Ep 32 - Cathrine D'Ignazio - Getting the Data Basics Right

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As an educator, I am always working with people who aren’t naturally “numbers people”. I believe that you don’t need to be a data scientist to effectively work with data.
— Catherine D'Ignazio, Data Educator

What does feminism and data science have in common? Well if you talk to Catherine D’Ignazio, quite a lot actually!

Caroline Doye

Catherine was in Minneapolis for the Eyeo Festival over the summer and Dave sat down to learn more about her presentation, some of the work she does as an educator, and about some of her side projects like the “breast pump hackathon” and the Data Literacy tool, “Data Basic”

Obviously we had to dive into the hackathon a bit more to understand exactly what that was, and how it came to be (it’s actually a really cool cause!)

But Catherine’s work in data literacy was what got us really excited.

Catherine co-created DataBasic as a suite of easy-to-use web tools for beginners that introduce concepts of working with data. These simple tools make it easy to work with data in fun ways, so you can learn how to find great stories to tell.

Dave also talked to Catherine about data journalism, something that Catherine spends a lot of time in. They talk about the mission of journalists to provide unbiased information, and how data can be such a critical piece of doing that well in the future.

More about Cathrine D'Ignazio

LinkedIn - in/catherine-d-ignazio-61a57ab1

Twitter - @kanarinka

Website - www.kanarinka.com

Passion Project - makethebreastpumpnotsuck2018.com

Data Basic (Data Literacy) - databasic.io


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Why We Should Be Excited About Data Literacy

Allen Hillery

Hello! My name is Allen Hillery and I’m happy to be teaming up with Matt and Dave to get you excited about Data Literacy. I’m a data champion who has worked with business and data teams throughout my career playing the role of ambassador and coaching them on how to better leverage data. I’ve had the opportunity to work in companies with varied data maturities ranging from reactive to more thoughtful on executing results. Like most of you, I aspire to work in a truly data informed organization where everyone is literate to understand the context of their data they’re analyzing and the value it brings internally and externally. 

So my question to you is - How comfortable are you with data? Does the thought of getting your hands dirty with data excite you or make you want to cringe? According to Forbes, there are 2.5 quintillion bytes of data created daily. If you think about it, data is a major part of our lives.  Each one of us, generates data as we move from google searches to shopping with a club card at the supermarket, not to mention data created by Internet of things. In the office, are you the go to dashboard expert or maybe you’re resident data whisperer who massages insights out of your analytics teams? 

Being data literate means you have the ability to read, understand, create and communicate data as information. We are on the precipice of an exciting time, as we have superfluous data available to analyze.  This data can present information that provides better customer experiences and enables your team to identify which segment would be best served by your products. While the amount of data being created can sound daunting, the evolution of the tools and infrastructure to help us navigate this landscape is intriguing! 

People aren’t going to go to BI, BI has to go to to the people.
— Nick Caldwell

Tech executive, Nick Caldwell said, “People aren’t going to go to BI, BI has to go to to the people. This is already happening in a big way.” The staggering amount of data that has been made available to us has hit a tipping point where data analysts have to enable non technical business partners to develop insights on their own. This trend has caused a shift towards more intuitive self-serve tools.  At the same time, the proliferation of opportunities to learn query language are seemingly ubiquitous.  

In addition to trends pivoting our work cultures to being more data informed, the growth and learning opportunities that will come from leveraging both data and data literacy have me really psyched!  Companies are beginning to realize the importance of investing in their employees’ data literacy. AirBnB is a shining example of investing in data literacy through the creation of their data university. This effort was made with the belief that every employee should be empowered to make data informed decisions. It took roughly two years to launch but one of the amazing results is a reported 50% increase in active use of their internal data platforms. Another benefit is that it frees up data teams to concentrate on more complex tasks. 

AirBnB Data University

Sharing success stories, like AirBnB illustrate the importance of empowering employees and customers with data. Think of all the apps and services you use right now. You’re leveraging data when you are booking that next AirBnB, searching Yelp for food recommendations and hailing your lyft to get around. BI is coming for you and you’re more acquainted with data than you realize. So maybe you’re the resident data wrangler on your business team who realizes that data is not as aloof or mysterious as you once thought? Maybe your knowledge of the business combined with your new found data sleuthing skills has put you on a direct path to being a data champion lobbying for more training? Then you’re at the right place! We’re here to reassure you that you don’t have to be a data scientist to be data literate! You just have to be open to getting your hands a little dirty with understanding how to leverage data!


About the Author

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Allen Hillery

Adjunct Professor at Columbia University,
Writer and Editor at Nightingale, a Medium.com Publication

Allen serves as part time faculty at Columbia University’s Applied Analytics program. He has extensive experience in developing and executing data analysis and integrating results into marketing programs and executive presentations. Allen is very passionate about data literacy and curates an article series that focuses on the importance of creating data narratives and spotlighting notable figures on how their use of storytelling made major impacts on society.

Follow Allen:


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Ep 31 - Tricia Duncan - Implementing Data Viz in Organizations

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The most data-informed organizations I’ve seen are the ones that have a plan, and that integrate data into their day-to-day, instead of using it as an afterthought
— Tricia Duncan, Data Luminary

As analysts and “data people” we often see all the amazing things that are possible with data, data science and data visualization. We research new tools, new technologies, and new approaches.

