Dear Analyst KeyCuts

With an undergraduate degree in zoology and a master's in public health, you wouldn't expect Amanda Makulec to lead a successful career in data analytics and data visualization. As we've seen with multiple guests on the podcast, the path to a career in data analytics is windy and unexpected. It was the intersection of public health and data visualization that got Amanda interested in data visualization as a career. In one of her roles, Amanda was supporting USAID by analyzing open data sets and creating charts and graphs for publishing content. Her team consisted of graphic designers and developers. Designers would basically take her charts from Excel and add more color and add on text to the chart. Amanda found that large enterprises were facing the same challenges as the organizations she was supporting in public health (and enterprises have more money to throw at this problem). Thus began Amanda's career in data viz.















How do you tell a data story?







We've talked a lot about data storytelling a lot on this podcast. If there is one person who can crisply define what data storytelling is, it would be Amanda. This is Amanda's definition according to this blog post:









Finding creative ways to weave together numbers, charts, and context in a meaningful narrative to help someone understand or communicate a complex topic. 









We talked a bit about how data storytelling can mean different things to different people (this blog post in Nightingale talks more about this). You might work with a business partner or client who says they want a data story, but all they really want is just an interactive dashboard with a filter. Amanda cites Robert Kosara's definition of data storytelling in 2014 as one of her favorites:









* ties facts together: there is a reason why this particular collection of facts is in this story, and the story gives you that reason







* provides a narrative path through those facts: guides the viewer/reader through the world, rather than just throwing them in there







* presents a particular interpretation of those facts: a story is always a particular path through a world, so it favors one way of seeing things over all others









Amanda stresses the 3rd bullet point as the most important part of data storytelling. If the audience has to walk away with one analytics fact from the story, what is that fact you want to get across?







Source: Effective Data Storytelling







Getting feedback on your data stories and visualization







One point Amanda brought up during the conversation which I think is worth highlighting is feedback. After you've published of launched an analysis, dashboard, or data story, you rarely get feedback on how effective the product was at telling a story. You might get some qualitative feedback like the dashboard answers specific questions or that the findings are "interesting." But was the visualization actually effective at telling a story?







Amanda likes to ask people what they like and don't like about her data stories and visualizations. Often people will get frustrate because the key takeaway from the data story is simply counter to what they believe. This leads them to questioning the validity of the data source. But you as the storyteller are simply conveying the signal from t...

Sometimes pop culture and data analysis meet and the result is something interesting, thought-provoking, and of course controversial. How can one use data to prove definitely which Taylor Swift songs are the most underrated? Isn't this a question for your heart to answer? Andrew Firriolo sought to answer this question over the last few months and the results are interesting (if you're a Taylor Swift fan). As a Swiftie since 2006 (moniker for Taylor Swift fans), Andrew wanted to find a way to bridge his passions for Taylor Swift and data analysis. He's currently a senior data analyst at Buzzfeed, and published his findings on Buzzfeed to much reaction from the Swiftie community. In the words of Taylor Swift, Andrew's methodology and analysis just "hits different."















From comp sci to data analytics







Andrew studied computer science at New Jersey Institute of Technology but realized he liked the math parts of his degree over the engineering parts. Like many guests on this podcast, he made a transition to data analytics. Interestingly, it wasn't a job that propelled him into the world of data analytics. But rather, going to graduate school at Georgia Institute of Technology (Georgia Tech). GIT has some really affordable online technical programs including data analytics. After getting his master's degree, he worked at Rolling Stone as a data analyst. This is the beginning of Andrew's exploration into the Spotify API to see the data behind music. You can see some of the articles Andrew published while at Rolling Stone here.







Source: Pocketmags







After Rolling Stone, Andrew landed his current role at Buzzfeed building internal dashboards and doing internal analysis. In both of his roles, he talks about using a lot of SQL and R. A big part of his job is explaining the analyses he's doing to his colleagues. This is where the data storytelling aspect of a data analyst's job comes into play. I call this the "soft" side of analytics but some would argue that it's the most important part of a data analyst's job. In most data analyst roles you aren't just sitting at your desk writing SQL queries and building Excel models. You're a business partner with other people in the organization communication skills are more important than technical skills.







Answering a Taylor Swift question with data







Andrew became a Taylor Swift fan through his sister in 2006. They both listed to the world premier of Taylor's first album. Given his background in data, Andrew decided to answer a question about Taylor Swift that's been on his mind for a while: what are Taylor Swift's most underrated songs?















To read Andrew's full article, go to this Buzzfeed post.







Andrew's hypothesis was that there's a way to use data to prove which songs in Taylor's discography are most underrated. When I classify something as "underrated," it's usually a decision you make with your gut. But it's always interesting to see the data (and the methodology) for determining if something is truly "underrated."







Multiple iterations in song streaming analysis
...

