Episode #71: Benn Stancil, Co-Founder and Chief Analytics Officer at Mode Analytics on all things analytics

This post originally appeared on the KeyCuts blog.

Learning the tips and tricks for doing data analysis in Excel is great and all, but stepping back to see the bigger picture leads to better questions (and answers) you can ask as an analyst. Benn Stancil is one of the founders at Mode Analytics, a data visualization platform you may have used before. Benn has done a ton of typical "analyst" work (check out his newsletter and previous Medium blog) but in this episode we talk about building Mode in the early days, asking good questions, and of course a little bit about Excel and the tools he uses.

Life before and founding story of Mode Analytics

Benn was a math and economics major in college. He worked in DC for a few years for a think tank doing economic policy research. It was an interesting time to do this type of analysis because this was right around the start of the financial crisis in 2008. Frustrated with his research not being able to make an impact given the glacial pace government adopts change, Benn ended up finding a job at Yammer (a social network for professionals, sound familiar?). Yammer was acquired in 2012 by Microsoft and Benn and some folks from the analytics team at Yammer left Microsoft to start Mode.

I would take a look at this blog post from Benn to get his reflections on the early days at Mode, but here is a quick summary. The founding team at Mode includes Derek Steer (CEO), Josh Ferguson (CTO), and Benn (the analytics guy). Before there was really a product, Derek was off talking to investors and Josh is talking to the engineers. Benn's expertise is in doing data-related stuff, but the problem is there wasn't a lot of data to analyze or explore and not many customers to get data from.

In the early days, Benn wrote blog posts that were generally about data but not about data products. He was basically doing content marketing for a small nice of data professionals. Today this is called data journalism and Benn was writing data stories related to sports (before 538 became ta thing). Once Mode had more customers, Benn's role changed often as he did tours of duties through marketing, customer support, sales, and a variety of other roles.

You have a "job title" and a "role" and those end up being two are very different things.

We talked a bit about a blog post Benn wrote in 2015 that had some traction in the data community on Facebook's "magic metric" of getting 7 friends in 10 days. The key takeaway is that Benn was doing the analysis in Excel, R, and a little python for web scraping. Benn had taught himself how to use some of these tools before. Now that he had a goal to work towards (creating these blog posts), this was the extra push for him to get over the hump to learn these tools more in-depth.

Source: Mode blog

Key skills for doing exploratory data analysis

I liked Benn's answer to this open-ended question because it doesn't involve mastering X tool or taking advanced statistics classes:

First and foremost you have to be curious. Be relentless in knowing there's a better answer out there.

Most analysts get a dataset and just look at the data to start generating questions as they do the analysis. Benn suggests going the other way. Generate questions you have about the dataset before you get into the analysis. This leads to answers that are expected or unexpected. Regardless, this strategy will leda you to keep asking questions.

The most interesting questions are the ones that you don't start with.

I'd recommend checking out some of Benn's older posts where he documents some of his exploratory analyses like this one about the price of weed.

Learning tools other than Excel like SQL and Python

During Benn's stint in economic policy research, he was primarily using Excel to do analysis. While it's the first tool analysts reach for, Benn said that you can still be a good analyst even if you don't use Excel. Some of the best analysts he knows aren't using Excel every day but are asking good questions about the data.

Having said that, learning other tools outside of Excel also opens the possibilities for you to do your analysis faster and opens the door to more interesting questions. Using Excel for all your data analysis needs just means you might have to do more manual work than what's necessary.

One caution Benn brought up is that there may be a danger in learning the advanced features of different data tools. It's easy to go down the rabbit hole and get addicted to these advanced features. What ends up happening is that you try to do everything in that tool when another tool would've been better for that use case.

Benn references a time when I was learning D3 for data visualization. Before he knew it, he was trying to use D3 for all his data visualization needs since he knew the platform so well, but a simple chart in Google Sheets might have sufficed.

How tools might be influencing how you think about data

I've noticed that the proliferation of online SaaS tools can influence how you think about work. Tools you use for communication, design, marketing, and data analysis have built-in "opinions" about how the companies behind those tools view the world. By buying into the tool, you are implicitly buying into their ways of working and being productive.

I asked Benn on his thoughts about this topic as it relates to data tools. He brought up an interesting example with a tool that's been around for some time: Tableau.

Tableau is kind of like a giant PivotTable. By using Tableau, you get nudged into thinking about your data a certain way. If you want to do a time series analysis, you could bend Tableau to make it do the analysis you want. But it wasn't made to do time series analysis easily, and there might be a better tool for the job (e.g. Excel or Google Sheets). Same can be said about using SQL for statistical analysis. SQL makes you think about structuring your data tables a certain way for easy querying, but it's nothing compared to R for statistical analysis.

Let the questions shape the tool.

The future of Mode

I would take a listen near the end of the episode to hear Benn's take on what the future of Mode looks like, but the quick takeaways are:

• Finding ways to let analysts answer questions quickly
• Extending Mode into other departments beyond the data group so that the sales team can start asking questions about their data (side note: we're seeing this with a lot of other online tools that are moving from "single-player" to "multi-player" like Figma for design teams)
• Mode may be competitive with other BI tools like Looker and Tableau, but can be overlapping with these tools a well in terms of consuming data

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