What makes us trust analytics, and how to argue.

1. Prior experience → More trust
In Trustworthy Data Analysis, Roger Peng gives an elegant description of how he evaluates analytics presentations, and what factors influence his trust level. First, he imagines analytical work in three buckets: A (the material presented), B (work done but not presented), and C (analytical work not done).

“We can only observe A and B and need to speculate about C. The times when I most trust an analysis is when I believe that the C component is relatively small, and is essentially orthogonal to the other components of the equation (A and B). In other words, were one to actually do the things in the ‘Not Done’ bucket, they would have no influence on the overall results of the analysis.”

Peng candidly explains that his response “tends to differ based on who is presenting and my confidence in their ability to execute a good analysis…. If the presenter is someone I trust and have confidence in, then seeing A and part of B may be sufficient and we will likely focus just on the contents in A. In part, this requires my trust in their judgment in deciding what are the relevant aspects to present.”

Is this bias? When our assessment differs based on who is presenting, Peng acknowledges this perhaps reflects “all kinds of inappropriate biases.” Familiarity with a trusted presenter helps decision makers communicate with them efficiently (speaking the same language, etc.). Which could, of course, erupt into champion bias. But rightly or wrongly, the presenter‘s track record is going to be a factor. And this can work both ways: Most of us have likely presented (or witnessed) a flawed finding, which causes someone to lose credibility – and winning that credibility back is rather difficult. Thanks to Davd Napoli (@biff_bruise).

Almost every retail data point has its own report, creating oceans of paperwork…. Prescriptive analytics eliminates that hassle by interpreting the reports.

2. Predictive analytics → Quick action
Guy Yehiav explains how predictive analytics can benefit retailers. A must-have capability is recommending specific actions in simple, plain-text messages – rather than dumping analytics reports on people. Retailers can improve margins with quick, data-driven actions.

3. Fight right → Better solutions
“All teams that are trying to address complex issues have to find ways to employ all of their diverse, even conflicting contributions. But how do you fight in a way that is productive? There are tactics your team can employ to encourage fair — and useful — fighting.” strategy+business tells us Why teams should argue: Strong teams include diverse perspectives, and healthy working relationships and successful outcomes hinge on honest communication.” One tactic is to “sit patiently with the reality of our differences, without insisting that they be resolved”. (Ed. note: Management advice and marital advice are beginning to sound the same.)

Posted by Tracy Allison Altman on 

Events Calendar

Bringing the funny to tech talks: Explaining complex things with humor. Denver, August 13, 2018 – no charge. Meetup by PitchLab, Domain Driven Design, and Papers We Love – Denver.

Using evidence for smarter decision-making. London, June 27, 2018 – no charge.

Data Visualization Summit, San Francisco, April 10-11, 2019. Topics will include The Impact of Data Viz on Decision Making.

Decision Analysis Affinity Group (DAAG) annual conference, Denver Colorado, March 5-8, 2019.

Photo credit: CloudVisual on Unsplash

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