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The people dilemma of analytics
Using shared services to improve acceptance
Why are advancements in data analytics leapfrogging the organizational capacity to accept the insights found in the data and to act to realize the benefits?
How are organizations approaching analytics today?
When it comes to building analytics capabilities, organizations essentially have two options:
- Using analytics to help solve known problems typically within a given function
- Using analytics to identify issues that may have been previously unframed
In the first model, demand is generated by people who have already formulated a hypothesis and are looking for technical help to provide the answer. Examples include using price-elasticity modeling to improve product margins or examining purchase behaviors and patterns to identify fraudulent credit card transactions. In solving known problems, data analytics generally supports the current organization’s roles and responsibilities, and the path from analysis to acceptance to action is fairly simple.
The second model has yet to be widely embraced. For instance, predictive analytics can help organizations gain insights into how customers make buying decisions, which can impact distribution channels, social media actions, advertising, product design, pricing, etc. Herein lies the dilemma: there are often organizational barriers or potential penalties associated with embracing the insights. Sometimes these obstacles are straightforward in that a single function or leader has the power to make a change (e.g., shifting advertising spend from broadcast to web), but he bears the risk of failure and of being penalized if something goes wrong. For example, if the analysis was flawed, most organizations would hold the advertising director responsible, not the statistician. In other cases, effecting change requires a collective effort crossing many organizational silos, again with each leader having her own perceptions of risk, planning processes, and individual goals and objectives.
In our experience, when analytics serves to assist a single function in addressing a distinct challenge, adoption is high but value is low. When analytics transcends traditional functional walls and provides answers to "unasked" and more complex questions, the opposite is true.
Why aren’t more organizations pursuing the higher value proposition of using analytics?
Many organizations haven’t figured out how to get the permission to challenge the conventional wisdom. At present, there are many analytics groups—often within a shared services organization—that are using data analytics to provide insight, but in many cases, these groups are responding to—or soliciting—specific, focused questions. They’re "answering the mail," so to speak.
When an analytics group goes beyond "answering the mail" to look for ways to proactively add value, they often encounter difficulties with gaining acceptance and taking action. After all, an analytics group probably won’t get a good response if they call a functional leader and say, "These are the things we found you’re doing wrong." Many organizations earnestly believe that analytics can deliver great value by going above and beyond functional walls to illuminate previously unseen improvement opportunities. However, they struggle with embedding analytics into existing roles and responsibilities, determining how to organize their delivery capabilities, and getting business leaders to embrace what could potentially be an uncomfortable set of conversations. Unfortunately, the perceived risks to the status quo and to people’s positions can hinder the organization from taking action on the findings and from realizing greater value. Why does such reticence exist? The crux of the matter lies in the innate objectivity of data.
At the very core of analytics is what is often referred to as the "cold hard facts," the un-emotional, dispassionate, bits and bytes of data. When organizations attempt to replace people’s personal knowledge and experience with insights derived from data analytics, they inadvertently suggest shifting "power" from managers who are responsible for delivering results to statisticians and mathematicians who have no bottom-line accountability. In the worse case scenario, if the "cold hard facts" run counter to experience or fail to deliver the promised results, the reputational damage may gravely impair the organization’s ability to embrace analytics.
"The availability of data and the technology needed to mine it have put analytics within reach of almost every organization."
How can an organization overcome resistance to change?
The way an organization delivers its analytics capabilities plays a big role in facilitating this transformation. If the organization is focused on "answering the mail," it’s not so important where the analytics group sits, since the capability can be organized around the users who are requesting that information. However, this is not the case when the organization wants to use analytics to look above and beyond the functional walls to call out enterprise-wide issues that nobody owns. Here, an end-to-end process perspective is paramount, so a proactive approach to analytics could fall within the purview of a shared services center of excellence (COE) or a global business services (GBS) organization. Because of its strong relationships with business leaders and its charter to facilitate greater alignment with enterprise goals, this type of organization would likely have more permission to suggest ways to improve, even if the problems and potential solutions counter conventional wisdom. While analytics delivered through a global shared services construct isn’t a panacea for all of the challenges associated with gaining business buy-in, it may provide a practical path for moving from acceptance to action.
In addition to considering shared services as the delivery platform for data analytics, three simple guiding principles may help improve alignment between those responsible for finding answers and those responsible for taking action:
- Accept that analytics can be a threat to those who must embrace it. Engage with decision-makers and leverage their experience and knowledge to help produce more valuable insights versus scanning data and generating "fact-based" conclusions.
- Regardless of where analytics ultimately sits within your organization, consider utilizing either Global Process Owners or GBS liaisons as the bridge between the business units/functions and the data analytics team. They can leverage their knowledge of the organization and end-to-end processes to help generate ideas, improve outcomes, and more importantly, provide guidance on how to support their customers as opposed to inadvertently embarrassing them.
- Consider the long-term impact both upon the entire organization and upon individual roles as the focus of power shifts from experience and knowledge to data and facts. This has the potential to invert the typical hierarchical structure, essentially turning the organization upside down, where younger personnel drive decisions and more-experienced employees implement the insight.