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 Culture and process change Becoming data-informed, in this sense, requires a different way of making decisions; it is a deep cultural change for many organizations. In the past, we might have made decisions by crafting detailed plans, analyzing options with the available data, and choosing the option that—given only the available data—appears to deliver the best outcomes. In the digital world, we refuse to accept only the data that is available at the instant the plan is created. Instead, we design experiments to yield additional data and then incorporate that data into our decision-making. We resolve uncertainty by capturing new data. An example is the technique for IT governance that we devised when I was the CIO of US Citizenship and Immigration Services (USCIS). Instead of writing a large requirements document and handing it over to the technologists for implementation, we simply handed over a business objective. In one case, for example, we noticed that a skilled case processor (a "status verifier") could process about 70 cases a day, and our business objective was to make that number much higher. In another business case, we found that a number of paper files got lost in transit as we moved them between processing locations, and we wanted to eliminate those losses. For each of these objectives, we began by creating a dashboard that showed the key metric: the number of cases per day or the number of files that were missing. Instead of writing a requirements document, we created a cross-functional team of business operators and IT technologists, and we charged them with improving the metric. We gave them the tools to make changes to IT systems and business processes quickly and then monitored the dashboards with them. They tried small, incremental changes and monitored the results every day. Based on what they saw in the data, they could decide what to do next to maximize the outcome. And management could decide whether to continue funding the initiative or direct the funds elsewhere. The result was a data-driven, reduced-risk, lightweight governance process that delivered value quickly. This leads to another important point: accountability is enhanced by transparency. By making data widely available, we made the team's progress visible. As a result, oversight bodies could constantly revisit the investment decision, either investing more or less, redefining objectives, or stopping the investment entirely. Results were the only gauge of success, and results could be achieved quickly. But those results had to be supported by the data. 16

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