We sometimes forget that a large organization is composed of groups and divisions. Within these groups, there are teams and individuals looking to advance their careers. Sometimes at the expense of others. When your advancement depends on the success of your project, the benefits of that project to your company may be suspect and the tools you use to complete that project may not be the best tools for the job. Alex Kolokolov started his journey in data like many of us: in Excel. He moved on to Power BI, PowerPivot, PowerQuery, and building data visualizations for the last 15 years. In this episode, he talks through consulting with a company as the analytics expert only to find out that the the underlying forces at play were company politics. He also discusses strategies to make your line charts tell a better data story.















The state of analytics at companies in traditional industries







Alex consults with large companies in "traditional" industries like oil, gas, and mining companies. The state of analytics and knowledge of analytics is not equal in these companies, according to Alex. You'll come across data science and AI groups at these companies who are, indeed, working on the cutting edge. But then when you approach other departments like HR or operations, they are still quite far from this digital transformation that everyone's talking about.















Alex worked with a mining company where there are cameras that can ID employees using facial recognition when they walk through the door. But when you sit down with the folks who are actually doing the work at the plant, they are still humming along on Excel 2010. Excel 2010! What a time...







Source: dummies.com







In terms of creating dashboards, teams from these companies would consult their IT or tech team to create a report. But then the IT team comes back and says it will take three months to create this report given their current backlog. Hence the reason these companies outsource the analytics training, metrics collection, and dashboarding to people like Alex.







Internal battles for power and platforms







Alex once worked with a government institution and they were building an internal SQL data warehouse before Power BI came on the scene. This specific project was driven by IT as a warehouse solution for the finance department. a few years later, the head of this SQL project became the CIO, but started getting some pushback from the heads of the finance department. It turns out the finance department heads already had their own platform in mind and claimed Microsoft's technology was outdated for their purposes (the finance team wanted to go with Tableau to build out pretty dashboards).







Source: reddit.com







The finance department proceeded to roll out their solution in Tableau and the CFO eventually became the Chief Digital Office and pushed the CIO who was spearheading the SQL project out. The project wasn't about Microsoft vs. Tableau at all. It was all about who was better at playing the game of internal politics and fighting for the resources to get your project across the line.







When digital transformation is 10 years too late







Large companies Alex has worked claimed they went through "digital transformation" but this was back in 2012. When Alex started working with these companies over the last few years, he found that individuals were still using SAP and Excel 2010. It's as if the digital transformation didn't go past 2012,

When you think of data journalism, you might think of The New York Times' nifty data visualizations and the Times' embrace of data literacy for all their journalists. Outside of The New York Times, I haven't met anyone who does data journalism and data storytelling full-time until I spoke with Ben Wylie. Ben is the lead financial journalist at a financial publication in London. Like many data analysts, he cut his teeth in Excel, got his equivalent of a CPA in the UK, and received his master's degree in journalism. In this episode, we discuss how his side passion (sports analytics) led him to pursue a career in data journalism and how he approaches building sports data visualizations.















Playing with rugby data on lunch breaks







When Ben worked for an accounting firm, he would pull rugby data during his lunch breaks and just analyze it for fun. One might say this started Ben's passion in data storytelling because he started a blog called The Chase Rubgy to share his findings. The blog was a labor of love, and at the end of 2019 he had only focused on rugby. After building an audience, he realized data journalism could be a promising career path so he did some freelance sports journalism at the end of his master's course. At the end of 2022, he started Plot the Ball (still a side project) where the tagline is "Using data to tell better stories about sport."















Learning new data skills from writing a newsletter







Ben spoke about how writing Plot the Ball forced him to learn new tools and techniques for cleaning and visualizing data. All the visualizations on the blog are done in R. A specific R package Ben uses to scrape data from websites is rvest. Through the blog, Ben learned how to scrape, import, and clean data before he even started doing any data visualizations. Sports data all came from Wikipedia.







I've spoken before about how the best way to show an employer you want a job in analytics is to create a portfolio of your data explorations. Nothing is better than starting a blog where you can just showcase stuff you're interested in.







How the NBA became a global sport







One of my favorite posts from Plot the Ball is this post entitled Wide net. It's a short post but the visualization tells a captivating story on how the NBA became global over the last 30 years. Here's the main visualization from the post:







Source: Plot the Ball







Ben first published a post about NBA phenom Victor Wembanyama in June 2023 (see the post for another great visualization). Ben talks about this post being a good data exercise because there is no good NBA data in tabular form. This "waffle" chart was Ben's preferred visualization since it allows you to better see the change in the subgroups. A stacked bar chart would've been fine as well, but since each "row" of data represents a roster of 15 players, the individual squares abstracts the team composition each year.







