Five reasons why Data Culture is just as important as Data Science

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Data Science is an amazing tool in the organization’s toolbox. It can provide immeasurable value when done well. Those in data circles have spent the last 10 years hearing about those success stories, reading example after example of the great things it can create. To be sure, there is still untapped value in that “oil well”.

And yet, while executives have been steadily investing more resources into tapping this seemingly endless well of value, we’re starting to see the cracks. Gartner estimates that by 2022, 20 percent of analytic insights will deliver business outcomes. Wait, what? If that’s the future, what does the current state look like?

Anecdotally, you see it too. The business is frustrated that they’re not seeing the returns. The CFO is starting to scrutinize those budget lines closer. The CEO is getting more impatient for results.

Is this a failure of the data scientists? Maybe we couldn’t afford the “good” ones. Perhaps they didn’t do enough data visualization or data storytelling?

Perhaps it’s a failure of the executives? They didn’t invest as much as they needed to get the return. Or, perhaps they weren’t fully bought into this new approach.

Perhaps it’s a failure of the business teams? They didn’t value what the data scientist could bring to the table. Or, perhaps they didn’t listen to the recommendations and insights being generated.

Or worse yet, data science just doesn’t provide the value we thought it did. Yes, people are actually asking this question.


Data is not a siloed activity

Separating the data teams from the business teams is a surefire way to never get value from your data

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I once worked with a brilliant analyst named Dan. He was a true data scientist before that term was popularized. A truly skilled mathematician, gifted with just about any statistical tool you could give him, he could quickly diagnose and solve the stickiest data challenges we had. I learned a lot from Dan early in my career.

But Dan wanted nothing to do with the business team he supported. Meetings with the business partners was a nightmare for him. He was clearly intellectually superior to those regular old business people. He knew what they needed and he was going to provide them with that. Besides, he was on the analytics team and didn’t report to them, so it didn’t really matter what they wanted.

Perhaps not quite as extreme as Dan, but I often come across this “Analytics vs. Business” mentality in analytics teams that I work with. And I think the root of the problem is that we’ve structured our teams in such a way that we’ve isolated them from each other.

How many of your organizations set out to build more analytic capabilities by hiring a “head of” analytics, then building out a team, then going to work building (or re-building) the data infrastructure? I’ve personally worked with a dozen Fortune 500’s that fit this bill. It’s not a bad place for a startup analytics group. Silo your efforts so you can focus on laying the groundwork. Afterall, you can’t extract value out of your data if you aren’t correctly capturing, moving and storing that data.

Unfortunately, this approach has a sinister downside. It creates an isolated bubble of data-related activities and projects. And the longer you stay in the bubble, the harder it is to push beyond that bubble.

Great data projects don’t happen because isolated data scientists are casually strolling through the data, looking for interesting tidbits. The best data scientists I know today build strong bridges between their team and the business team they support. They understand that the data is there to augment the business, and isn’t there as an end-all-be-all panacea.

 

Algorithms don’t add value, people do

The most accurate forecast model ever created adds no value if the business doesn’t do something with it

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A number of years ago, I was working for a company that was struggling. Sales were down, costs were up, and the industry was in the middle of a seismic shift. The company was caught in a bad situation and was unprepared to deal with it. To make matters worse, we had purchased several other struggling competitors as a way to “bring consolidation” to the market, but all it did was increase the number of low-revenue/high cost problems we needed to solve.

My data science team decided it was time to start leveraging our data to create some actionable tools that could start moving us in a better direction. We set to work building a complex algorithm that would create a series of benchmarks around our customer’s buying history, compare each customer against the benchmark, and expose each customer’s unique “hidden opportunities”. We reasoned that mining over 1 Billion records to find nuanced, customer-specific buying patterns would arm our sales team with critical insights about their clients that would help them target their conversations and drive sales. It was a brilliant plan.

