Dear Analyst #61: Empowering businesses and individuals with data literacy skills with Oz du Soleil

Oz is one of the best creators of Excel content I know with his Excel on Fire YouTube channel. Unlike traditional “how-to” videos, his videos blend education with entertainment making the learning process feel like binging your favorite Netflix show. Oz and I met on Google+ way back in the day and in person at the 2014 Modeloff competition. While Oz is an Excel MVP and Excel trainer on LinkedIn, our conversation goes deeper into data literacy and understanding where your data is coming from before it gets into the spreadsheet.

Know just enough Excel to get your job done

When I first met Oz at the Modeloff in 2014, he told me a story about how he discovered the power of Excel for changing people’s lives. This story really shows the human side of a spreadsheet program that is typically associated with business and enterprise use.

Oz was teaching Excel at a medical school and helping the students in his class automate their reports. He met one student who was simply copying and pasting cells up and down the spreadsheet, and was spending an hour doing these manual operations. He realized the student just needed one formula to automate the task she was doing, she just didn’t know what that formula was.

I started learning about people who needed to know how to use certain features in Excel, but didn’t need to know how to learn how to use everything in Excel.

Once the student saw how the formula could eliminate all the tedious work she was doing, it changed how she worked and gave her so much more time to focus on more important aspects of her job.

I think a lot of people approach their tools and software with a similar mindset. You know there is probably a better or faster way of doing something, but you go with what you know. There’s a bit of the JTBD (jobs-to-be-done) framework here. Knowledge workers need to know just enough to solve the problems they face on the job, and can leave the rest of the software’s feature set for the power users.

You’ll work with data no matter what role you have

Prior to our conversation, Oz mentioned to me he wanted to talk about more than just Excel tips and tricks. These topics are covered at nauseum by other content creators; and for good measure as people need and want this training (yours truly has benefited from creating this type of content). What really tickles my fancy are the topics surrounding Excel, and there is no one better to go in-depth with me on these topics than Oz.

Analyst might not be in your title.

Nonetheless, you are or will be sorting, filtering, and summarizing data no matter what department or level your work in. Excel is merely a tool to get you from the raw data to the story you tell to your internal stakeholders to launch X feature or to external clients to purchase your product.

Oz talks about how people taking an Excel class will get them feeling comfortable about using the tool, but it only goes so far. As you get real world experience, you’ll start to ask questions about data quality and the data source(s). These are topics that go beyond Excel and into the realm of databases, data transformation, and data pipelines; topics I’m trying to cover more of on this podcast.

Oz opined about the dilemma one faces with duplicate data. Do you de-duplicate at the source (perhaps in a view in a database) or do you do it in the spreadsheet? Most analysts (present company included) will make the necessary changes in Excel or Google Sheets for one reason: it’s fast. Harkening back to the previous section’s takeaway: I just need to get a job done and and don’t care (for now) how it gets completed.

Before data storytelling, there’s data literacy

I’ve talked about data storytelling on numerous episodes (see the data storytelling episode with the New York Times). It’s a hot topic for a lot of companies as they start incorporating software into their product offerings (if you’re a SaaS company, you’re already swimming in a big data lake).

Before one can create these masterful data-driven stories, Oz believes there is a more fundamental skill one needs to acquire: data literacy. When you look at a report, you should be able to answer questions like “Can I trust the data source?” and “What am I really looking at with this data?”.

A recent article by Sara Brown at MIT Sloan highlights the following data literacy skills today’s knowledge worker should have:

  • Read with data, which means understanding what data is and the aspects of the world it represents.
  • Work with data, including creating, acquiring, cleaning, and managing it.
  • Analyze data, which involves filtering, sorting, aggregating, comparing, and performing other analytic operations on it.
  • Argue with data, which means using data to support a larger narrative that is intended to communicate some message or story to a particular audience.

The article goes on to explain the different steps a company can take to build an effective data literacy plan. An interesting stat Brown highlights in the study is this one from a Gartner survey conducted by Accenture:

In a survey of more than 9,000 employees in a variety of roles, 21% were confident in their data literacy skills.

Should we be surprised by this finding? I think not.

Did you ever need to take an Intro to Data Literacy course in middle or high school? Was learning spreadsheets part of the curriculum? Things change a bit at the university level as deans and presidents realize their students are not meeting the demands of hiring managers. I reference an episode of Freakonomics in episode 22 where they break down the deficiencies in the U.S.’s math curriculum. Key takeaway: a majority of what you learn in the K-12 system does not prepare you for a job requiring data literacy.

Empowering small businesses to use Excel

Oz made a great point about not just the content produced about Excel, but the features many bloggers and trainers decide to demonstrate in their content.

I worry that so much conversation has enterprises in mind, or the start-ups that want to get huge. But there are a lot of small businesses, and they’re lost in conversations that they don’t know aren’t meant for them.

Naturally, the type of professional who can spend a few hundred or few thousands dollars on a comprehensive Excel training probably works at a large enterprise or well-funded startup. But there are millions of flower shops, retail stores, and non-profits who may still be using Excel like the way Oz’s student was using Excel at that medical school.

This is an area Oz is passionate about and there is clearly a need to provide Excel training for this demographic. Chances are the flower shop won’t need to do complex VLOOKUPs and mess with Power Query. They just need to know the features–hope you’re starting to see the theme here–to get their jobs done.

Is Excel a database?

For many of these small businesses, yes.

Oz has seen small 5-person companies have some database platform installed and no one in the company uses the database because no one knows how to. He saw a non-profit where the DBA was a woman who worked half a day a week. If anyone needed to get data or add data to that database, they had to wait for the 4 hours a week she was available to handle their requests.

While it pains many of you (I include myself here) to see businesses inefficiently store their data in Excel or a Google Sheet, we must come to accept that not every business scenario needs to have auto-refreshing PivotTables and VBA macros.

Oz talks about the need to have more honesty and empowerment around what is possible with Excel. He hears the database vendors and data science crowd talk about using the latest and greatest database platforms or programming in R or Javascript. These are all great solutions for the enterprise, but who is going to implement these solutions at the flower shop? Perhaps this is the realm for the no-code platforms like Shopify to make e-commerce as simple as possible.

At the end of the day, Oz realized (like many analysts) that his Excel skills are necessary for many businesses whose data are trapped in databases. He would be in conversations with companies who need to create detailed reports, but then argue about which cost center is going to “fund” the project. Then you have green light committees who need to approve the SOW.

You’ll find these types of internal battles at corporates all over the world. But Oz knows if he just gets the data dump from the database, he can clean up the data and get the business the reports and stats they need with his knowledge of Excel, but more importantly, his understanding of the business logic.

Build vs buy

At the very end we talked a bit about a podcast I listened to recently (see Other Podcasts section below) where the classic dichotomy between build vs. buy was brought up. The main idea is that software engineers are not always great at putting a dollar value on the time it takes to build an application (versus just buying the off-the-shelf version).

Like Oz, I agree that Excel and Google Sheets should be treated as development platforms. Oz talked about working on a consulting project where the client was paying something like $60K/year for an industry-specific software application. The issue was that his client was only using a fraction of the features the software offered. When you purchase expensive software like this, you may also need to purchase the customer support for situations where the software breaks.

Instead, Oz was able to develop a prototype in Excel that had just the features the client needed and was using from the expensive enterprise software.

So there are situations where building can be more beneficial than buying the shiny software that’s targeted for your use case and industry. Additionally, you become the customer support because you know the ins and outs of the solution you created which is an empowering feeling.

Other Podcasts & Blog Posts

In the 2nd half of the episode, I talk about some episodes and blogs from other people I found interesting: