Unlike IT, where solutions are often obvious and widely adopted by enterprises worldwide, analytics processes are frequently unique and individualized. "Choosing the best analytical method is sometimes straightforward, sometimes art," Magestro says. "For example, looking for cause-effect relationships in data usually means some kind of regression, and looking for similar characteristics in large customer datasets likely involves clustering algorithms." When optimizing a marketing budget, an analytics expert can select from countless methods that can do the trick. "In such cases, it's often more important that a method is used properly and with good assumptions than whether it’s the 'best' method," Magestro says.
Experts differ on whether enterprise analytics initiatives should be centralized, either within IT or a standalone analytics department, or spread across individual business units. Many believe that IT is best positioned to serve as an analytics advocate and technology supporter, not as a base of all enterprise analytics initiatives. "There is no reason why data analytics should be siloed inside one department," Johnston says. "Rather, it’s a set of skills that should be encouraged to grow throughout the enterprise."
Since data science is a rapidly evolving field, there's a considerable advantage in having multiple teams collaborating and learning from each other, even if there is a bit of friendly competition among them, Johnston claims. "Different teams will do things in different ways, resulting in faster exploration of the entire field in order to better discover the kinds of methodologies most suited for one’s business environment," he notes. "Such cross-pollination of ideas can be further encouraged by rotating people in and out of different teams."
The analytics Center of Excellence (COE) model — a group or team that leads and coordinates analytics initiatives across the enterprise — has been discussed for many years, yet has found little support versus embedding analytics talent within business units, Honaman notes. "Within traditional IT, there is typically a centralized model for consolidating and managing operational data while providing access to that data to analytics resources within the business." Yet, with a few exceptions, this approach doesn't mesh well with the unique and specialized analytics needs of individual business units.
Magestro says that the case for centralizing analytics is strongest in two instances: when data or skills synergies are widely spread across different business functions, or when less mature business functions could benefit from the expertise a central team can provide. "We see cases where a central analytics team can be a temporary catalyst for growing functional analytics, in which case the central team might be best for highly specialized needs, such as machine learning or artificial intelligence," he says.
Regardless of how or where they originate within the enterprise, all analytics projects require strong and knowledgeable leadership. "The key is to have a good skipper at the helm," says Anirudh Ruhil, a professor of leadership and public affairs in the online Master of Public Administration program at Ohio University. "You also want the team leader to have a wealth of proven experience, because that is ultimately the gauge of a good analyst."
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