How much increased revenue did it generate? None. We tweaked the algorithm for almost 3 months, adding depth, complexity and accuracy. Getting it “just right” was important. Then we gave it to the sales team who… did nothing. It was too abstract, too complicated. They poked holes in calculations. In the end, it never was rolled out to the sales team, and 3 months of great Data Science efforts were wasted.

6-8 years ago, the industry told everyone that Data Science was the answer. It told employees to rush out and re-brand their skills, or train on those skills. Data Science Bootcamps were created, Universities built new Masters programs. Coursera and Udemy exploded with online certifications. Don’t get me wrong, these are good things! Great things, even.

But what was missing from the R and Python training, the Hive, Spark, Tensorflow, and AI training, the Bayesian probabilities and the clustering techniques… was the reason we create these things in the first place. It’s not about the algorithms. It’s about the human behaviors that our data influences. That’s when data becomes valuable. If we’re not creating things for humans, with the goal to influence those humans in some meaningful way, then our fanciest and most sophisticated algorithms won’t add a drop of value.

 

Knowing what to focus on

Data can solve problems, but knowing which problems to solve is the real question

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Erin is the VP of Sales at your organization. Sales are flat and Erin is growing increasingly anxious about getting back to growth. You decide to take a look at the customer sales data. Is the miss coming from a certain product line? Is it a customer retention problem? Are new business efforts lower than expected? Perhaps deal sizes are shrinking? After some digging, you find that most of your sales are doing fine, but the annual revenue generated by 8 out of your top 10 customers has shrunk over the past 12 months. Erin is now armed with just the information she needs to address the problem and get the company moving in the right direction again.

But what if Sales are booming? Erin is likely just trying to stay above water with all the new deals. She has plenty of problems, but they’re quite different. Now she’s worried about how the Operations team is going to handle the influx of new orders. She needs to make sure that legal has time to review all the contracts. She’s looking into hiring new sales staff to keep up with demand. If you come to Erin with a customer analysis and breakdown of where she’s missing, it probably won’t be received well. She’s got too many problems already and doesn’t have time to fix this problem too. Erin doesn’t need NEW problems, she needs to solve NOW problems.

The point is, you could do the same analysis in both situations, and receive a drastically different response from the leader. That’s because data is only useful when it is being used to solve problems that your stakeholders care about. When there’s misalignment between the business and the data teams, you miss huge opportunities to leverage data.

 

Business teams don’t understand data

Don’t turn your marketing team into Analysts… arm them with data to become even better at their jobs

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Do you have that special person in your life that’s still slightly scared of their computer? Like it’s going to eat them if they turn it on? This is exactly how my grandmother feels. We finally got her to log into her email account once a week, but it took 3 years of convincing.

My grandma is a brilliant lady. And yet she still spends an enormous amount of time with basic tasks like maintaining her calendar, managing daily tasks, basic communication. The computer is there to help her, yet she keeps doing things the way she knows. She’s COMFORTABLE with her old process. She knows it, and it hasn’t failed her so far.

I would argue that most business lines have a similar comfortability with their own industry knowledge. It’s what has gotten them to this point in their careers, and they’ll be damned if a data person comes in here and tells them something different. It’s not that they think data is bad, or doesn’t add value. In fact, if you asked them, I bet 9 out of 10 would say that they need their business line to be more analytical.

But saying you should do something, and actually doing it are very different things. What is stopping them from taking a data-informed approach? It’s fear of what they don’t know. Using data gives up control and safety of their industry expertise for something foreign, confusing, and different.

The lesson here is that getting your organization to use data isn’t about better algorithms, more hadoop clusters, or even more dashboards. It’s about the business becoming comfortable with using the data they have and blending it seamlessly with what they already know.

Executives haven’t truly bought in

executives need to live it the data-driven mindset, building it into their plans, and bringing their teams along

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You’d be hard pressed to find an executive that hasn’t added data and data science to their list of yearly goals. They’ve read enough books, seen enough articles, and heard their colleagues and competitors talk about it to know that it can help them. So they dip their toes in the water… perhaps a bullet point at the end of their annual roadmap deck that says “oh yeah, and one more thing… we need to be data-driven”.

