The Deloitte Center for the Edge has published a report on the decline in the return on assets of American businesses over the past 40 years. Jon Taplin, a professor at USC, posted a very insightful summary of the report, likening this decline and how it was hidden to a shell game. What was interesting to me in his post was how the blind obedience to a particular metric has been in large part to blame for our current financial insanity.

What the Deloitte report points out is that companies have been able to “juice” their return-on-equity (ROE) numbers by consistently taking on more and more debt. Meanwhile, their return-on-assets (ROA) have fallen steadily. If you are even a casual investor or small businessperson, you’ve probably heard of ROE and why it is important. You may not have heard of ROA. Let me briefly explain the difference

Return on equity is a company’s annual net income divided by total shareholder equity. Shareholder equity is essentially how much money investors have put into your company, so ROE measures how effective you are at generating a return on invested funds.  Return on assets, on the other hand, is your annual net income divided by total assets. ROA, therefore, measures your effectiveness at generating a return on everything the company owns and is in the bank.

You may already be seeing the disconnect, just based on my choice of words when I defined ROE and ROA above. Let me give you an example: Let’s say you have two companies, A & B. Each of these companies generates 1 M$ per year in net income. Each of these companies has 5 M$ in equity on the books, meaning that the investors have 5 M$ in them. In each case, the ROE of the company is 20%. Not too shabby. But there is an important difference between them. Company B also has 5 M$ of debt outstanding. Company B will thus have 5 M$ more assets on the books than Company A, and thus their ROA will be lower.

If you’re a CEO and you’re managed by your board on the basis of your ROE, you thus have a substantial incentive to leverage your company with loads of debt in order to have more resources with which to expand your business, since that debt doesn’t show up directly on your measurements. People will still invest in your company on the basis of your keen ROE (so long as they don’t look at your debt-to-equity ratio, or your actual return on assets.)

The bottom line here is that a lot of people had a warning right in front of them about what was happening with GM and other companies, but couldn’t see it because one of their chief metrics hid it from them. As with so many other things, relying on a few simple metrics is dangerous and sloppy. Simple metrics are useful, but they must be cross-checked and reviewed with a constant eye on exactly what they tell you and what they do not.

In my mind, this is a clear argument for open access scientific journals. While it would be possible to corrupt the process of peer review in an open access journal, it would be pretty difficult to hide the fact for very long.

I’m going to talk about how that love affair manifests, how you manage it, and how you move past it into a wonderful relationship that will hopefully last many years, bring in revenue, and perhaps even change the world.

What do I mean by “technology” here? It might mean the latest programming language hotness or it might mean the latest thing in biofuels or in carbon sequestration. It could mean the latest gadget for helping you build a Twitter-enabled flowerpot. Technology, as a functional definition, can be said to be the use of knowledge to interact with our environment – both our physical environment and our social environment. How can you not fall in love with something that is so fundamental?

The problem comes when we expect everything from some bit of technology. You find it and it looks cool. Novel. You can do things with it that you’ve never done before. So you start using it and soon you want to use it to do some other things too. Things that are important, but maybe things that the technology really wasn’t supposed to do. Like using the butt-end of a screwdriver to pound in a nail. Or trying to write a fast Fourier transform in pure Java. You can probably think of your own examples.

The main thing that I do at my job is technology development. We see the seeds of a new technology – sometimes they’re at the “little black speck in a paper envelope” stage. Sometimes they’re at the “tiny sprout” stage or even at the “seedling ready to transplant” stage. We either develop the technology ourselves or partner with the people who are currently developing it, when we think there is a good fit. We take it, grow it until its ready to bear fruit and then commercialize it. And sometimes, we see a technology that sets our hearts pounding. Our pupils dilate, our palms get sweaty – we fall in love. And that love is a beautiful thing, because it means that the technology is likely to be useful and that there is going to be an internal champion for it.

