Last week, Trifacta celebrated the 1.0 launch of our Data Transformation Platform at the O’Reilly Strata conference. Strata is a place for the data community – including data scientists, business leaders, tool builders and vendors – to come together, share progress and advance the state-of-the-art.
Our team had a blast sharing the Trifacta product and contributing office hours, tutorials, and featured talks. However, like most conferences, I found that the most meaningful and memorable events were the conversations: in the hallways, over meals and on the exhibition floor.
We have entered an ideal time to invent new tools for data analysis. Important advances continue to be made on the infrastructure, for example the increasing adoption of systems for machine learning such as GraphLab and for lower latency processing like Apache Spark. There also remains palpable excitement around programming-oriented tools for data scientists, particularly in the Python and R programming language communities.
The people I talked to varied widely with respect to their backgrounds in statistics, computer science, and data visualization. A theme that came up repeatedly is that as the Big Data infrastructures have matured, the critical outstanding challenge is to build better tools that really put these systems to work. In particular, these tools should solve pressing business tasks without requiring a degree in computer science or statistics in order to be accessible to business domain experts. This theme goes hand-in-hand with the growing recognition that the biggest bottleneck in data science is transforming and cleaning raw data to enable effective analysis.
Enabling people, whether data scientists or business experts, to put their data to work is an exciting challenge and an incredible opportunity. I left Strata more excited than ever about Trifacta’s mission to tackle this challenge head on.