Wow a whole bullet point!? What a show of commitment! I’m sure your teams will jump right on it. I mean, they have no training in this, they don’t really know what “data-driven” even means, and they likely have a 10 other areas of focus coming out of that roadmap deck. But yeah, I’m sure it will get done.

Joking aside, most executives have likely allocated a bit of resources, but they’re not fully committed to the idea. To them, it’s still a nice thought experiment. But this is exactly the problem. It signals to their teams that they don’t need to take data seriously. A passing fad.

The unfortunate side-effect of leaders who keep data at an arm’s length, is that when the going gets rough, they are unlikely to rely on it in that critical moment. And they’re even less likely to ensure their teams rely on it. They’ll revert back to their guts. There will be “reasons” why we can’t rely on the numbers, especially when the numbers aren’t stellar. You will hear things like “You didn’t consider that our biggest customer doesn’t go through that [data collection] system”. Or, perhaps “we already tried analyzing that data and it was inconclusive”. Or, the worst “we already knew this information”.

The point is that an organization must have leadership on board with a data-informed culture. They can make or break whether the organization captures the value it’s seeking. Make sure your leaders are actually on the train, not just punching a ticket.

 

So What is Data Culture?

What is the solution to Data Science’s woes? A more HUMAN approach to data. One where the focus is on alignment between the executive teams, the business teams, and the data teams.

You want to be successful with data?

Do the hard work to ingrain it into the core DNA of your organization.

It needs to permeate how each employee thinks, that they have a voice in their heads asking “what does the data tell us about this?”.

When the business has a fundamental understanding of data, it allows them to speak a common language, often referred to as Data Literacy. This common language builds trust and encourages collaboration between the business team and the data scientists. It opens up the opportunity for them to ask bigger, more impactful questions because they know that they can even attempt to ask them. It ensures that they are more comfortable with a sophisticated mathematical solution to their problem, even if they don’t fully understand it.

But most importantly, it allows the business to bring data to the table, combine it with their deep domain expertise, and make an EVEN BETTER decision than they would have otherwise.

Want to use data more effectively? Align your data science teams with leaders and business teams to make sure they’re all moving in the same direction, and basically aware of each other’s needs and capabilities.

Create a culture of data, help your business team “speak the language of data”, and make sure the data team is tightly aligned with executive & business team objectives. Do this, and you’re all but guaranteed to see data project success rates well above 20%.


Beyond the Data is on a mission

We help high-performing individuals become champions for a more data-driven approach in their organization. We believe that data science is only part of the equation.

Getting value out of data requires building a culture that starts with YOU, is supported by executives, and trickles down to every front-line specialist in your organization.


 
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The Importance of Data Storytelling Pt 1

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Episode Summary

Storytelling has a long history and is one of our most basic ways to pass along knowledge.

Storytelling has a long history and is one of our most basic ways to pass along knowledge.

The tradition of storytelling to pass along knowledge and inspire dates back thousands of years. Much of our history storytelling was the main means of passing along knowledge. Even though we have fancy technology though storytelling is just as important today. We are inspired by leaders and orators that can tell engaging stories. 

This episode is the first of a two-part series on the importance of storytelling. In this episode we discuss why storytelling is so important. Facts are important, but human emotions are even more important. Simply putting facts on a page won’t necessarily elicit a change in a person. Storytelling helps a person relate to the information you’re trying to communicate.

How about a few tips for people to practice when storytelling? First, know where your story is going, and be able to summarize what the point is. “What is the moral of the story”? Second, re-framing the story into something that people understand. So rather than stating a bunch of generic numbers about how many items move through your supply chain, tell a story about a bag of frozen peas, and how it got from processing facility to your kitchen table.

In Part 2 of the Data Storytelling series, we’ll discuss more tips and tricks on effective data storytelling.