But we often work for organizations who are “stuck in their ways”, content with that excel table instead of a sankey diagram. This can be frustrating when you SEE the possibilities, but you can’t convince anyone to move in a better direction.

So what do you do? Is it you? Is it your organization? Is it the leadership?

In this episode of Data Able, we talk with Tricia Duncan who has been consulting on Tableau, Data Visualization, and new approaches for over 6 years. She’s worked with small, mid-size, and fortune 500s all over the midwest to help them implement data visualization best practices and truly “modernize” their approaches to analytics.

Caroline Doye

As someone who has seen all sizes and kinds of organizations, we were interested to see what kinds of roadblocks existed. Is everyone as averse to modern BI and visualization approaches, or is it just a select few?

What Tricia has seen, leads us to beleive that this is a common problem, not limited to any single team, industry, or size company.

One of her stories revolves around a Chief Marketing Officer who wanted to see some new marketing numbers. Tricia saw the opportunity, built an amazing dashboard, and was met with confusion by the CMO when delivering it back.

While her dashboard was likely “better” than what the CMO wanted, it didn’t match the intended ask. The valuable lesson Tricia (and we) learned was that its better to deliver on the ask, and “slow-feed” people a more visual approach. Give them a little bit more each time they ask for something. Getting them from 1 to 2 on the maturity scale is far easier than trying to get them from 1 to 9.

Check out the whole episode for more great tips on how to help your organization improve their analytics maturity!

More about Tricia Duncan

LinkedIn - in/triciaduncan1

Links from the episode

Data Hero - Nick Pedersen - Planning Director @ State of MN

Favorite Book - The Model Thinker

Favorite Storyteller - Octavia Butler

Favorite Podcast - Partially Derivative


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Do your metrics have a positive ROI?

Metrics are important but not everything needs to be measured in a data-driven world. It is easy to add a metric without really understanding the upstream and downstream consequences. That is why it is important that your metrics have a positive return on investment (ROI).

ROI is a simple calculation where the benefits are divided by the cost and if the benefits are greater than the cost you have a positive ROI. This might sound obvious but most of the time metrics are implemented in silos and upstream and downstream impacts are not understood and accounted for.

So what are the potential benefits and costs of metrics:

Potential benefits:

  • Enhanced clarity by leadership resulting in enhanced decision making outcomes

  • Changed behavior that results in more revenue generating activities or reduced cost generating activities

  • Benefit of not having to maintain other metrics and related costs if a metric is replacing one or more metrics

  • Reduced friction between areas with metrics that align teams or departments with organizational objectives

Potential costs:

  • Time spent in calculating metrics by individuals

  • Storage and processing costs in creating metrics

  • Time spent rolling out new metrics

  • Time spent communicating out and reviewing metrics

  • Changed behavior that results in less revenue generating activities or increased cost generating activities

  • Enhanced friction between areas with metrics that misalign teams or departments

Each metric that is implemented should have a clearly positive ROI. The purpose of calculating ROI is not just to come up with a precise measure though. We think calculating ROI related to new metrics is most valuable because it provides a process for deliberate thought around implementing new metrics and maintaining old metrics.

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Just as importantly as determining the ROI on new metrics you should make sure the ROI on existing metrics still is positive. While metrics are not something that should be changing all the time, they should be reviewed and updated in a deliberate fashion.

Hopefully this post helps you think more about your metrics and their ROI. Much of being a data informed organization is simply the critical data thinking that goes about aligned to desired organizational mission.

This is the first in a series of posts we have planned around metrics. We think metrics should be front and center in an organization that values data because metrics are something that people are already familiar with and use regularly. More importantly, we think good metrics that are well communicated can unite and accelerate an organization.

Looking to implement metrics & KPIs in your organization? Check out our latest workshop “Designing Metrics & KPIs That Work” in Minneapolis, MN on October 15th and in Chicago, IL on October 22nd!


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Ep 30 - Nadieh Bremer - Anatomy of a Great Data Visualization

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Episode Summary

Want to be a better data visualizer? Make lots of projects. Look for other people’s work and try to iterate on it. Pick something you’re passionate about and start making something.
— Nadieh Bremer, Data Visualization Freelancer
Caroline Doye

We hear a lot about people transitioning into a data science role.

But how many people have you heard who are transitioning OUT of data science and into something more artistic.

Meet Nadieh Bremer, an ex-Deloitte data scientist with a background in astronomy and predictive algorithms.

Nadieh is a leader in the data visualization space, but she didn’t always start there. After years of churning out “just another predictive model” she was in search of something that fueled her more creative side. And she found data visualization! She didn’t realize just how powerful and needed these skill-sets really were.

Nadieh now does data visualization work full time through her company, Visual Cinnamon. She has won data visualization awards for her work in such publications as Scientific American, The Guardian, World Bank and Google News Lab. We also highly recommend checking out her visualization on Lord of the Rings!

We asked Nadieh to walk us through her process for creating the Lord of the Rings project. Surprisingly, there was much more to data visualization then just creating a pretty chart! Much of the data that she needed to answer her question wasn’t available in a format that was useful.

Hear her describe the effort from start to finish, and learn how to create awesome visuals that both captivate and inform!

More about Nadieh Bremer

LinkedIn - in/nbremer

Twitter - @nadiehbremer

Nadieh’s Website - Visual Cinnamon

Links from the episode

Dataviz - Lord of the Rings Project

Project - Data Sketches: A Year of Exotic Visualizations


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