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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I Started Learning Data... Now What? [Guest Article]

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I came across a recent new resource, the Data School by Chartio. They’re building lots of free materials on how to learn the basics of data analysis. If you’re still working on integrating data into your day-to-day work, Data School will be a nice resource and reference guide for you!

Below is a guest post from Matthew David on Data Literacy and the Data School


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Education in the data space is largely focused on helping individuals get jobs in data. They give you just enough information to get you past an interview. This typically involves entry level SQL knowledge, basics of analysis, and some proficiency with a Business Intelligence tool.

The Data School was created to pick up after people get jobs and start running into multiple people querying for data and conducting analysis. New problems emerge.

  • Analysis is affected by biases

  • Dashboards that a lot of effort went into go unused

  • People second guess your results

  • People want to be taught SQL

  • Data is messy and oddly represented

While these may not be asked in your interview these are challenges that every company who uses data faces. Solving these problems increases trust in an organization and leads to more informed decision making.

The Data School is a community driven library of free web books covering the data challenges companies face as they have more and more employees using data.

So far we have written:

Learn SQL - An interactive SQL tutorial to learn the syntax

How to Teach people SQL - Gifs and visuals to help people build intuition for how SQL works

How to Design a Dashboard - How to apply design thinking principles to the process of building dashboards

Misrepresenting Data - How people accidentally present false or misleading results

SQL Optimization - Common techniques for improving query speed and database performance

We are currently writing on Data Governance, Data Modeling, and the Fundamentals of Analysis. We write these books both internally and with the help of many data professionals. 

Check out our site: https://dataschool.com/

Join our community: Slack

About the Author

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Matthew David

Product Lead - Data School at Chartio

I am a Product Manager focused on making it easier to make decision based on data. With a lifelong passion for data, I’m focused on building great content for analysts who are getting started in their data career.


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Ep 29 - Ben Schein - Organizations Need More Data Curiosity

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Episode Summary

I don’t want to ever build anything that’s completely done. I want to leave that last mile unfinished because it enables lots of people to answer lots of business questions.
— Ben Schein, Vice President at Domo
Caroline Doye

What if you had all the best data lakes, ETLs data pipelines, and BI tools in your organization?

What if you had an amazing team of technical data experts capable of writing python, R, SQL, and proficient at data visualization and data storytelling?

That would be great, right? You’d have a well-run data organization! Except… maybe you wouldn’t.

On this week’s episode, we talk with Ben Schein, VP of Data Curiosity from Domo. What an awesome title!

Ben is on a mission to show that simply having skills and tools is NOT enough to a data-driven organization in today’s world. As a former data leader at Target Corporation, he saw that the quality of the business team’s questions really mattered. If they were curious, asked lots of questions, and sought out insights, those teams would be most successful in implementing a data driven culture.

Of course, data curiosity is a two-edged sword. If you can’t deliver on the questions, that’s not good either. Ben’s solution was brilliant… build your data products end-to-ALMOST-end, leaving the last mile available for the business teams to scale their questions (and their answers).

Check out the whole episode for some amazing tips and stories about empowering teams with data, and developing a “data curious” workforce!

More about Ben Schein

LinkedIn - in/ben-schein

Twitter - @benfrominn

Ben’s company - Domo

Links from the episode

Book Recommendation - The Idea Factory

Mentor - Paritosh Desai, Chief Data and analytics Officer at Target

Mentor - David Hussman, Founder of DevJam

Mentor - Jason Goldberger, CEO at BlueNile


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What kind of games do you play?

This might be a question you answer with chess, basketball, Call of Duty, Overwatch, tennis, poker, or Dungeons & Dragons. But, now if I ask you “what type of game is your current change or transformation effort at your organization?” What is your answer? Most people I have asked that question are not sure how to respond. We look at games as something we do for fun and leisure and sometimes will incorporate into a learning activity but we generally don’t consider games serious activity.

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There is the concept of finite and infinite games that James Carse detailed in his book “Finite and Infinite Games: A Vision of Life as Play and Possibility.” Carse’s main concept is that there are both: a) finite games; and b) infinite games. Finite games have fixed boundaries and rules and that there is someone that wins which results in the game ending. On the other hand, infinite games that have no fixed boundaries or rules and the purpose is to continue the game indefinitely with no ending.

One type of game is not better than the other. They both serve a purpose. But, it does matter what type of game is intended and played by those playing.

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Friction can arise if you are playing a finite game in an infinite-game way or vice versa. There are also “advantages” and “disadvantages” you will have if you are playing a finite game and others you are playing with are playing an infinite game.

Getting back to my question above, most change or transformation efforts in organizations are treated as a finite game. That is there is a start and an end and there are rules laid out with an objective goal in mind. Accordingly, an organization’s players, namely employees and other stakeholders, look to play a finite game and win.

