Last month, I wrote an article for CMSWire: Real World Digital Asset Management ROI where I looked at some of the popular methods you see used for ‘proving’ ROI in a quantitative fashion. The summary was that when consultants, analysts and vendors offer hard ROI numbers to support a pre-implementation business case for DAM, they’re probably at best wrong and, more often than not, a complete work of fiction.
I am seeing more of these type of justifications for DAM than I recall in the past. In addition to the article referred to, my co-contributor, Naresh Sarwan, has questioned some vendors’ statistical claims about the ROI of their products last week and in the past also.
I have noticed some other articles in the more general DAM trade press too, including this from Ed Smith over on DAM Coalition. Below are a couple of quotes from the example ROI calculation exercise in his article:
” Let’s say we have 5 people that each make around $50,000 each year and waste 1 hour each week searching for images.” [Read More]
“Let’s figure in that DAM cuts the time it takes to find images by 75%” [Read More]
There are some obvious potential issues here which I have highlighted. The first is, how do you know how much time exactly was wasted? Who measured that and how did they do it? The second is the implied saving made by DAM. Where is the 75% figure derived from?
Ed doesn’t share the source of his figures (that I could find anyway) but my expectation is that they are obtained from an analyst study where the research method is flawed. It’s important to remember that analyst reports are not like medical or scientific journals where the authors are going to get heavily scrutinised and challenged on their methods.
As I will discuss below, even if the analysts involved are entirely impartial or you try to come up with your own average figures, the nature of Digital Asset Management makes the whole exercise impossible to reduce down to a universal series of numbers that you can realistically interchange between different scenarios and still expect to get reliable results back.
Ed finishes up with this:
“In this scenario, our DAM system provides a ROI of 56% in the first year. If this DAM was a savings account, I’d put all my money in it (especially in this economy!)” [Read More]
Or perhaps more like a Bernie Madoff style Ponzi scheme?
I have probably been unfair on Ed Smith in my critique as he is far from alone in using these methods. I also need to acknowledge that many of his other articles on CMSWire and DAM Learning Center are first class and offer a great introduction to the key concepts of DAM.
This Infosys infographic is typical of many other similar examples and a brief glance over various other DAM vendor websites will quickly find you others. The colourful designs and highly stylised presentation give the game away that these are more marketing ‘sales tools’ than the indecipherable spreadsheets or scrawled ledgers that are the more familiar tools of the trade used by accountants and other finance personnel.
The fact is that you cannot accurately measure ROI up-front for DAM by plugging in numbers from third party sources. There are many reasons why this doesn’t work; here are a few:
- Digital assets are not commodities. They rarely have many uniform characteristics at all, otherwise you would be unlikely to need a DAM system to manage them in the first place.
- Organisations where DAM systems are deployed are similarly diverse. Levels of education, IT literacy, experience and understanding of DAM concepts like metadata and various other human factors can impact how effective DAM systems might be and how quickly the users can get to grips with them.
- The method by which DAM is implemented – especially the findability and metadata strategy can radically alter how many assets get found and with what speed.
- The size of the repository can impact the figures. A large DAM with hundreds of thousands of assets will increase the likelihood of something being found, but reduce the chances of exactly the right asset being located. It’s vice-versa for smaller libraries and the shape of the ROI curve can alter either way as a result.
- The level of training can make a major difference to the level of usage and how effective it is. It’s not just the act of delivering training either, but the quality and depth of the tuition also.
- One of the other features of many enterprise DAM systems is that the end users occupy a dual role as asset suppliers and consumers – often simultaneously. These can further impact ROI in ways that the vendor can only influence rather than fully control.
I am not opposed to quantitative ROI studies, but they have to be based on real numbers, not guesswork. You cannot properly conduct that task until you have something (like a DAM system) to automatically collect the data directly and in real-time. Continuous analysis of ROI is an essential element of a Digital Asset Management strategy, it’s a fluid and on-going process, not a do it once and forget about it job.
There is nothing wrong with selling or marketing DAM software and services, but when it comes to properly assessing ROI, those on the sell-side of DAM owe it to their clients and customers to be absolutely 100% honest. Setting aside any ethical arguments about why it’s a bad idea to tell your customers fibs about what your products will do for them in the manner of some 19th century travelling apothecary, the obvious flaw with this technique is that before long you are going to get called to account and asked to prove your case. This could pose a problem if there is no factual basis to support the original assumptions.
If the business has based its strategy on some fabricated numbers that don’t bear up to reality, there is a risk that the whole DAM market segment will be mistrusted if extra noughts get added to the level of capital expenditure committed without those original promises being kept.
Analysis of the state of the CRM software market should prove instructive as to why this is not a sustainable long-term strategy. Similar excessive ROI claims were once made by vendors in that market to justify the expense of developing bloated products that attempted to cover numerous disparate end user requirements – which they often spectacularly failed to do. If you do Internet searches for anything about CRM now, the auto-suggest feature that most search engines now have will propose ‘failure’ as an additional term with little additional effort required. There are entire websites devoted to detailed analysis of what went wrong – which should be required reading for anyone actively involved with DAM as there are some definite parallels emerging.
This is a subject for another article, but the ROI proposition for DAM segues into some other trends currently in play as users start to expand the scope of their requirements. The DAM market is currently in danger of going through a collective second system syndrome. We need to carefully manage our clients exposure to risk by providing tight, well defined, modular and interoperable products that allow more precise targeting towards proven need, not making up numerical fantasies to justify ever higher budgets for excessive solutions that might never produce a positive ROI.
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