#worthassigning: Daniel Waisberg on getting and presenting insights from data

More and more writing and other courses require students to do original research, to work with data they generate or find. For example, on October 7, TechRhet, an e-mail discussion list for writing teachers who focus on the nexus of writing and technology in their pedagogy, had a query from a colleague about assigning an infographic in first year composition courses.

It's with that kind of assignment, or assignments where research projects ask students to develop and/or work this data that this resource might be one #worthassigning. "From Data to Insights: The Blueprint for Your Business," by Daniel Waisberg, an analytics advocate at Google, looks at two processes: one on defining data and the other on presenting data, two processes that inform one another.

While Waisberg's title addresses the use of data to guide business decisions, the piece is really about using data for finding insights, insights that can lead to action. So where writing assignments invite students to use data to offer insight, to motivate people to one action or another, the advice by Waisberg will serve well.

Here's an excerpt that gives you a sense of how well this piece can serve any course or project where data plays a role, including for academics doing their own research, whether for scholarship, service or teaching purposes:

Defining the data
Gaining successful insights means figuring out what you want from your data—finding its value. Consider what you want to do with the actual data. In Thinking with Data, Max Shron offers a helpful framework for narrowing the scope of a project such as data analysis. Similar to a story, a project will always include exposition (the context), some conflict (the need), a resolution (the vision) and, hopefully, a happily-ever-after ending (the outcome).

Answering the following questions will help illuminate the best plan for using your data.

• Context: What are you trying to achieve? Who is invested in the project’s results? Are there any larger goals or deadlines that can help prioritize the project?
• Need: What specific needs could be addressed by intelligently using data? What will this project accomplish that was impossible before?
• Vision: What will meeting the need with data look like? Is it possible to mock up the final result? What is the logic behind the solution?
• Outcome: How and by whom will the result be used and integrated into the company? How will the success of the project be measured?

 As you can see, Waisberg makes recognizable textbook moves -- linking to and citing an authority, offering a heuristic for planning, one that maps easily onto the kinds of questions we ask students to consider about audience, purpose, and context.

The other value to Waisberg's piece for faculty is that he provides a framework and vocabulary for discussing with students data planning, gathering and visualizing/presenting. Waisberg draws inspiration for his steps to follow, questions to consider from "Michael Graves, emeritus professor of architecture at Princeton." Graves, reports Waisberg, sees architectural drawing "as much a process as it is an end product, and that while computers have their place, so does the human/emotional element. He deconstructs architectural drawing into three types: the 'referential sketch,' the 'preparatory study' and the 'definitive drawing.'"

I'll leave it to you to visit Waisberg's piece -- http://bit.ly/10UKDhg -- to see how well and how usefully he maps Graves's insights into practical steps students can be taught to follow and practical questions they can be taught to ask about their own and peers' projects.

You'll find it useful, and if, you're doing work on your own with data or teaching students how to work with data, a link #worthassigning.