On August 17, 2014, the New York Times published an article titled, “For Big-Data Scientists, ‘Janitor Work’ Is Key Hurdle to Insights.” The article discusses the struggles facing several startups that offer products and services related to big data analysis. The article’s title refers to the critical and often unseen process of cleaning data to improve accuracy and usefulness. “Data scientists,” according to the article, “spend from 50 percent to 80 percent of their time mired in this more mundane labor of collecting and preparing unruly digital data, before it can be explored for useful nuggets.”
Schoolzilla is not in the “big data” business. Big data is all about aggregate trends. While of course Schoolzilla can do that too, we like to think of Schoolzilla as helping educators solve smaller and often more important data problems, such as highlighting challenges and achievements for educators, administrators, and family members (whether at the individual, group, classroom, teacher, grade, district, subgroup or district level, among other things). Having said that, we believe that consistent data quality and accuracy is among our most important early contributions to our customers’ success.
Savvy educators know it when they’re looking at data that doesn’t make sense. Often this issue results from errors during manual data entry into one of the many systems that schools use to track everything from attendance to behavior to in-progress grades. The Schoolzilla data model was designed to be consistent across all customers and source systems, and we’ve developed data quality checks and reports that can identify and surface these issues before they make it into our customers’ analyses.
How it looks in the field…
Here’s an example of a report designed to surface errors in Student Information Systems. Every error that this report displays is “clickable,” allowing you to efficiently clean up your SIS, and root out the source causes of persistent data quality issues.
The term “janitor work” sounds pejorative in the title of the New York Times article, but we interpret it differently. We have enormous respect for the men and women who work hard to keep schools safe and clean, and we humbly accept the analogy for the work we do with school data.