RESEARCH & RESOURCES

Utilities Uncertain about Big Data Analytics, IDC Reports

Big data analytics should be a slam dunk for utility companies, which manage networks of devices that continuously generate streams of data. A new report from IDC reveals that utilities are uncertain about what they're doing or why they're doing it.

One can't-miss hotbed for big data adoption should be the utility industry. After all, they manage widely distributed networks of "intelligent" devices, many of which are equipped with telemetry sensors, radio frequency identification (RFID) tags, and remote management and administration facilities. These devices continuously generate -- and in some cases consume -- big data, so they must be good candidates for big data analytics, right?

Yes and no, says market watcher International Data Corp. (IDC), which published a recent study (The Maturity of Analytics Strategies in the Utility Industry) on the issue. According to IDC, utilities say they are undertaking big-data-oriented initiatives or pilot projects. There's a problem, however: more than a few say they're uncertain about what they're doing or why they're doing it.

"Conversations with utilities continue to indicate a significant amount of uncertainty about the drivers for big data analytics, key success factors, and how to leverage internal expertise, [as well as] how to direct an initiative," IDC says. "While a rapidly growing volume of incoming data is certainly a strong motivator for some utilities, other utilities are simply reacting to market hype, and jumping on what they perceive as a bandwagon."

In this regard, at least, they aren't much different from enterprises everywhere.

In the utility vertical, early adopters of big data analytic technologies tend to be proactive. IDC notes that some are already partnering with consultants or integrators, both to help them develop big data analytic programs and to identify potential applications and cultivate expertise. Fence-sitters seem quite content to wait until the dust settles. This is the case in other verticals, too.

IDC notes that fence-sitters "are intentionally delaying the [big data analytic] technology component pending progress in other dimensions of their analytics maturity." This, too, isn't unusual: many organizations don't yet have their decision-support houses in order. From this perspective, big data analytics -- with its new (and largely unfamiliar) skill requirements, its tolerance of (and preference for, in some cases) semi-structured or multi-structured data-types, and its far-from-commoditized technology stack -- could well be seen as a distraction.

A common best practice in any emerging technology area is to identify a limited number of applications -- e.g., low-hanging fruit, such as a Hadoop-based data landing zone, or clearly defined projects (perhaps with more complex requirements) that could deliver high-value returns quickly -- and to focus on developing these. IDC says this is just what most big data adopters in the utility industry are doing, too.

In any emerging technology area, you'll also see a select few aggressive adopters: companies that go more-in, if not all-in, by yoking important projects or even strategic initiatives to the new technology architecture. According to IDC, a select few utilities are doing this, too.

Similarly, another time-tested best practice is to study adoption use cases or case studies in other industries -- particularly those that have been at the forefront of technology adoption. Ditto for utilities and big data, which IDC says would do well to look to the telco vertical: "Utility industry executives who would like to find relevant analytics case studies in another vertical context should look at the telecommunications industry, which in addition to presenting structural and business similarities to the utility industry has some worthy examples of analytics leadership."

The IDC report doesn't pack terribly much in the way of surprises. Consider its findings regarding the role of strong leadership, which it suggests "is almost always beneficial" in making or breaking the success of a big data analytic initiative. As most data management (DM) practitioners know, strength or quality of leadership is always a big factor in any project's success. Most DM practitioners would likely agree with IDC's caveat to this finding, as well: namely, that while strong leadership might be beneficial, it doesn't always deliver immediate and definitive results. In fact, the market watcher concludes, "research shows that this often requires patience and perseverance."

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