The problem comes a little ways into the relationship. Sure, your new love is a fun to take into the lab and gives you that sizzle when you’re down and dirty with it. You want to solve every problem with it. It’s a sexy beast! But, after a few months with it, your new love’s moved into your apartment and you’d really like for him to – ya know, pay some rent? Plus, he drips on the toilet seat occasionally and leaves his dirty dishes all over the place. But still, you can’t let him go because, well…. he’s just so good!

At some point, you have to make a decision, just like you do with every relationship. Maybe it was just a fling – you learned something from it and had a good time doing it, but it’s just not for you. Or maybe this one is worth keeping. Sure, he’s rough around the edges, but a little time and effort and he’ll be something to take home to Mama. Or at least to your C-office.

What makes the difference between the one you kick out of bed and the one you go steady with is how honest you are with yourself when you’re learning about the technology. If you don’t quickly learn what the limitations of the new technology are, your new love is bound to disappoint. And as thorough as you might be up front about the technical limitations, you’re going to have a second round of limitations that come up each time you try to take that technology to a new market. It’s just like taking that new girl you hooked up with a few months ago to an office party and watching her get hammered and puke into the ficus. Quickly, you learn that while she’s an excellent choice for the “wild night on the town” market, she’s perhaps not quite the one for the “impress your clients” market.

One way we keep track of our current thinking about a technology is with a chart like this:

This sort of bubble chart keeps track of where the technology falls in your portfolio in terms of strategic fit, development stage, and expected financial return. The size of the bubble indicates the financial return, typically either by “best N years” performance or “N years post launch” performance. The chart above shows some typical positions in a portfolio. You tend to want to have a decent scatter over the chart, but ultimately, if you’re a technology-driven company, you don’t want to have a lot of things sitting in the lower left hand corner. That means your putting a lot of resources into things that are farther out and will ultimately bring you no new revenue.

During the course of a love affair with a particular technology, you will sometimes find that its trajectory across the chart will look something like this:

This happens a lot more frequently than you might suspect. By the time people take a long hard look at what they’ve invited into their bed … lab, that is, they’ve realized that the technology just isn’t as shiny as it used to be. Back when things were good, you dumped a lot of resources into it. Lots of fancy dinners, flowers, jewelry. You put time and effort, maybe even a sizable fraction of your available development staff, into making this technology go. And for what?

Some of this is inevitable. You’re never going to know a priori what a technology’s domain of applicability is. And if you spend too much time trying to map that out oh-so-carefully before you start, you’ll never bring anything to market. Further, your initial estimates are just that. You’re going to overestimate its financial impact – except in those rare cases where you’ve underestimated it. You’re going to completely misjudge how close to commercialization the technology is. But ultimately, the goal is to move past the love affair into a stable relationship, where there is a clear path from technology to product.

The key to managing this transition is flexibility. Once you’ve made a decision to try the thing out, you must constantly be looking for the mismatches between your technology and the problems you’re looking to solve with it. At the same time, you need to be flexible enough to realize that while the new technology may not solve the particular problem you’d set in front of it, there may be a completely different problem that it is particularly adept at solving.

There’s a lot left unsaid here about technology development: commercialization strategies, product migration maps, technology adjacencies, market adjacencies. I’m not going to talk about those here. They’re important and you should be thinking about them, but they don’t help you deal with the emotions behind finding a new technology, rubbing the shiny off of it, and dealing with the commercialization endgame.

Do you have to be in a research and development group to follow this advice? Not at all. The advice holds for anyone who is prone to love affairs with technology. The specifics of methods and desired outcomes will change. Some people will try Ruby on Rails and ultimately find that sleek hottie too much for them, returning to the stable, quiet hippie in Django. Some folks will be looking for a greener energy source to put their activism into, take that one look at Clean Coal, realize that she’s just a two-bit whore and pass on to something that they know they can support while keeping their self-respect. At the end of the day, everyone will fall in love with a technology at least once. The trick is to be honest enough to know when its not the technology you’re seeking and flexible enough to let it solve the problems at which it is best.