While treating change efforts as a finite game clearly helps organizations plan and invest, does this approach hinder sustaining change? My proposition is yes. This is because sustaining changes like digital, data literacy, customer-centric, and product-oriented transformations have no finish. They need to be treated as a game that continues and without winners and losers but rather how an organization and its stakeholders benefit from playing the game.

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Maybe you buy into this concept and your next question is “how do I do it?” Here are some general things to take into account as you design your transformation game.

  • Establish leadership support for an infinite game approach.

  • Communicate and incentivize people involved in leading the transformation to design and support an infinite game.

  • Establish and support councils and communities that will help drive transformation and sustain it.

  • Establish metrics that incentivize and infinite game.

Thinking of your large transformations as an infinite game will help ensure they sustain and have maximum benefit. Good luck on your transformation game and hope it is well played.

No matter what happens keep playing and have fun at what you do.


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7 steps for getting started with data as a business person

Getting started in data can be overwhelming in today’s world. The hype machine of Big Data and Data Science makes it feel like you need to learn 3 different coding languages, 4 BI tools, and have a PhD level skill set in machine learning and statistics.

Case in point. A couple weeks ago, I was in Chicago doing a Data Visualization and Storytelling workshop. After the day-long training, an attendee asked if I could chat. She had been tasked with building some executive dashboards for her small organization and fell in love with data and driving more meaningful analytics. So she started doing some research about moving into the field. But after reviewing several job descriptions, she felt completely lost and pretty discouraged. There was no way she would qualify for those kinds of jobs without going back to school and spending countless dollars and hours.

I assured her there were LOTS of jobs out there for her skill sets, which included Excel, business context, communication, project management, and translating needs into requirements. But, those aren’t obvious to a person just getting started.

If you’re a business person and want to get deeper with using data here are my seven recommendations to get started:

Ignore the hype

There is a real need for great data scientists in this industry. But you’re not going to be one of those. At least not in the next couple years. The good news is that for every 1 data science job, there are 10 business analyst jobs that are just as critical. People who can translate the business. Who can project manage and communicate effectively. Who can drive change management and adoption of data-driven approaches. Those are the jobs for you.

Identify and focus on your unique talents

Focus on the skills you DO have, not the ones you don’t. Perhaps you have an accounting background. Maybe you are great at training people or speaking? Do you like listening and empathizing with your peers and leaders? Find what makes you, uniquely you. I bet your organization will benefit from combining whatever that is with data.

Raise your hand for data-related projects

You DO need some understanding of how data works, and you need to prove it. I once hired a person with virtually no professional experience over a person with 6 years of data 7 analytics experience. Why? Because the no-experience person during a summer internship had raised her hand to do some reporting, found Tableau, tried their free trial and got the whole department to start using it. Organizations want people like that.

Pick a tool

There are a mind-boggling number of data and analytics tools out there. Nobody can learn all of those tools. Pick one and get started. Most of them behave similarly enough that once you learn one really well, you can pick up most of the others well enough. Remember, you don’t need to be everything to everyone. Get good at one thing and build to the rest.

Take a class or workshop

You don’t need to go back to school to be a great analyst. But you can easily pick up some of the basics through cost-effective online or in-person programs. Udemy, LinkedIn Learning, Udacity, and Coursera all offer good self-paced programs for reasonable prices (stay away from the “data science” programs to start). Want a more guided and personal format that’s still cost effective? Check out our programs from Beyond the Data, which are tailored to business professionals like yourself.

Consume lots of blogs & podcasts

There are so many great free resources out there from leaders. Just start reading and listening. You’ll pick up all kinds of useful information on how to become great at using data. Blogs like Storytelling with Data, Flowing Data, and Visualizing Data. Podcasts like Data Skeptic, Data Stories, and Present Beyond Measure. We’re also pretty partial to our own podcast: Data Able.

Join a community and learn from leaders

Getting connected to other people like yourself is THE best way to jump start your new direction toward a data career. You’ll meet influential leaders in your local area who know the right people and can help you navigate your local market. I guarantee they will be willing to coach and mentor you along the way. That person from Chicago? She started networking and within a week had eight (8) different leaders reaching out to HER about their open jobs!

I can hear it now… “That’s all fine and good, but the job description says I need 3 years of SQL and 5 years of Python!” Yep. HR put those requirements on the job description. One of three things will happen if you apply:

  • The hiring manager actually needs those specific skills and you won’t get the job. You probably don’t want a job that technical anyways.

  • The hiring manager is willing to overlook some or all of those technical skills because teaching technical skills is way easier than teaching people/business/soft skills.

  • The hiring manager just copied and pasted the job description from a different role and doesn’t really care if you have it or not… they were just trying to weed people out.

My point is don’t let yourself be stopped by silly things like “x years of z tool.” You have unique talents that you can bring to the table. Fuse those talents with data, and you’ll be a data rockstar in no time.

Good luck and happy analyzing!


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