If there is one place where advanced analytics had the chance to come to life, it was Gartner’s 2014 BI and Analytics summit in Las Vegas.
Boy, technology moves fast. And machines can increasingly give you hints at where to look—so the needle in the data haystack is easier to find. Visualization is now so cool that I often found myself thinking, “Am I looking at this because it is insightful, or because it reminds me of a design extravaganza I saw somewhere?”
Perhaps it is the fatigue of doing Vegas for work too often, perhaps it was not the purpose of this event, but for all the excitement reminiscent of past dot-com eras, all the talk about the scarcity of data scientists and ever-improving technologies seemed to obfuscate a very fundamental issue—the analytics crowd doesn’t care that much about how to scale up the generation and use of analytics throughout the companies they want to serve. Or rather, they assume that the issue will go away by demo cratizing access to analytics tools for business users, and creating an “analytical culture.”
Not so fast. Although that will clearly help (a lot), I don’t think that senior management believes for a second that technology will work itself into becoming a driver of huge business impact without a significant organizational makeover. If anything, the C-suite understands that unless material innovation is deployed at scale, it doesn’t create material impact. And without understanding how to scale up analytics generation and use, there will NOT be material impact. This is not how many petabytes of data you can churn. It is about how people (yes, humans—at least until IBM’s Watson decides otherwise) create insight from the raw materials they have, and how those get that insight used throughout the enterprise.
That is where the Las Vegas crowd didn’t venture (with a few notable exceptions—Kimberly Harris-Ferrante, Carol Rozwell, and Roy Schulte), and that made the whole show feel a bit self-referential. Where were the non-IT senior leaders?
The next industrial revolution, with a twist
Here’s the crux: despite the significant hype about big data and analytics, a critical aspect is often neglected. “Data-to-insight” and “insight-to-action” are business processes. To generate material business impact, they require scale, as well as appropriate design, related to change management, incentives, and people accountability.
Given the process’s enterprise-wide nature, the data-to-action macro-process is the cornerstone of real enterprise performance management and needs the attention of the C-suite, which must consider “industrializing” (yes, that’s what it is) the process through operating models that are not limited to fabulously intelligent people and technology. Up to 30% of the analytical effort is related to heavy-lifting tasks that advanced operating models such as shared services or outsourcing can enable, and many analytics-related activities benefit from centralization and sharing of best practices, critical data, and IT assets. Finally, insight needs to be ingrained deeply into those business process steps that generate material impacts on the chosen business outcomes. That happens through looking at business outcomes, their drivers, and what part of the business processes that influences those drivers. That’s where you “inject” your insight at scale—and you choose the right metrics to unleash all that analytical firepower.
Does it sound like a movie we have seen elsewhere? You bet. Analytics are operations, and must be embedded into operations. That’s the part of the equation that didn’t show up in the Vegas algorithms.
The proof you’ve sat on this morning
A useful analogy can be derived by observing what’s happened in the automobile industry. (Read on, it is even useful conversation at dinner parties.) There are obvious differences between cars, even the best ones, built 20 years ago and those built today. Although they use very similar mechanical parts, today’s cars handle driving completely differently, because they sense and react to conditions in a granularly, timely, cost-effective, and scalable way. Today, the most innovative aspect of car engineering is not the design of the mechanical parts. Itis the technology engineering that embeds the insight from relentless analytical work, embedding millions of tests into the programmed reaction of key mechanical components—shock absorbers, gas throttle, steering wheel, brakes, and even tire pressure and over head augmented-reality displays. Statistical simulations drive our cars, literally, making them “intelligent.” Soon cars will be able to drive themselves. Moreover, interestingly, even the difference between older and newer cars is particularly striking on “difficult,” unpredictable, winding roads—where agility is necessary. It is not just a technology success but also an analytics break through that has been embedded—industrialized—to perfection. The same thinking can and should be applied to business processes, where insight deserves being embedded at scale.
Dot-com times no more
The net of this is that analytics (just like the Internet and ERP) is changing and will change the world. However, if left to the technophiles (this time accompanied by a crowd of statisticians), analytics will go through a very steep trough of disillusionment before emerging again as a material driver of economic change. Are we happy to wait five to seven years, like we did after the dot-com bubble burst, until the smoke dissipates and the (cheap-money-funded) irrational exuberance subsides? Idon’t think so, and with me are many of the leaders we talk to. That’s why through our research we provide a new angle for designing, transforming, and running analytics-heavy operations—indeed, for creating intelligent enterprise processes.
More to come on this—but if you are impatient and you don’t mind the deep dive, try a hot-off-the-press white paper Data-to-insight-to-action. You might find it a good counterpoint to the useful but oddly monochromatic Vegas show.