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Crossing the Chasm Between Data Science and Impact

Deep analysis is only half the job. Win trust with decision makers and inspire actions to deliver impact.

Most analysts have been part of failed projects or have encountered the dreaded “I am not sure I understand it...” followed by an awkward, unspoken “and, I can’t do anything with it!”

Research reports say that many (nearly 87%) analytics projects fail. Several reasons have been cited, such as organizational structure, culture, talent, data availability, etc.

However, translating insights into trustworthy recommendations and inspiring actions is a big stumbling block for many analysts. Often stakeholders aren’t able to quickly grasp the analysis and therefore develop misgivings and abandon ship without taking any actions.

It appears that the last mile poses a significant gap!

The role of “Analytical translator” has been proposed to help structure problems and translate insights. However, that proposal would move the analyst one step further away from the context, causing other complications.

I posit that with a bit of attention, planning, and focused effort, data scientists can begin bridging this gap.

So, What causes this last mile gap?

Insufficient time for insight generation/ storytelling
Insight generation is an overlooked step in the analytical process, as we allocate a disproportionately small amount of time to building insights and storytelling. Instead, we feel an onus to spend most of our time digging through the data and expect that a few charts pasted onto slides will communicate the story.

This last-minute effort is a key failure point, where data scientists struggle to communicate complex analysis and insufficiently bridge the analyses to the objectives or recommendations, which causes a failure to win trust with decision makers.

Complexity tradeoff
As data scientists, we are tasked with finding insights or building an accurate prediction model. To that end, we might dig deep and chase down a complex train of thought or apply an ensemble of algorithms to achieve the desired outcome. However, the more complex the analyses, the harder they are to explain, especially to an audience less experienced with data science and algorithms.

A lack of shared context
Shared context and motivations with business folks is critical to creating hypotheses, identifying model features, and ultimately connecting the dots to meaningful recommendations. Often, analysts and stakeholders spend little time sharing context and walk away with different visions for the project. An analyst might not know what to ask for, and the stakeholder may not realize there is insufficient prior knowledge. Ultimately, the effort ends up surfacing something rudimentary or missing the mark completely.

How do we overcome the gap?

Allocate sufficient time
I propose that analysts budget at least 50% of time in the project plan for generating insights and structuring a narrative. The shift to telling the story, rather than continuing to dig the data, will also serve as a check against analysis paralysis, bring focus back to the exam question (reduce scope creep), and at the same time enable the analyst to address any potential holes or follow up questions that might come up.

Iterate and polish
The process of creating meaningful recommendations and building a crisp narrative is iterative. It can at times be frustrating to continually edit slides, but with the right feedback and direction, the effort pays off. With each iteration, you are looking to address any insight gaps and improve the effectiveness in conveying the insight and recommendations.

It helps to identify the key decision makers and tailor data-based recommendations to their style and appetite. You may want to anticipate potential follow-up questions they might ask and preemptively address them. Improvements you might make include verbiage, content flow, imagery and even things like font, colors etc. You want to eliminate any distractions to the key message such as ambiguous statements or erroneous facts that can derail the presentation. The higher the stakes, the more refinement you might want to make to the overall presentation. It isn’t uncommon to go through dozens of iterations to get a polished final product, ready for a CXO presentation.

Find a sparring partner
It is extremely useful to find a trustworthy sparring partner — someone that has tenure in the organization or has expertise in the business area you are analyzing. Most importantly, this partner will lend a critical eye to your work and provide direct feedback on considerations such as whether your insights are new and impactful; whether your recommendations are meaningful and feasible; and whether the presentation communicates effectively. You also want their counsel on how best to win the trust of key decision makers and potential areas that can derail the effort.

Invest in building context
Context is acquired over time and every project serves to bring more. It can be intimidating for an analyst to seek to understand the inner workings of a product or a business. However, without it, your ability to answer the question gets compromised. It is okay to be vulnerable and ask for context around the underlying business, the motivations for the project, and any potential hypotheses. Request any prior studies that might exist and build time for exploratory analyses to understand key business drivers. Most importantly, foster relationships to continue building context over time.

Continuous improvement
As with anything, effective communication is an area for continuous focus. After each presentation, reflect on the outcomes and identify areas for improvement to make future reports more impactful. However, the key step is to acknowledge that it takes focus and effort to bridge the last-mile gap. Once you have identified the gap, it is a matter of time before we cross the chasm safely.


  1. M.Henrion, Why most big data analytics projects fail (2019), ORMS today
  2. C.McShea, D.Oakley, C.Mazzei, The Reason So Many Analytics Efforts Fall Short (2016), Harvard Business Review
  3. T.Davenport, Keeping up with your quants (2013), Harvard Business Review
  4. N.Henke, J.Levine, P.McInerney, Analytics Translator: The new must-have role (2018), McKinsey