Resources and Links

Some great resources that can help you get started around storytelling include:


The Data Able podcast is produced by Dave Mathias and Matt Jesser, and made possible by Beyond the Data.

At Beyond the Data, we are on a mission to help high-performing individuals become champions for a more data-driven approach in their organizations. We believe that data science is only part of the equation.

Getting value out of data requires building a culture that starts with YOU, is supported by executives, and trickles down to every front-line specialist in your organization.

 
 
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Data Viz Made Simple with Kristen Sosulski

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Episode Summary

If a picture is worth a thousand words, then a data visualization must be worth far more than that - Dave Mathias

Meet Kristen Sosulski. She’s an Associate Professor of Information Systems and the Director of Learning Sciences for the W.R. Berkley Innovation Lab at New York University’s Stern School of Business.

Kristen also recently published a book about data visualization, “Data Visualization Made Simple, Insights into becoming visual”

Kristen is an absolute expert on Data Visualization and teaches data viz best practices for both NYU students, as well as through a certificate program. Her passion is in helping up-and-coming analysts use visualization to enhance their work, tell stories, and communicate effectively with data.

Data visualization is important because we can use it to:

1) Explore our data and understand it

2) Communicate well, especially with non-data-literate people

In the former, when you’re exploring your data, you want to use more rudimentary visualization tools like scatterplots and trellis plots. These are great for understanding variation or differences between dimensions. But they are pretty terrible when it comes time to present your findings.

Don’t make your audience work too hard
— Kristen Sosulski

For the latter, when using data viz for communication, stick to simpler methods like bar charts, line graphs, and maps. Preattentive attributes, highlighting the thing you want someone to focus on, is a really effective way to keep someone’s attention.

So how about highly designed visualizations like Infographics? Kristen wouldn’t say “no”, but she certainly wasn’t wild about them. The problem is that they tend to over-simplify the data that it’s trying to communicate. That said, there are some great design concepts that we can use from infographics when creating powerpoints and other presentations.

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While we cover some in-depth topics, it’s clear that data viz is for everyone, not just data science. Kristen covers the 4 categories of data viz tools, from basic excel or powerpoint, to advanced like R or Python.

We also wanted to learn more about Kristen’s new book, “Data Visualization Made Simple”. In chapter 6, we learned about ways to maximize retention of the reader. A critical piece to this is EMPATHY and being in-tune from your audience. You may go as far as drafting a survey so that you can understand the potential reader and make sure you’re designing to their needs. Don’t make your audience work too hard.

We wrapped up our conversation, talking about the future of visualization, and discussing how things like augmented reality and AI are already starting to change the game for data viz.

Thanks for coming on the show, Kristen!

More about Kristen

Check out her book on Amazon: Data Visualization Made Simple

Check out Kristen’s NYU Stern Class: Visualization Data

Follow Kristen on Twitter: @sosulski

Follow Kristen on LinkedIn: in/sosulski

Resources and Links from the Episode

Sankey Diagram

Trellis Plots

Understanding Media: The Extensions of Man by Marshall McLuhan

What I Talk About When I Talk About Running by Haruki Marukami

Amanda Cox from the New York Times’ “The Upshot” section

Science of Happiness Podcast

Data Science for Business by Foster Provost & Tom Fawcett

Edward Tufte

Nathan Yao

Stephen Few

Dona Wong


The Data Able Podcast is made possible by Beyond the Data

We are on a mission to help high-performing individuals like you to become champions for a more data-informed approach in your organization.

Getting value out of data requires building a culture that starts with YOU, is supported by executives, and trickles down to every person in your organization.

 
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How to Data Viz like a Pro Part 2

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Episode Summary

If a picture is worth a thousand words, then a data visualization must be worth far more than that.

In Part 1 of our two part series on data visualization, we talked about GOOD visualizations, what types of visualizations work better, when to use them and the like.

Today we’re talking about BAD visualization. When data viz goes wrong. And of course we have to start with the PIE CHART. As a wise friend once told me, “If a chart is named after food, then I don’t like it”.

