Advanced Analytics: Two Big Things You Can’t Afford to Get Wrong

Posted by Jay Milligan, CEO KPISOFT-Americas on Feb 14, 2019 10:07:15 PM
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shutterstock_141931021Advanced Analytics: Two Big Things You Can’t Afford to Get Wrong

These days analytics is top of mind in every C-suite and board room. You’d be hard-pressed to find a business leader or director today who isn’t actively searching for ways to leverage big data to improve actionable decision-making. On the flip side, you also wouldn’t have to look far to find a CEO who is at least slightly discouraged—if not altogether disillusioned—by an analytics program that is failing to live up to expectations.

Many analytics pilots stop well short of delivering the impressive transformation leaders are hoping for. A recent survey by McKinsey found that only 8 percent of respondents with analytics initiatives had engaged in effective scaling practices.[1]

There’s no shortage of opinions out there about what causes analytics programs to fail, but we’ve boiled them down to what we believe are the two most common:

1 - Vision: Cloudy with a Chance of Failure

The most successful analytics programs are born from a specific need and designed with the end goal in mind. McKinsey notes that often management “struggles to define valuable problems for the analytics team to solve.”[2] Gallup adds that programs are likely to fail when “organizations approach analytics as a technology issue… without thinking through the problems they want to solve and how they will use the insights they gather from the data.”[3] Deloitte also agrees that the biggest difference between a successful AI project and an unsuccessful one is whether it is driven by technology or by business need.[4] 

Key takeaway: Don’t put the technology cart before the horse. Clearly defining business needs and building an advanced analytics program around those needs—rather than throwing technology against the wall and waiting to see what sticks—will help set up your organization for success.

2 - The Right Team: A Winning Playbook Isn’t Enough

Defining a clear vision and strategy is critical for helping an advanced analytics program start off on the right foot, but it isn’t enough on its own. According to McKinsey, “Few executives can describe in detail what analytics talent their organizations have, let alone where that talent is located, how it’s organized, and whether they have the right skills and titles.”[5]

Key takeaway: Identify, invest in, and equip the right team.Gallup suggests that savvy resources—those who “understand how to use data appropriately and appreciate its limitations”—should be distributed throughout the corporate hierarchy: top leaders, analytic talent, decision-makers, and managers.[6] McKinsey echoes this, recommending a hybrid organization model in which “agile teams combine talented professionals from both the business side and the analytics side,”[7] and additionally champions the concept of an analytics translator[8], “someone on the business side who can help leaders identify high-impact analytics use cases and then ‘translate’ the business needs to… experts so they can build an actionable analytics solution.”[9] Simply stated, companies should consider having a “story teller” - one who has the ability to contextually weave a story from the data and insights generated specifically to the desired audience.

Regardless of the specific talent solution that makes the most sense for your organization, the overarching message is clear: Finding and distributing the right talent—whether that means identifying resources within your organization or bringing new people on board—is key.

Obviously there are many more components that comprise the most valuable (and scalable) advanced analytics programs but getting these two right will help give you the greatest chance for success. Want to get started on your journey?  We’re here and listening. 

 

[1]Oliver Fleming, Tim Fountaine, Nicolaus Henke, and Tamim Saleh, “Ten red flags signaling your analytics program will fail,” McKinsey.com, May 2018. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ten-red-flags-signaling-your-analytics-program-will-fail

[2]Oliver Fleming, Tim Fountaine, Nicolaus Henke, and Tamim Saleh, “Ten red flags signaling your analytics program will fail,” McKinsey.com, May 2018. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ten-red-flags-signaling-your-analytics-program-will-fail

[3]Bill Petti and Sean Williams, “5 reasons why your company’s analytics program is failing,” Gallup.com, January 15, 2015. https://www.gallup.com/analytics/237725/reasons-why-company-analytics-program-failing.aspx

[4]“8 secrets of successful AI programs,” Clusterdata.nl, December 11, 2018. https://clusterdata.nl/bericht/news-item/8-secrets-of-successful-ai-projects/

[5]Oliver Fleming, Tim Fountaine, Nicolaus Henke, and Tamim Saleh, “Ten red flags signaling your analytics program will fail,” McKinsey.com, May 2018. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ten-red-flags-signaling-your-analytics-program-will-fail

[6]Bill Petti and Sean Williams, “5 reasons why your company’s analytics program is failing,” Gallup.com, January 15, 2015. https://www.gallup.com/analytics/237725/reasons-why-company-analytics-program-failing.aspx

[7]Oliver Fleming, Tim Fountaine, Nicolaus Henke, and Tamim Saleh, “Ten red flags signaling your analytics program will fail,” McKinsey.com, May 2018. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ten-red-flags-signaling-your-analytics-program-will-fail

[8]Nicolaus Henke, Jordan Levine, and Paul McInerney, “Analytics translator: The new must-have role,” Harvard Business Review, February 2018. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/analytics-translator

[9]Oliver Fleming, Tim Fountaine, Nicolaus Henke, and Tamim Saleh, “Ten red flags signaling your analytics program will fail,” McKinsey.com, May 2018. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ten-red-flags-signaling-your-analytics-program-will-fail

 

 

Topics: augmented analytics, business analytics, artificial intelligence, AI, data, analytics, innovation, business intelligence, machine learning, business objectives, customer experience, talent management, board of directors

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