Jeff Sloan - Better Analytics through Product Management Principles

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“The best analysis for YOU might not be the best analysis for your USER. Make sure you think about the outcomes that you’re trying to drive.”
— Jeff Sloan

Today, on the podcast we’re trying something a little different. For those who may not know, Dave has been travelling through Africa and Europe the past 2.5 months as a part of a program called Remote Year.

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During his time in Cape Town South Africa, Dave was introduced to our next guest, Jeff Sloan who is also a part of Remote Year. Jeff is a self-prescribed Data Product Manager and they thought it would be fun to try recording some of their conversations.

They headed over to a local coffee shop and set up the mic and started chatting about data, business intelligence, product management and how these disciplines are starting to intersect.

If you hear banging dishes or cars driving by, feel free to imagine sitting outside sipping latte’s on a lovely warm day in an open-air coffee shop in downtown Cape Town, South Africa.

So, what is a “Data Product Manager”, exactly?

According to Jeff, this role is responsible for thinking about the data infrastructure of the entire organization, mapping out the flows, sources, and storage platforms for both internal and external data. It’s the first step in empowering things like Machine Learning, AI, and A/B testing across the organization.

 
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Jeff loves data, but he feels like many of the traditional ways that organizations use data and analytics today could be even better. That’s why he’s so interested in bringing product management concepts to data to deliver more value and better, more integrated insights. Product management tools like SCRUM, backlog grooming, and user experience can all play a role in driving more value.

“It’s important to understand where your [internal or external] customers are coming from. The more we can understand what they need and how they need it, the better we will be as data people… We need to take them along on this journey to using data.” How do we do this? Jeff recommends starting with the business question and then working backwards from there, in an iterative and agile way. This will ensure that the insight/analytics produced meets the needs.

Thanks for sitting down for coffee, Jeff. And good luck on the rest of your Remote Year travels!

More about Jeff Sloan

Links and References

 

5 Things Executives Must do to Support a Data Culture

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Executives today are faced with many pressing needs. Customer success, your P&L, internal politics, shareholders and investors, managing your teams, strategy and goals, keeping your key projects moving forward. But there’s a new growing factor to add to this never-ending list: Data.

You hear about it from Harvard Business Review. You hear about it from Forbes. You hear about it from McKinsey, and the Wall Street Journal, and CIO. You hear about your competitors doing things with data to get an edge.

In a previous article, we talked about the critical components of making data work in your organization. Hint: It isn’t just investing in a data science team and then waiting for profits to roll in.

Culture change must be part of the equation. Probably not what you wanted to hear, but that’s what it’s going to take. Changing your team’s culture takes several things, but the critical part we’re discussing today is the top-down approach.

Here are the five starting items that executives need to consider when implementing your data culture change.

Support a data-informed decisioning culture

This is first because without this then there simply isn’t anything else. Everyone in your organization must be on board to seek out data, learn from data, and make decisions based off analysis. A core tenant of hiring, promoting, and rewarding people needs to be off of strong data-informed decisioning. This applies just as much to executives themselves as their staff. Too often data is produced to back a gut-based decision and proper analysis and experimentation not performed. Then, when something does not workout then people raise their hands saying the data told them to do so but instead data just supported the desired outcome. There is a component to this item which requires that data and ability to access and analyze it must be put in place and maintained through proper data governance and self-service business intelligence platforms.

Support your Key data champions

Every organization needs data champions to keep your momentum going. You should have many of these data champions, embedded in the business lines, singing the praises of analytics and what data can do for them. Who are your data champions? Are they being recognized, rewarded, and empowered in their efforts? It is vital that executives understand that data champions are needed to drive data culture bottom-up.

Support data-informed decisioning technologies

It is no surprise that having appropriate data and analytics technologies available for not just the analytics teams but also the business teams is a must. Having the proper tools to do the job whether it is Tableau, Qlik, Power BI, Domo and others. That data and ability to access and analyze it must be put in place and maintained through proper data governance and self-service business intelligence platforms.

Support an information-sharing culture

It is not alright for departments to silo off data so they can benefit from it and other departments can’t. Yes, there are instances that data cannot be shared for various data privacy reasons. But, when data is shareable within an organization, the default should be to do it. It is not alright for departments and people to indiscriminately put up data silos against other areas of the company.

Support organization-wide data literacy

Data literacy is essential for all of your employees. This doesn’t mean that everyone needs to be a data scientist. In fact, there are different levels of data literacy needs depending on organizational roles. First, understanding your employees’ data literacy is essential. Then, helping those employees close data literacy gaps with training that is done in an engaging and practical way.

All these items are essential for executives to drive a data culture. However, it is really important to point out that executives must eat their own dog food. No longer is it alright for you to tell others to do what you say, now what you do. Demanding data in your own decisions and even getting hands-on with an executive level dashboard should be expected.

Taking these items and putting into practice will help create a data culture at your organization. Then, everyone will not only be speaking the language of data together and making decisions on analysis in a sustaining data culture.

This article appears in a series of blog posts about Data Culture, Data Literacy, and why it matters for organizations to think beyond Data Science. If you liked this article, make sure to read the rest of the series:

Five Reasons Why Data Culture is Just as Important as Data Science

The Key Roles of a Data-Informed Organization

Who is Driving your Data Culture? The Role of the Data Champion

Jordan Morrow - What Data Literacy can do for you

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“You can’t just throw data like spaghetti against the wall and see what sticks. Think about outcomes you want to acheive”
— Jordan Morrow

Jordan Morrow is on a mission to help organizations and individuals become data literate. He believes that the ability to speak, read and write data will be the next big differentiator in the next few years. He travels around the world speaking with people about how to improve their own data literacy.

