December 20, 2017
When Did We Get So Satisfied With 10% Innovation?
I came across a research study recently that, frankly, surprised even this industry veteran with deep knowledge of the tech space. The study found that of a large business’ typical technology spend, 90% is devoted to preserving the existing state of technology at the company while only 10% is spent on doing something new.
10%! In the past, some of us may have looked at a tech company with this type of spend breakdown and thought, “Wow, isn’t it innovative that a company is devoting a portion of their funds to work on something totally different than what they do now?”
Let’s take a closer look at what we’re really talking about, though. The reality is that a company that is only 10% innovative can’t be considered good enough.
Yes, they can continue to devote 90% of their finances to staying current and “getting by” but at some point, new technologies do come along. If the company hasn’t evolved by then, it’s a company that’s going to be seen as a dinosaur.
Expect, Anticipate And Act – There Is No Choice
I’ve been reading a very good book called "What To Do When Machines Do Everything" by Malcolm Frank, in which the author points out that as automation and various forms of machine intelligence become more capable, they will permeate our business lives and at some point, our personal lives.
For a variety of companies wondering what machine learning may mean for their business in both the short-term and long-term, Frank advises them to expect a trend so they can better anticipate that trend rather than wait for it to arrive and then wonder what they do next. With better expectations in advance, a company has a much better chance of taking advantage of the trend when tools become available that are used for particular business problems and processes.
What follows in his line of thinking is particularly important – he urges large, established companies not to cling to the old way they've done it for the last 50 years, but to try to figure out how to re-engineer their business to be built around more digital trends. This way, they can collect more information and figure out how their business relates to this new technology about to be used.
Now, I don’t believe automation will fully displace human intelligence and analytics. There's still great value for companies to use automation technologies to look at vast data sets, but we also have to account for the human factor in helping machine learning technologies continue to progress.
Do We Have All The Data We Could Ever Want? Not Quite.
Some think we have all the data we could ever want, but I take issue with that. Suddenly, the amount of data created for day-to-day things like smart watches means there's always data that could be analyzed and consumed. And even within all of this consumption and analysis, do machines know how to place the data in the proper context of what the organization is striving to accomplish and make meaningful suggestions? Sometimes yes, sometimes no.
Let me give you an example of what I mean.
The other day, I received a notification from Google Maps that said, "Here's what you've done for the month of November." It could identify when I left my house, parked my car and so forth. Google Photos can tell me that it just organized all of my photos for me so that I can look at all the appropriate people I’d like to. So there’s certainly a fair amount of data and organizational capability, but when it comes to connecting the dots from data to insight, there is still room for continual improvement.
Instead of hoarding data and trying to save it all for analysis at some later time and having people drown in that data trying to analyze it, there are a variety of applications related to machine learning pointing toward a smarter way such as subscribing to a thousand news feeds, then having an intelligent assistant scan through those and figure out what you need to consume most.
How Do We Get More Than 10% Innovative?
Within your company, you should be continually looking at your technology stack and saying, "Which technology is still producing the best returns for me,” and “Which technologies I should get rid of and replace?"
Perhaps that’s a complex question to answer, but that doesn’t mean the status quo is perfectly acceptable either. Consider that a great many of the largest financial institutions have never figured out how to get rid of old technology that, for whatever reason, they’ve grown to depend on.
In fact, I know some of the largest brokerages still have mainframes circa 30 years ago as the core of their technology. These same brokerages are also still using Cobal and CICS because after these technologies and others like them were built, nobody invested in a mission of keeping up with the times and planning for the future from a technological standpoint. Consequently, you still have technology in some of these brokerages that was popular in the late 1970's and early 1980's.
What this essentially says to me is that you have technological progress creeping along that many organizations have somehow managed to more or less avoid. Unfortunately, they can’t figure out how it’s probably easier to jettison certain technologies. They also can’t figure out what progress really looks like because the people running the company didn't appreciate how fast technology is changing or
should be changing. As such, they didn't build into their business (and their business perception) the fact that technology will be ever-changing and that it will be getting continually cheaper and more capable. Consequently, these organizations may be just fine for quite a while. They can even be leading the way – until a new type of disruption comes along.
When that disruption occurs, how easy will it be for a large organization to be quickly adaptable? Can your company go from a 30-year-old system at its core to a system that’s 3 months old? That might be very difficult. Organizationally, some people in place likely don't have the skill for making this type of transformation as they’re already top-heavy on maintaining things “as is.”
Being more than 10% innovative also extends to the way you hire. Instead of matching people to job descriptions, truly innovative companies factor in for a range of complexities that include culture, work style and many other pieces in addition to the individual’s skill set.
At Roy Talman & Associates, we help tech companies develop a hiring strategy that doesn’t merely identify the best people for current roles of need today but the best people to help lead a company through the potential changes and disruptors we foresee later on. Because the more people who are nimble and proactive in the face of impending change, the more likely a company can commit to innovation at much higher than a 10% level.