Ep 34 - Lori Silverman - What it take to drive analytics adoption

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Episode Summary

What is the goal of bringing data into organizations? People make tens of thousands of decisions everyday... data can create more intelligent groups that improve those decisions.
— Lori Silverman, CEO & Professor

Combining multiple disciplines can so often be a recipe for success. Some of the best inventions, ideas, and movements were started because someone from a different background came in to an industry, saw things through a different lens, and approached a problem from a completely different angle.

For Dave, that “combination” has been through Chemistry, Law School, Product Management, and now Analytics.

For Matt, that “combination” has been through Computer Science, Industrial-Organizational Psychology, and now Analytics.

Caroline Doye

For Lori Silverman, she brings a wealth of experience from her psychology background, but has also worked on many “up-and-coming” movements in the quality, organizational change management, and storytelling spaces.

In this episode, Lori shares incredible insights from 30+ years of helping organizations think differently, improve results, and “shift” the way they do business.

Lori is passionate about driving change and about helping her customers succeed in building data informed organizations. She’s also a consummate researcher, so she’s always looking at ways to combine research into “real-world”. We talk a little bit about the research behind an insight (literally called an “ahas"!”). We also talk about the research behind System 1 and System 2 thinking, developed by the amazing Dr Daniel Kahneman and Dr Amos Tversky, summarized in their book Thinking Fast and Slow.

If there’s one thing Lori does, it’s to inspire people towards becoming a champion for data. She tells us the story from early in her career, in Wisconsin working for a local hospital, where she identified a need, went out of her way to learn from great people on the topic, and then spent 2 years slowly building buy-in from various leaders to change how the organization thinks. This is exactly what data champions do! They influence their peers and get buy in from leaders to help facilitate the change they know is possible!

Check out the whole episode for lots of great tips and inspiring stories from a true thought leader!

More about Lori Silverman

LinkedIn - in/lori-silverman-700963

Website - www.partnersforprogress.com

Twitter - @LLSilverman

Lori’s Books - Business Storytelling for Dummies, Wake Me Up When the Data is Over

Heroes - Cassie Kozyrkov - Chief Decision Scientist at Google


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What are your Data Principles?

First off, you might be saying, “Data Principles, WTF?” Ok, think of Data Principles as your manifesto or by-laws in how your organization uses data. Data Principles could be about how you go about collecting data. How you go about sharing data within the organization. What your view of data in the decision making process. What are your views around Data Governance. What is transparent and what is hidden to your consumers, partners, employees, etc.

Wow, this is a lot you say. Let’s take a step back though and provide a little more context on Data Principles and we shamelessly adapted this concept in part from Ray Dalio’s Principles book that is a great read. Ray Dalio applied Principles that he used in his personal life and professional life. While we recommend you read the book we think the concept of putting together your Data Principles for three reasons:

  1. Process of developing principles helps engage in a meaningful conversation and ensure there is buy-in.

  2. Provides an immediate reference point so that everyone in the organization can understand how data should and should not be used within the organization.

  3. Better provides accountability in using data well in an organization and at the same time helping democratize the message around the importance of data.

There are certainly other reasons that these Data Principles matter. One important point when putting Data Principles together this should be done by a diverse, cross-functional, and cross-seniority level group. It can’t just be leaders. It can’t just be data people. A broad group of input and agreement on Data Principles is needed. Further, the result of this effort needs to be even more broadly communicated so that everyone understands the result.

The process of coming up with Data Principles is just as important as the Principles themselves. Don’t cut the process short and use it as an alignment and communication effort around how your organization cares about data and will use it strategically and ethically. Having an outside facilitator helping provide the Data Principle development sessions is often beneficial so that it is not too driven by one area or another of the organization. It is important that all areas feel and actually do have input in the Data Principles coming together.

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Now you might ask, "what should our Data Principles be?” That we cannot answer blanketly but instead each organization’s culture, maturity level, risk tolerance, and other factors come into play. What is important is that the conversation happens and the Data Principles result. One important point in coming up with Data Principles is don’t lie to yourself as an organization. Data Principles should align with culture or if different than culture than align with the direction that the organization is broadly going to put effort into shifting culture.

Data Principles are only as good as the effort you put into creating, communicating, and reiterating. Got your Data Principles defined and want to share then we would love to see them.


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Dave Mathias

Co-Founder of Beyond the Data
Passionate about Data, Analytics, Product Management & Communities


Dave has been advising clients on strategy and data for over 12 years, helping them elevate their products and services with data and analytics. His passion is bringing together people, process, technology, and data to make the world a better place.

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How Data is Helping Leaf Peepers Plan the Perfect Fall Trip

Happy Fall! Fall is my favorite season and living in the Northeastern region of the Continental US I look forward to chillier days and leaves changing. I enjoy seeing what Albert Camus describes as “Second Spring” where the leaves are a vibrant red, yellow and orange! The challenge I have every season is finding that right time to drive up to that scenic overlook to capture a picture perfect landscape of colorful leaves with just the right amount of blue skies. It looks like a data scientist, Wes Melton might have solved our problems by determining the precise future date that the leaves will peak in each area of the Continental US!

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Wes Melton along with his co-founder David Angotti have created the Fall Foliage Prediction Map. According to Melton, this is one of the only fall leaf tools that provides accurate predictions for the entire continental US. Looking at the snapshot above, a user can take the slider across the 12 week period to see when and where fall foliage will peak in a given region. Looking at the week of October 5th, we can can see patches of the Northern US hitting peak season like Maine, Vermont and New Hampshire. Another great area that might not come to mind are the aspen trees in Colorado. 

The story behind the creation of this map is interesting. At the time that they were starting their Smoky Mountains Cabin rentals six years ago, they were getting questions on the best times to experience the fall colors. They started conferring with meteorologists and the predictions were accurate. The advice was well received and they have been updating the tool ever since!

Autumn is a second spring where every leaf is a flower
— Albert Camus

To create the map, a complex algorithm was developed that carefully analyzes several million data points and outputs approximately 50,000 predictive data pieces. This data then enables the program to forecast county-by-county the precise moment when “fall peak” will occur. As time goes on and the algorithm is fed more data, it will only become more and more intelligent.

Some of the data points processed by the prediction algorithm include National Oceanic Atmospheric Administration (NOAA) historical temperatures, precipitation, forecast temperatures, and forecast precipitation; historical leaf peak trends; and peak observation trends.

I am excited about this story and this tool because it was birthed from customers looking to rent cabins and Melton and Agnotti went above and beyond to accommodate them. They didn’t stop at the Smoky Mountain Region but went on to answer this question for the whole continental US. They were also able to compile data points from several sources to piece together this aesthetically simple visualization.

Check out the map and learn a little bit more on how and why the leaves change color. I would love to hear about your favorite leaf peeping location!


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.

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

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

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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


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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.

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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: 

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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

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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.

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

Follow Allen:


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