Ep 33 - Matt Anderson - The Link Between Librarians, Product, and Data

/

Listen to the Episode

Subscribe to the Podcast

Episode Summary

“A lot of product managers are looking at their sales numbers, but they’re not thinking broadly about how the data can provide a wider lense.”
— Matt Anderson, Product Manager

Using data is critical for every facet of the business. But none is more powerful and readily usable than in product management.

Product owners, product managers, product analysts, you name it. Companies who have taken the plunge into digital transformation and agile frameworks need great product people. And those great product people MUST rely on data to do their jobs.

Caroline Doye

Matt Anderson didn’t start his career in the product space… he started as a librarian! But he found his niche in product management and has been using data to help understand his customers, his product, and his vision to drive profitability and sales for his company.

In this episode, we talk about what data can do for business folks… both how to use it, and how NOT to use it.

More importantly, Matt talks about his unique approach to collecting data that feeds the questions he’s trying to answer. This is different than the typical approach of “use whatever data you have”. Instead, he’s thinking strategically about what data he NEEDS, then goes and gets that data from his customers. He’s also passionate about QUALITATIVE data, not just quantitative. The user’s own stories are what provide the context that helps shape where the product can go.

My favorite story from our discussion was when Matt talked about using data to NOT make a decision. See, often times we think about data informing a decision… to take an action in some way. Matt found that his data collection efforts actually helped him steer clear of a decision that may have been costly.

Make sure to follow Matt on LinkedIn and Twitter, as he’s regularly writing about relevant data + product topics!

More about Matt Anderson

LinkedIn - in/matt-anderson-87988823

Twitter - @MattAndersonUT

Website - www.mattanderson.org

Heros - The folks from the New York Times Graphics

Favorite Book - The Lovely Bones by Alice Sebol

Great Storytellers - John Cutler, Melissa Perri, Theresa Torres

Would you like us to email

when new episodes air?

 
Cover+Art+-+Final.jpg
 
 
Dave-Mathias-Signature-Image.PNG
Matt-Jesser-Signature-Image.PNG

Data Literacy is Not One Size Fits All

/

“You don’t have to be a data scientist to be data literate.”

In fact less technical colleagues are welcome to help narrow the data literacy gap! As I mentioned in my last post, there are 2.5 quintillion bytes of data created daily. I love Chartio’s view of the current BI landscape - The world has gotten really good at collecting data, now the largest bottleneck is our ability to understand the data and make informed decisions based on it.”

“You don’t have to be a data scientist to be data literate”

There’s a lot of data to process and most companies have been hiring the most technically oriented people they can find to build armies of analytics and data science teams to analyze data. The one thing they have ignored is the data professionals’ ability or desire to communicate with a general audience.

“The world has gotten really good at collecting data, now the largest bottleneck is our ability to understand the data and make informed decisions based on it.” — Chartio’s view of the BI landscap

I’ve worked on several analytics teams and while I choose to champion the capabilities of data, I’ve seen my peers struggle working with business teams or fall short in explaining their analysis. I’ve also found the expectations placed on analytics teams to be unrealistic at times. Analysts are expected to wrangle data, analyze it in the context of knowing the business and its strategy, make charts and present them to business stakeholders with short turnaround times. Wash, rinse and repeat.

The bump in the data road lies right at the last mile - when it comes time to explain the analysis to decision makers. In a question on Kaggle’s 2017 survey of data scientists, to which more than 7,000 people responded, four of the top seven “barriers faced at work” were related to last-mile issues, not technical ones. Here they are in the word cloud below.

p1.jpg

I’ve experienced all of the above; I can assure you all that it’s not fun. What I have come to realize is that data is not just for the uber technical.  The opportunities in data can be harnessed by many with liberal arts backgrounds. Before you gasp in disbelief, hear me out. The identified bottleneck has been the ability to understand data and make informed decisions. Combined with the four barriers that have been cited above we need individuals who can narrow the data literacy gap by:

  • Framing questions correctly

  • Bringing together cross functional teams to work effectively in analyzing data

  • Communicating results to decision makers and the public

Analytics teams need all the help we can get at the last mile! While you may have believed that without knowing what an R package is there is no way you can contribute to a data project, you couldn’t be more wrong. When it comes to analyzing and presenting data, critical thinking is crucial. If you’re on the business side of the organization, you are closer to the key performance indicators that the company is striving to obtain. You could potentially  be a project manager with a proven track record of meeting deadlines. These are all skills that are much needed to drive data analytics pass the finish line!

One trend that has been growing in data driven organizations is hiring of liberal arts talent. These individuals possess a lot of the key skills needed for analysis - critical thinking and context setting. I like what William Cronon writes in his article, “Only Connect”. He defines a liberally educated person as someone who can: 

p2.jpg

Data folks, I’m not taking anything away!  The data presentation piece of the puzzle needs to catch up to all the advancements we’ve made in ingesting and processing data. These additional talents will complement our teams and the symbiotic relationship will advance our cause. In Scott Berinato’s article, “Data Science and the Art of Persuasion”, he points out that one of the steps to building a better data science operation is to define talent not team members. The core set of talent that Berinato describes is qualities I’ve seen in past teams. They include:

As Berinato pointed out in the article, there’s a difference between talent and team members. A team member can possess a few of the talents listed above. I know that I’ve strived to be an ambassador in my organizations and bridge marketing and analytics folks to move projects along. I’m also very passionate about presenting data insights as a story. 

About the Author

Allen.jpg

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.

You can sample his work here: Three Reason Why Storytelling is Important in Business

Follow Allen:

 
 

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

/

Listen to the Episode

Subscribe to the Podcast

Episode Summary

“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 Catherine D'Ignazio

 

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

Allen.jpg

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:

 

Ep 31 - Tricia Duncan - Implementing Data Viz in Organizations

/

Listen to the Episode

Subscribe to the Podcast

Episode Summary

“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

Links from the episode