You can learn an amazing range of things from government data, particularly if you push it through a model. I’ve been slogging through that process for decades now, but for the last 6 months I’ve been focusing on the question of just what vertical markets are doing with IT and networking. The raw government data is interesting in itself, but to get the most from it, you need to add a bit of interpretation, which I’ve done using my market model. The question I’ve been trying to answer is whether “the market” can be defined in terms of behavior, or whether it’s so fragmented in behavior that the concept of an overall market is misleading, even useless.
My process looks at 62 vertical markets, and for this blog I’m focusing on US market data where I have the best access, best model/survey data, and most familiarity. One thing that immediately stands out in the assessment is the sharp polarization of verticals in terms of one critical metric, centralization of IT. Only six verticals rise to the level of “centralized” (a score of 50 or more) and 47 are “decentralized”, meaning they score less than 10.
Five of the six centralized verticals relate to the financial industry, and of the nine “partially centralized” verticals, seven are transportation verticals. In all, of the 15 verticals that are at least partially centralized, six are financial and seven transportation, so 87% of the verticals where centralized IT is a strong factor in IT overall are in just two broad segments of the market. These are the companies where data centers are highly important, and where centralized IT planning and management tend to occur. They are also the companies where there is a narrow set of “core business” applications that dominate everything. Keep these points in mind as we talk about the future.
Let’s look at another measure, which is the dependence of the company on “network spending”, which is largely a measure of the influence of private/VPN networks on their IT spending. Six transportation verticals are in the “dependent” range (a score of over 10) out of fifteen companies, but no finance companies. This correlates with a shift of companies with a retail-something focus to use of the Internet to reach customers and partners, since Internet spending isn’t included in this metric.
Internet growth and dependence is universally high in retail verticals and in other verticals (finance, for example) that rely on online customer relationships. In fact, all of the “dependent” companies (score over 35) fit those criteria. Public cloud data, which I have to get from my own contacts since it’s not reliably reported to government agencies, correlates with this same group of companies, which make up only 18 of the 62 verticals in my list.
Moving back to IT, we can see some validation of this retail vision of the cloud. Changes in IT direction are made most easily where there’s a change in budgeting, as indicated by spending growth. In the nine companies with “strong” growth in server spending, a score of 14 or over, we see two transportation verticals and one financial, both of which are verticals with limited retail exposure. There is only one retail vertical, accommodations, in that “strong” growth in server spending zone. Generally, retail firms that relied on storefronts had more distributed computing, and my own contacts suggest that this group of businesses were unwilling to risk centralized or cloud operations for their locations, for fear of a network outage that would suspend their ability to do business.
There is strong indication in my own surveys that companies who have difficulty acquiring and retaining skilled technology expertise are more likely to adopt cloud computing. One indicator of that situation is the growth in spending on integration services. There are two distinct sub-groups represented among the top twelve verticals in this group. The first are companies with overall high unit values of labor, such as investment firms and management consulting firms, who also offer higher-than-average salaries to IT personnel, and the other are firms with concentrated, specialized high-value employees but a larger overall labor base, who tend to spend less on IT people. Apparently, companies with a majority of skilled people are prepared to go outside to obtain technical skills when needed, and also to adopt cloud services to respond tactically to opportunities or risks. Companies with a small number of specialist-skilled people, like mining, manufacturing, petro-chemical, etc., also spend less on IT personnel on the average, and seek integration stills because they lack in-house resources. These are also candidates for cloud computing.
Another insight we can gather from the data relates to IoT. If we look at five-year network spending trends in the 62 verticals, we find that four of the five verticals with “strong” five-year network spending growth (scores of 10 or higher) were manufacturing, mining, and other verticals whose network usage was influenced by collecting telemetry from remote operations (the fifth was a financial vertical). IoT impact, not surprisingly, was largest in manufacturing and transportation. Interestingly, these same verticals topped the spending growth chart for personal computing, and remote personal computer activity was responsible for much of the network spending increases outside manufacturing/transportation.
All these, I think, are interesting insights, but perhaps the most interesting insight of all is that the differences in IT and network spending, centralization, and growth are enormous across these 62 verticals. The ratios on the spending side are anywhere from 10:1 to 50:1, and in spending growth in IT there are some verticals that show negative growth. In network spending, half of the total verticals show negative spending growth. The conclusion? That it’s meaningless to talk about “the market” in broad terms. The business service potential for the cloud, for IoT, for 5G, and for everything else will depend on what verticals we’re talking about.
That even applies to geography. Vertical markets aren’t evenly distributed, even nationally. Get down to the state, and even more to the zipcode, and you’re going to see massive differences in potential because of massive differences in how the verticals map to those geographic boundaries.
We can’t let ourselves get carried away with this, though. Just as there are enormous differences among verticals, there are enormous differences within them. Firm size is a factor that creates variations within verticals, and so is the concept of “focus”. Is a manufacturing company focused on a single product class or are they broadly diversified? Is a financial company a consumer retail broker or a federal reserve bank? I use a 62-vertical segmentation to address some of this, but even within one of those tighter verticals, there are differences.
Everyone knows the old joke about people trying to identify an elephant behind a screen; they reach in, and depending on what they happen to touch, they think it’s a snake or a tree or a cliff. We may be doing this very thing talking about markets in networking and IT. The data, to me, shows that the differences among verticals are so profound that we can’t assume that a trend that appears in one place is really a market trend, or just a local phenomena. It also shows that sales/marketing processes, to be optimal, will have to look at the market differently.