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Jordan is also just really fun to talk with. We ended up chatting for a long time (we edited it down a bit for your sake) about Data Literacy and how it fits into Data strategy, Data culture, Data science, and how it can drive real, tangible outcomes for organizations.

We also talked about a real-life example of what Data literacy and good Data culture looks like. The Avon Somerset Police Force is enabling their staff and officers with data, helping them understand how to interpret the results and what to do with it, and it’s having a real, positive impact on how they do their jobs!

We asked Jordan how an organization like that could get to a point where they were changing culture. It boils down to three things:

1) It starts with a leader who understood the value of data

2) It requires training. Not just for analysts sitting back at the home office, but for the officers out patrolling the streets each day

3) It requires communication, roll-out and adoption plans to ensure the culture change “sticks”

We talked a lot about an outcome-based model to make data truly powerful. Let’s start with what we want to achieve… “We want more sales”, “We want more return on equity”, “We want higher employee engagement”. Let’s start there and then bring data to the table to help solve that. The worst thing we can do is use data to confirm our own biases.

One of the cool projects that Jordan works on is the Data Literacy Project. As the board chair, he started this project as another way to help people and organizations become more capable with data (Dare we say… Data Able?). It’s a fantastic source of stories and tools to help YOUR organization get started.

We had so much fun talking with Jordan and can’t wait to have more chats in the future!

Thanks so much for coming on the show!

More about Jordan Morrow

Connect with Jordan on LinkedIn: in/JordanMorrow

Check out the Data Literacy Project: TheDataLiteracyProject.org

Follow Jordan on Twitter: @Analytics_Time

Links and References

Book - Can’t Hurt Me: Master Your Mind and Defy the Odds by David Goggins [Explicit]

Book - Freakonomics by Stephen Dubner and Steven Levitt

Podcast - Data Skeptic by Kyle Polich

Podcast - Joe Rogan Podcast [Explicit]

Podcast - Revisionist History by Malcolm Gladwell

Blog - Qlik’s Data Literacy Blog

 

Building a Data-cated Community with Kate Strachnyi

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Kate Strachnyi is a superhero.

Seriously.

She writes books, she works a full time job, she hosts the Humans of Data Science video channel, she’s a Udemy instructor for Tableau, she started the Datacated Weekly project, and she still has time leftover to be a mom and to run crazy-long marathons!

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

Dave and I were thrilled to sit down with Kate for a few minutes and learn about how she got into analytics and data science. Hint, it wasn’t her original career path!

We also learned a little about her role at her Big 4 consulting firm, where she works on executive reporting and analytics for the C-suite. She also helps drive Tableau and Power BI Self-service adoption across the different business teams. Her keys to getting people on board? Start with leadership buy-in and then simply show people the power of the tools. Software like Tableau and Power BI make it easy for non-technical users to jump in and start using their data.

“Show people that dashboards are not that hard to build. Once they see that there’s not much friction to get started, they’ll start using it.”
— Kate Strachnyi

Kate has a huge following on LinkedIn and Twitter (for good reason). We asked her about the community she’s built around data. She says that the people side of data is the most interesting part of being in our industry. Learning and growing together is far more interesting than trying to do it alone.

And boy has she done just that. And she encourages you to create your OWN community! As we wrapped up our time together, she shared her step-by-step process for creating your own analytics or data science public project to start sharing insights and learning from others. You’ll be amazed at what happens when you do!

Thanks so much for coming on the show, Kate!

More about Kate

Connect with Kate on LinkedIn: in/kate-strachnyi-data

Check out Kate’s Website: storybydata.com

Follow Kate on Twitter: @StoryByData

Links and References

Udemy - Tableau Visual Best Practices: Go from Good to GREAT!

Book - Journey to Data Science

Book - The Disruptors: Data Science Leaders

Cathy O’Neil - @mathbabedotorg

Book - Weapons of Math Destruction by Cathy O’Neil

Book - Lean In by Sheryl Sandberg

Book - Extreme Ownership by Jocko Willink & Leif Babin

 

When Is it Okay to Ignore the Data?

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Episode Summary

 
“Data is there to answer a question. But human intuition needs to play a role as well. Combining the two is the goal.”
 

There is no easy button for determining how to use your data. We like to think that data is this perfect, impartial mediator for human emotions and bad decisions. But really, the data is just a full of biases as your intuition and “gut’ is.

An analysts job is to understand what types of biases could potentially impact the output of your analysis, dashboard or model, and then ensure that the data users know the pros and cons of your dataset.

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

In this episode of Data Able, we’ll talk about looking at your datasets, making judgement calls about that data, and questions to ask of your data’s origin/source. We’ll also cover some real-life examples from our own pasts where data we used were suspect and how we handled them.

Most importantly, we’ll talk about some strategies for managing through the inherent bias in your organization’s data, and how the “manage” your end-users through that process. Getting executive buy-in and ensuring everyone is comfortable with the pros/cons of the dataset BEFORE you deliver the analysis is the key!

Links and References

Understanding Data Governance - CIO.com

Understanding the Types of Data and How They’re Captured - HBR.org