If a picture is worth a thousand words, then a data visualization must be worth far more than that - Dave Mathias

If a picture is worth a thousand words, then a data visualization must be worth far more than that - Dave Mathias

We know lots of people don’t feel the same way about pie charts, so we wanted to discuss a bit about WHY it’s not a great tool for helping you tell your data stories. We won’t say you can’t use it, but make sure you know what it does and doesn’t do well. We’ll also hit on the “Data to Ink Ratio” which was pioneered by Edward Tufte and look at the pie chart on this ratio scale.

Finally, we wanted to talk about DESIGN when it comes to data visualization. Design doesn’t have to be colorful or frilly. Design can actually be minimalistic and utilitarian in form and function. The goal here isn’t to say that one is better than the other, but to ensure you’re thinking about your audience and how you want them to act after seeing your visualization.

If you’re creating something public and want lots of Shares, Re-Tweets and Likes, then a more infographic approach can work well. If you’re creating something for your CFO, tables, numbers and no-frill visualizations are probably a better way to go.

Resources and Links

Some great resources that can help you get started are Storytelling with Data by Cole Nussbaumer-Knaflic, and Makeover Monday by Andy Kriebel and Eva Murray.


The Data Able podcast is produced by Dave Mathias and Matt Jesser, and made possible by Beyond the Data.

At Beyond the Data, we are on a mission to help high-performing individuals become champions for a more data-driven approach in their organizations. We believe that data science is only part of the equation.

Getting value out of data requires building a culture that starts with YOU, is supported by executives, and trickles down to every front-line specialist in your organization.

 
 
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We Deserve a Better Paradigm for Professional Education

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We Deserve a New Paradigm For Professional Education

Providing new and innovative ways to deliver data training is one of the founding tenets of Beyond the Data

Providing new and innovative ways to deliver data training is one of the founding tenets of Beyond the Data

Higher education is in need of disruption. Decade after decade it remains essentially unchanged. An educator stands up in front of students and dictates knowledge. The students’ knowledge of facts, theories, and processes with occasional application are then tested.

Worse yet education has become increasingly expensive with students investing large sums prior to truly knowing what they want to do. Then, they go off into the workplace and in land of rapidly changing environments many times those skills become obsolete.

One of the founding tenets of Beyond the Data was to find a better way to provide the RIGHT skills to the RIGHT people at the RIGHT time. Starting today, we’re re-writing the rules on professional education

The building blocks of a new education paradigm

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Affordable

If this is going to work, then it needs to be affordable for both students directly and also for employers paying for employees’ education. We’ve seen the mountain of debt that students come out of school with. It can’t continue like this.


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Accessible

Students should have opportunity to learn no matter where they are in a convenient fashion. This means not having to drive long distances to stale classrooms. It can mean online classes, but it could also mean learn-at-your-own-pace type environments. Or more one-on-one scheduled mentoring sessions.


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Practical

If a student can’t apply the knowledge in some meaningful way RIGHT NOW, then what’s the point? Providing real problems that they are passionate about is what will create lasting skills that improve their careers. It is time to stop memorizing facts and to stop thinking in theoreticals.


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Continual

Learning doesn’t stop when you leave the classroom. In fact, it might not START when you enter the classroom. Learning takes time and requires doing, seeing, experiencing, and discussing. That’s why the lessons should be long-lasting, with the content always available to come back to… months or even years later.


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Communal

This might be the most important part. Learning happens in a shared space with others. When communities are created, ideas are shared, relationships are built and we become better with these people than we ever could have without. They push us to think differently, to reach beyond our limits. Community is the secret sauce that makes learning work.

Want to learn more about how we’re implementing these in the data education space?

Check out our Data Accelerator program for details


Beyond the Data is on a mission

We help high-performing individuals become champions for a more data-driven approach in their organization. We believe that data science is only part of the equation.

Getting value out of data requires building a culture that starts with YOU, is supported by executives, and trickles down to every front-line specialist in your organization.


 
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