So Many Analysts, So Little Added Value !

December 25, 2021 by

“Through 2022, only 20% of analytic insights will deliver business outcomes.” 

In its shocking report, Gartner outlines that roughly 80% of analytics projects will fail to deliver business outcomes. What does this mean exactly ? Is this an indication that analytics are useless ? 

Vendor analysts, sales analysts, project analysts, data analysts, data scientists, economic analysts, operations analysts, business analysts, big data analysts, program analysts, and the list goes on. 

So many analysts whose main mission could be summed up as the examination of anything complex in order to understand its nature or to determine its essential features (source: merriam-webster). Yet, study after study show that analytics projects fail to deliver on this mission and render an added value to their businesses. 

We can think of so many elements that could contribute to this failure to meet expectations and deliver business outcomes. But I think one of the essential root causes of this failure is the adoption of a thinking mindset led by WHAT rather than a thinking mindset led by WHY.

When asked about their job, analysts will tend to focus on WHAT they do: designing dashboards, analyzing data, drafting metrics, kpis and measurements plans; few of them focus on WHY they’re doing what they’re doing. 

Overtime, this “What-led” mindset might induce the analyst to adopt a set of conducts that may lead to high analytical pitfalls (such as: analytical technical debts, functional gaps, capability gaps). For example:   

  • Rushing to delivery instead of requirements gathering and problem understanding
  • Not leveraging existing insights (through internal organizational knowledge, researching the topic of analytics, and learning from past use cases ) to detect and factor in avoidable misapprehensions
  • Not knowing how to measure success or evaluate the results of analyses (could also manifest in wanting to measure everything) 
  • Not prioritizing: not knowing what first to deliver, what has greater value in the context at hand, not being able to invest time efficiently…etc
  • Not engaging with other experts in the organization (an insider’s perspective can be very crucial in understanding the problem, prioritizing, evaluating, and implementing solutions)
  • Reacting instead of proactively acting: delivering ad hoc analytics versus proactively mapping a roadmap for an analytics project and its results (can also lead to positioning the analytics team as task executors rather than transformers and value driven)

How to ask WHY ?

Learning to ask why is unfortunately not a straightforward learning curve. It requires thinking critically, practicing, and learning to investigate, among other skills. But I would recommend using the sources below to start !

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