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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 4. Provide tools A data-informed organization makes a range of analytic tools quickly available to its staff, often through a self-provisioning model, as described previously. A wide variety of software and services are available. If you want to perform traditionally structured queries against the data, for example, you can set up a data warehouse based on the data in the data lake, or you can provision a tool that lets you do old- school, SQL-type queries directly against the data lake. But today, there are many more possibilities. You can, for example, visualize your data with modeling tools, and you can construct scenarios and ascertain their consequences. Today's analytics revolution is all about artificial intelligence (AI) and ML, which opens up new possibilities for what we can do with our data: predict outcomes, spot anomalies, categorize data, analyze sentiment, discover patterns, guide robots, and much more. In addition to broadening the uses of data, today's technologies can dramatically reduce the time to science. For example, at the University of Adelaide in Australia, researchers needed to analyze 48 wheat exomes and 18 whole barley genomes—some three terabytes of data. To get the needed capacity, they worked with AWS partner RONIN to create a high-performance computing (HPC) cluster in the cloud. According to Dr. Nathan Watson-Haigh, a research fellow in bioinformatics, the wheat exome analysis, which would otherwise have taken them two weeks to complete, required only six hours in the cloud. Education Perfect (EP), a New Zealand EdTech, uses AWS tools such as ML and rich data analytics to help instructors track student growth between pre-test and post-test periods and provide adaptive learning capabilities with real-time feedback, tailored pathways, and targeted resources. In the United Kingdom, Oxford University's Ashmolean Museum is making its treasures available to the entire world, in the process digitizing and cataloging 300,000 ancient Roman coins. Outside of higher education, you might have seen Sky News, in their coverage of Britain's royal wedding, using AWS ML to recognize the faces of celebrities in the crowd and identify them for the TV audience, or Formula 1, Major League Baseball, and BUNDESLIGA using ML to enhance the viewer's experience. These are powerful tools that open up many possibilities for innovation in education. To apply ML, you train a model based on earlier data sets and then apply it to new data as it is observed. In AWS, there are three general approaches to machine learning: (1) use a model such as Amazon Rekognition, which has already been trained to recognize objects in images, or Amazon Lex, which has been trained to understand intentions expressed in natural language (Rekognition, for example, was used by a number of institutions to support remote proctoring), (2) train and apply your own model based on any one of the common algorithms used for ML, using Amazon SageMaker, or (3) use your own algorithms and training approaches, if you have employees skilled in ML, by working directly with AWS services optimized for ML. With tools such as these, institutions can unleash the creativity of their faculty and staff and find new ways to put data to use. 11

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