Speeding up data understanding by interactive exploration
A key success factor of data science projects is to understand the data well. This blog explains why coding can be inefficient for this and how you can improve.
By Visplore.
Understanding the data is fundamental for all steps of a data science project. However, gaining that understanding can be challenging and time-consuming. It involves interpreting data patterns with deep knowledge from the project domain to understand, for example, which parts of the data are useful and which correlations actually matter.
Oftentimes, data scientists don't have that deep domain expertise. A key to project success is thus the communication to domain experts.
Traditionally, this communication works as follows: data scientists prepare a presentation by coding charts in languages such as Python. Then, they discuss the charts with domain experts. This typically triggers further questions such as "What are these clusters?" or "Which other variables show this trend?". Even great coders usually can’t spontaneously answer all these questions so that they get postponed to a follow-up meeting. Altogether, it may require several tedious feedback loops, significantly increasing the duration and costs of the project. Worse even, cutting this phase short may risk project success!
Traditional data communication vs. joint exploration
How can you improve this? Reconsider the use of coding for understanding the data! Coding is great for building data pipelines, models, monitoring apps, and much more. However, it is generally too static for answering ad-hoc questions from domain experts during a meeting.
Visplore is a graphical tool which is optimized for agile exploration of massive multi-variate data, in particular (but not limited to) time series such as sensor data.
Explore millions of raw values as fast as never before - even while sitting together with domain experts. This turns data exploration into a vivid dialogue and makes understanding the data an exciting step of a project.
Simply load data directly from Python, Matlab, R or other sources. Pre-configured analysis cockpits provide deep insights within seconds, with hardly any configuration. Built-in analytics answers complex questions on-the-fly, and powerful interaction tools support for selecting, cleaning and labeling data.
For shorter project duration, less risk, and more satisfaction of all stakeholders.
