Statwing simplifies data analysis

Quickly perform and interpret the results of routine Small Data analysis

With so much focus on Big Data, the needs of many analysts who work with Small Data tend to get ignored. The default tool for many of these users remains spreadsheets1 and/or statistical packages which come with a lot of features and options. However many analysts need a very small subset of what these tools have to offer.

Enter Statwing, a software-as-a-service provider for routine statistical analysis. While the tool is still in the early stages, it can already do many basic “data analysis” tasks.

Consider the following example of a pivot table constructed in Excel: this required 8 mouse-clicks, if you do everything perfectly, and about 5 decisions (what variables to include, what metric to use, …)

The same task in Statwing required 4 mouse-clicks and 0 decisions! Plus it comes with visuals:

The lack of clutter and the addition of a simple “headline” (“Female tends to have much higher values for satisfaction than Male“), makes the result much easier to interpret. The advanced tab contains detailed statistical analysis (in this case the p-value, counts, values). Many users get confused by the output/results produced by traditional statistical software. Let’s face it, many analysts have had little training in statistics. I welcome a tool that produces readily interpretable results.

The company hopes to replicate the above example across a wide variety of routine data analysis tasks. Their initial focus is on tools for (consumer) survey analysis, a potentially huge market given that online companies have made surveys so much easier to conduct. Users of Statwing pay a small monthly subscription, making it cheaper than most2 statistical packages. For a small monthly fee, their intuitive UI lets analysts get their tasks done quickly. More importantly Statwing may nurture aspiring data scientists in your organization.


(1) As this recent Strata presentation points out: Spreadsheets are the glue that keeps many organizations together.

(2) Open source tools like OpenOffice, R and Octave are free. So is the use of Google spreadsheets.

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  • Monica Lewis

    Awesome — I love making statistics more accessible to the masses, having seen in my own career as an engineer + consultant, how the application of statistics to understand processes can literally save thousands or millions of dollars for businesses with the touch of a button. I wonder about how to frame the outputs to ensure the statistical results lead to actionable changes in a business.

    I’m working on a different part of this problem, wondering more about simplifying the process of collaboration as it relates to data for the average business user, http://www.hellodata.me. I’m curious to hear your thoughts on where the world is going when it comes to collaborative data analysis, esp with the growth of improved collaboration through products like Box, and what tools will help turn analysis outputs into communicable results that influence decision-makers to take action.