About the Applicant
SAP Labs LLC, Design Services
Design Co-Innovation Center, America; Janaki Kumar, Eliad Goldwasser, Jaehun Jeong, Jadine Yee, Sarah Deng, Steve Garcia, Russ Green, Jim Brooks
Those of us who have the privilege of working with big data will face the universal challenge of taking apart massive data to find a starting point. We had a different priority than the project owners and developers who were focused on the data itself. As designers, we needed to pinpoint what questions we were trying to answer.
Using the design thinking process we found that analysts had a diverse set of use cases, but similar needs. Our solution allows users to have control over their queries when they know what they're looking for, but also gives flexibility to discover insights in areas not on their radar.
Data can surprise anyone, even professional analysts. Experienced analysts may know what to look for based on experience, but we give an opportunity to all users to find patterns in data. We implemented unique features for the user to "play with data". Charts can be merged with direct manipulation and undone with one click. Our timeline control slider updates all charts on the page for quick and nonpermanent snapshot views. A drill down of data can be seen by interacting with charts.
During the process we had some assumptions corrected by conducting design validations throughout with users. We tend to think real-time data is better than static data. But, we learned it is fundamental to consider whether this "trending feature" made sense for this solution. Users preferred batches, or snapshots, of data because they were more interested in patterns over a long period of time. This discovery also made us rethink our landing page which was originally a dashboard of real-time updates. Our landing page now acts as a starting point for creating new queries from a history of reports.
We thought we needed visualizations alone to make it easier to consume large amounts of data. However, we received skepticism from users on whether this would be a stand-alone tool as we intended. Because analysts hold the great responsibility of creating accurate insights, they need confidence in their data source. We adapted our design to make the raw data easily accessible from any visualization
We had feedback from subject matter experts in visualizations who were in consensus that "bells and whistles" in visuals hide meaning. Our charts are clean, simple, and 2-dimensional by design. We hope our journey will help others who are creating solutions for big data. With big data the goal is to break down data and display it for human consumption and connection.
Before the project started we knew that aggregating this data to meaningful insights about consumer usage of their mobile devices could change the marketing and media-buying field. People using the tool will be able to draw conclusions based on comprehensive data and not based on sample data which was the case before SAP Consumer Insight 365 was built.
The important insights were in analyzing usage patterns, location and movement of different demographics throughout the day. But when came to the design, we wanted to make sure that the anonymity of the consumers will be kept. We didn’t want to expose any data that can be traced back to an individual user. The next challenge was in the variety of dimensions in the data and the different intersection points of those dimensions and attributes. We started with some of the obvious market analysis questions that every marketer asks and we wanted to make sure that the application could easily answer those questions. And through user research, we found out how marketing analysts do their job, and got insights of how we might help make their job easier.