Home Nations closing the gap with Tri Nations in rugby

There are only so many ways to make Excel "fun." If you've been following this blog/podcast, stories about the financial modeling competition and spreadsheet errors that lead to catastrophic financial loss are stories that make a 1980s tool somewhat interesting to read and listen to. There are numerous tutorials and TikTok influencers who teach Excel for those who are actually in the tool day in and day out. Meet Matt Basta, a software engineer by trade. He published a story on his own blog called No sacred masterpieces which is worth reading in its entirety as its all about Excel. In this episode, we discuss highlights from Matt's time at Uber, how he built a version of Excel online to help Uber China compete with DiDi, and how Uber completely scrapped the project weeks later after DiDi acquired Uber China.















Business intelligence at Uber through the eyes of a software engineer







I don't normally speak with software engineers on the podcast, but Matt's story during Uber will resonate with anyone who works at a high-growth startup and lives in Excel. Matt's story has everything. Tech, cutthroat competition, drama, and of course, Excel.















Matt has worked at a variety of high-growth startups like Box, Uber, Stripe, and now Runway. He joined Uber in 2016 and worked on a team called "Crystal Ball." The team was part of the business intelligence team. The goal of this team was to create and develop a platform that analysts and business folks could use to figure out how much to charge for rides, how much incentives to provide to drivers, etc. All the core number crunching that makes Uber run.







As per Matt's blog post, employees were working on one of two major initiatives at Uber in 2016:









* Redesigning the core Uber app







* Uber China









As Matt told his story, it reminded me of all the news articles that came out in 2016 about Uber's rapid expansion in markets like China. The issue is that a large incumbent existed in China: DiDi. This comes up later in Matt's story.







Getting data to the city teams to calculate driver incentives







From the perspective of the Crystal Ball team, all they wanted to do was set up a data pipeline so that data about the app could be shared with analysts. Analysts would then download these files and crunch numbers in R and this process would take hours. In 2016, Uber was competing directly with DiDi to get drivers on the platform. The city team would use the data provided by the Crystal Ball team to figure out how much of an incentive to offer a driver so that the driver would choose to drive with Uber instead of DiDi for that ride.







Source: Forbes







The problem was that the city team in China was using these giant Excel files that would take a long time to calculate. In order to compete with DiDi, Uber China would need a much faster way to calculate the incentives to offer drivers. This is where Matt's team came in.







The only other "tool" the city team had at their disposal was the browser. The city team still wanted the flexibility of the spreadsheet, so Matt's team strategy was to put the spreadsheet in the browser. Now at this point,

One of the more popular courses you could take at my college to fulfill the finance major requirements was Behavioral Finance. The main "textbook" was Inefficient Markets and we learned about how there are qualitative ways to value a security beyond what the efficient market hypothesis purports. During the financial crisis of 2008, psychology professor and behavioral economist Dan Ariely published Predictably Irrational to much fanfare. The gist of the book is that humans are less rational than what economic theory tells us. With the knowledge that humans are irrational (what a surprise) when it comes to investing and other aspects of life, the capitalist would try to find the edge in a situation to get a profit. That is, until, recent reports have surfaced showing that the results of Dan Ariely's experiments are fabricated (Ariely partially admits to it). This episode looks at how the data was potentially fabricated to skew the final results.







Dan Ariely. Source: Wikipedia







Background on the controversy surrounding Dan Ariely's fabricated data







In short, Ariely's main experiment coming under fire is one he ran with an auto insurance company. The auto insurance company asks customers to provide odometer readings. Ariely claims that if you "nudge" the customer first by having them sign an "honesty declaration" at the top of the form saying they won't lie on the odometer reading, they will provide more accurate (higher) readings.







I was a fan of Predictably Irrational. It was an easy read, and Ariely's storytelling in his TED talk from 15 years ago is compelling. I first heard that Ariely's experiments were coming under scrutiny from this Planet Money episode called Did two honesty researchers fabricate their data? The episode walks through how Ariely a thought leader and used his status to get paid behavioral economics consulting gigs and to give talks. Apparently the Israeli Ministry of Finance paid Ariely to look into ways to reduce traffic congestion. In the Planet Money episode, they talk about how other behavioral scientists like Professor Michael Sanders applied Ariely's findings to the Guatemalan government by encouraging businesses to accurately report taxes. Sanders was the one who originally questioned the efficacy of Ariely's findings. Here is part of the abstract from the paper Sanders wrote with his authors:









The trial involves short messages and choices presented to taxpayers as part of a CAPTCHA pop-up window immediately before they file a tax return, with the aim of priming honest declarations. [...] Treatments include: honesty declaration; information about public goods; information about penalties for dishonesty, questions allowing a taxpayer to choose which public good they think tax money should be spent on; or questions allowing a taxpayer to state a view on the penalty for not declaring honestly. We find no impact of any of these treatments on the average amount of tax declared. We discuss potential causes for this null effect and implications for 'online nudges' around honesty priming.









Professor Michael Sanders







If you want to dive deeper into Dan Ariely's story, how he rose to fame, and the events surrounding this controversy,