Page 57 - The Real Work Of Data Science Turning Data Into Information, Better Decisions, And Stronger Organizations by Ron S. Kenett, Thomas C. Redman (z-lib.org)_Neat

44 The Real Work of Data Science

Uncertainty on which the
decision-makers must
depend on “soft data”
and/or their intuition =
(say) 52%

Total uncertainty in
decision = 100%

Uncertainty removed by hard
data = (say) 48%

Figure 10.1 All decisions are made in the face of uncertainty. The spirit of “data‐driven” involves
reducing that uncertainty in the future.

This thinking is especially important for data scientists – as we’ve argued throughout,
much of their real work involves helping people make better decisions. While advancing
any single individual’s and/or the organization’s decision‐making capability is beyond a
data scientist or CAO’s usual remit, it is clearly in their interest. We urge data scientists and
CAOs to take it on.
We recognize that this thinking is maddeningly abstract! But over the years, we’ve had the
good fortune to work with plenty of individual decision‐makers and groups, some terrific and
some simply awful. From that work, we’ve distilled 12 “traits of the data‐driven” (Redman,
2013b) and six traits of the “anti‐datas” (Redman 2013c). You can use these to baseline your
organization’s capabilities and identify strengths and weaknesses. In the short term, use them
to help ensure your results and recommendations are listened to. And look to build greater
capability in decision‐makers, in the longer term.
Traits of the Data‐driven

The data‐driven:

• bring as much diverse data and as many diverse viewpoints to any situation as they
possibly can;
• use data to develop a deeper understanding of the business context and the problem at hand;
• develop an appreciation for variation, both in the data and in the overall business;
• deal reasonably well with uncertainty, which means they recognize that they will make
mistakes;
• integrate their understandings of the data and its implications with their intuitions;
• recognize the importance of high‐quality data and invest in trusted sources and in making
improvements;