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The Data-Informed Institution

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Content elements: › How education is using data for digital transformation › The mission and business value of data › Data, adaptability, and agility › Agility for data - 6 steps › How can we use data to bring adaptability to our institution? › In closing › About the author 5. Upskill The next important element in extracting value from your data is to make sure you have employees or staff with the right skills ... in addition to a sense of curiosity. This is why data scientists are in such high demand today. Yes, there are plenty of tools available even for people with little skill or experience in statistics. But to really make the most of data, and to do so with rigor, it is important to have people with a good understanding of how to make correct inferences from data. For a simple example, those of us with less statistical experience tend to over-rely on averages, even when looking at an entire distribution of values can often lead to important insights. In one case I remember from my time as chief information officer (CIO) at US Citizenship and Immigration Services, we were looking to reduce the time it took us to process certain types of immigration applications. We created dashboards to track the average amount of processing time, but each change we tried seemed to have only a small impact on the metric. What we had missed was that the small number of applications that raised national security or fraud concerns took much longer to process, thereby skewing the average. We had no way to control how long those took. Although our improvements applied to the great majority of cases, because of the highly skewed average, we couldn't really see their impact. When we realized the problem and began monitoring, say, the 85th percentile completion time, we could identify the significant impact our changes had on the vast majority of cases. We had the data, the tools, and the access; we just lacked the skills to draw the correct inferences. Data-informed decisions can also be poorly founded when the data is presented (even unintentionally) in a misleading way. In his book The Visual Display of Quantitative Information, Edward Tufte shows how data can be distorted or obscured by the way it is presented 9 . Again, an organization that wants to be rigorous in its use of data must make sure that it has the right skills in analysis and presentation, as well as the data. 9. Tufte, Edward R., The Visual Display of Quantitative Information (Cheshire, CT: Graphics Press, 2015). 12

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