Navigating the data lake
Smart data, big data, red data, blue data. Data these days comes in countless forms and types. Its storage forever-home: the data lake. How might a software program help analysts navigate and process the raw information that sits in these lakes?
For SAS Institute, Aug. – Sept. 2015
User experience design
Working with SAS Institute, we asked: How might we design an intelligent and self-learning data management system?
Change the equation
The current data analysis process isn’t pretty. More time is spent prepping the data for analysis than actually analyzing it. Reversing this equation, by inventing smarter, more intuitive methods for a software program to handle data, was our mission.
Identifying an opportunity area
After mapping SAS’ data management project cycle we identified three main user groups that overlapped throughout the project cycle. Based on the variety of tasks a marketing analyst, one of three user groups we identified, was asked to complete we decided this user’s tasks offered the greatest opportunity for intervention.
There and back again, a user’s journey
To understand the data analysis process within SAS’ current software, we constructed a user journey map, illuminated some clear pain points occurring in the middle of the process. Tedious cleaning, partitioning, and transforming tasks dragged a user’s enthusiasm down, and their mood was slow to recover.
From experience maps to explicit tasks
We translated our user journey map into a series of three, explicit task flows. These task flows combined our knowledge of the current process with ideas for intervention.
Although marketing analysts clean data on a regular basis, and are the least-trained at this task compared to their peers, they have to work within a program built for experienced data scientists.
Build a data cleaning software for the rookies
We decided to focus on newly-minted marketing analysts, and build a data cleaning software suited to their new needs. This meant it had to be intuitive enough to cut down our audience’s data cleaning time, but without sacrificing the accuracy of higher-level software.
Our final prototype
In this task flow Juno Allcott, a new marketing analyst at a small pharmaceutical company, finds and analyzes relevant data sets about U.S. Lyme Disease patients; she must explore and clean this data in conjunction with her team.