Analyzing Time-series without the Llama Drama

Barry Burns
Barry Burns
1 Min Read
Summarize and analyze this article with:

Analyzing Time-series Data

Some argue that time is a flat circle. Most analysts, however, can agree that working with time-series data is tough.

Processing large volumes of transactional records, building rolling period aggregations, and implementing fiscal calendars are tedious. Don’t even get me started on time-zone transformations.

Savant Time Series Rollups

The pain is near and dear to our hearts at Savant, which is why we built a tool to greatly simplify time-series operations.

Let’s say we had a data set that tracks how many llamas each Savant employee pets on a given day.

No alt text provided for this image

Now, we’re a bit competitive at Savant, so we want to be able to track who pet the most llamas every quarter, as well as who is improving in their llama petting on a rolling month-to-month basis.

The Savant time series tool easily takes our raw data and creates the roll-ups needed for these calculations.

See how it works in this short video:

With just a few more steps in Savant, I’m able to automate a competition tracker that updates our llama scores on a daily basis:

No alt text provided for this image

Make smarter, faster decisions

Transform the way your team works with data

Co-Founder and Head of Customer Success
Barry Burns is the Co-Founder and Head of Customer Success at Savant. Previous to Savant, Barry held senior director roles, including Senior Director of D&B Digital, where he launched D&B Lattice in the United Kingdom, and Senior Director of Data Services, managing data solutions and developing service offerings for customer implementations after D&B’s successful acquisition of Lattice Engines, where Barry managed engineering and customer service teams. Barry holds a Bachelor of Art in Economics from Stanford University, and a Master’s of Science in Management of Technology and Entrepreneurship from École Polytechnique Fédérale de Lausanne.