted (30% seems to be popular). The discount factor is then applied to tell you the ROI.
This sounds reasonable, but there are assumptions and fallacies baked into this technique that, in my opinion, render it unfit for purpose.
Firstly, it is unlikely that anyone ever measured the length of time required to find assets before they planned to implement DAM. So that poses the question, how has the average search time been derived? The duration required to find assets is non-trivial to predict.
For example, the artwork you were reviewing this morning you can probably find in seconds. Something from last week, maybe a minute or two. Photos of discontinued product from several years ago could take hours or even days. On other occasions you might be persuaded to cut your losses after an unsuccessful search and use an alternative you can find, even if not exactly what you were looking for.
Some might argue that you can still average these durations out to identify a mean figure. That might be correct, but when are you going to start and end your sampling period? If a succession of hard to find assets are required, the average figures can trend upwards (and vice versa too). To get a properly representative sample, you need to carry this out over a period of many months — usually far more than the limited time available to decide whether you should be investing in DAM or not.
Properly measuring the time needed to find digital files is difficult to do accurately at scale. Kitting out every prospective DAM user with stopwatches and something to record the results is hardly practical.
Instead, what usually happens is that staff get asked to make an estimate (or more likely a guess) about how long they think it takes them to find assets on average. That prompts a series of HR issues also since they might be unwilling to admit that they spent hours searching for something trivial because they (or a colleague) filed it in the wrong folder and no one noticed until they tried to find it again.
While DAM systems give you some tools to allow you to aggregate assets and deal with them as though they were generic commodities, the reality is that each asset itself is usually a one-off instance with some characteristics that might make it uniquely valuable. You cannot appreciate exactly how much until you need to find them. If it were the case that assets were commodities, you would not have to sift through thousands of records to find the exact one you need and worry about topics like findability etc.
This raises the other issue, who comes up with these average percentage savings made possible by DAM and what is their exact method for working them out? How do they explain why a generic average can be applied in all cases?
Software applications are mechanical in nature and merely having a licence to use one is not sufficient to generate a cost saving. There are many additional factors that can have a bearing upon the productivity improvement available. A less versatile DAM can generate a higher ROI than a more fully featured system that is managed by those who lack the expertise to use it properly. In other words, ROI is heavily influenced by characteristics of the implementation rather than the technology itself.
Continue reading this article:
Source : cmswire[dot]com
No comments:
Post a Comment