I still don’t get it…
For illustrating the difference between Visplore and Tableau (Tableau Desktop version 2020.3), let’s have a look at an example use case. Assume we need to understand process data from a hydropower plant. It contains 21 sensors (which is not much for process data) with one value per minute over 4 years – approximately 2 million values per sensor. In both tools, the data can be imported within a few seconds. Afterwards, however, the process is very different. Note: we chose Tableau here, but the distinction is very similar for other business intelligence programs such as Power BI.
Step 2: Correlation analysis
Correlations are a key aspect of exploratory data analysis. In this use case, it’s relevant to identify groups of highly correlated sensors and periods that differ from usual correlation behavior.
Step 3: Comparing time periods
Correlations may change over time – and that’s typically relevant information. In this use case, for example, it can be meaningful to compare periods of normal process behavior, without the spikes caused by start-up and shut-down procedures.
So, is Visplore always the better choice?
No. The previous section illustrated a typical data exploration use case on sensor data. The story is different for building an operational report of a few selected business KPIs. For this use case, the numerous options to fine-tune details of the views and the flexibility in building and deploying custom dashboards makes Tableau or Power BI (or some other business intelligence tools) great choices!
And that’s the point: In today’s data-driven world, there is no “one size fits all”. Data is becoming way too business-critical for all enterprises so that using the wrong tool for the wrong task and/or wrong data would work in the long run. It can be tempting to keep using familiar tools or trying to minimize the overall number of tools. That’s why much data analysis is still done in Excel, even when it does not fit the capabilities of Excel at all. However, using tools for purposes which they have not been designed for is simply not efficient!
- For standardized interactive reporting of business KPIs, the best choice might be Tableau, Power BI or some other business intelligence tools.
- For operational predictive / AI models, this might be Python or R in conjunction with the numerous toolkits available for data processing and modelling.
- For advanced scientific or engineering programming, this might be Matlab.
- For building and deploying data processing pipelines visually, this might be Alteryx, KNIME, RapidMiner, or Orange.
… and this could be continued.
For that reason, Visplore offers integrations to Python, Matlab, and R – enabling users to work in a side by side manner and to use each tool for what it does best.
And for checking, exploring and labeling large sensor data as well as for digging deep into process issues – that’s Visplore.