How to Whisper to Data (and Executives) | Scott Taylor on The Artists of Data Science Podcast
On this episode of The Artists of Data Science, we get a chance to hear from Scott Taylor, also known as the “Data Whisperer.”
He has spread the gospel of digital transformation through public speaking engagements, blogs, videos, white papers, podcasts, puppet shows, cartoons and all forms of verbal and written communications. He has also helped organizations, such as Microsoft and Nielsen, comb through and organize their data for meaningful use.
Scott shares his “eight ‘ates of master data”, a set of rules to engage with master data in a meaningful way. He also goes over his tips for communicating with executives, along with important soft skills that are being overlooked by data scientists.
Scott is very articulate, and his passion for data and teaching are definitely evident in this episode!
Some notable segments from the show:
[10:08] What separates great data scientists from good ones
[11:32] The “eight ‘ates of master data” defined and explained
[17:04] How to communicate effectively with executives
[23:40] The biggest data blunder Scott has seen in the past year
[28:14] Important soft skills that data scientists need to develop
[29:54] Words of encouragement for those trying to break into data science
Where to listen to the show
Listen to the episode on Apple Podcasts, Spotify, Overcast, Stitcher, Castbox, Google Podcasts, TuneIn, YouTube, or on your favorite podcast platform.
Scott’s journey into data
Scott has been working in the data space for a couple of decades. His first role working with data was working for an organization where he is looking at location data about supermarkets. Before then, he worked in consumer promotion, where he was never quite satisfied. He is happy to be working with data, and helping people tell their data story.
[3:13] I think I was kind of hard wired for the master data taxonomy ontology space because my parents told me when I was a kid, instead of building with my Lego blocks, I sorted them. So I think if you sort your block, sort your toys, you’re got a chance to be in the data business, so anybody out there listening to your kids, sort their toys, encourage that. It’s not all about building. Sometimes it’s about making sure things are structured and organized the right way.
Where is the field headed in 2–5 years?
Scott tells us that the core value of the content is not going to change to the point where it is unrecognizable. Big data needs highly structured content to work. Regardless of what happens in A.I. and machine learning in the next few years, companies will still need highly structured data.
The stakes are getting higher. Companies understand the importance of managing data. If a company is complacent in some kind of legacy space, others are going to win from the data side
[6:04] I kind of take a sort of a dissenting view here a little bit because I don’t think the core value of this foundational content. Is going to change to a point where it’s unrecognizable. Kind of an awkward way to describe it there… Software comes and goes. Data always remains.The one point that I want to make is I think the stakes are going to change. I think the stakes are getting higher.
What will separate great data scientists from the rest of them?
Scott tells us what separates the great data scientists from the merely good ones is the ability to manage and govern core data assets. People tend to focus on the cool and sexy stuff. But if you build upon a weak foundation, it’s going to fall. Great data scientists build a foundation and are better equipped for long term success. It’s not all about the latest data science thing you’re doing, because you can’t do that at scale unless you’ve got great data.
[10:08] People tend to focus on the fancy, cool, sexy stuff. And if you build all that on a weak foundation, it’s going to fall. And I keep using that word foundation because it’s a great way to think about it. And it helps get the enterprise stakeholders, the business leadership who have to be engaged. Realize it’s not all about the cool stuff. It’s not all about the latest data science thing you’re doing, because you can’t do that at scale unless you’ve got great data.
Key takeaways from the episode
The eight ‘ates of master data
Hear Scott go deep on the eight ‘ates at the [12:57] mark.
Relate — build relationships with customers, supplies, etc. No relationship = no business
Validate — is the data real? Is it right? Is it deceptive?
Integrate — take the data sources and pull them together in some way
Aggregate — you need to aggregate the information up to the executives
Interoperate — this allows systems to talk to each other; how things connect to one another
Evaluate — How do we put this data into play? Think A.I., analytics, etc.
Communicate — You need to able to communicate your metics with others in an understandable way
Circulate — data has to be in motion for it to have value
Tips on communicating with executives
[17:04] Focus on the results from your findings. As proud as you might be about your algorithms and about all the mistakes you made that you fixed to get here, that executive team doesn’t care about that. You need to tie your results to some recommended action. Ask yourself, “how is my insight that I created or the opportunity that I discovered going to move the business forward?”
Legacy systems in a start-up
[21:43] Start-ups have the advantage of not having legacy systems. If you’ve got a set of data that you’re working with that’s becoming the standard for your organization, make sure you share that. Try to form consensus around some form of standards as early as you can.
Important soft skills
[28:14] You need to know how to communicate and listen effectively. You need to be able to communicate your idea through a story. I advise people to take a sales course, because that’s how you learn how to tell a great story with empathy, emotion, and emphasis.
People who think they don’t belong in data science
[29:58] You need to find your passion in data. Data science is the hottest space to be in, and that isn’t an original opinion. Data science can help businesses grow, improve, and protect themselves. I don’t think you need to do it all, but find your niche and find your expertise and then run with it. There is a lot of opportunities in this space.
[3:37] “It’s not all about building. Sometimes it’s about making sure things are structured and organized the right way.”
[7:11] “Hardware comes and goes. Software comes and goes. Data always remains.’
[16:11] “Data, to have value, has got to be in motion.”
[20:36] “If you’re a data scientist, you are the business….and it’s impossible for you to learn too much about your own business.”
[27:08] “…you’ve got to bring people from “I have no idea what you’re talking about” to “how can we live without this?” and that comes from telling a good story.”
The one thing that Scott wants you to learn from his story
[33:08] Remember the value, the foundational importance, the critical nature of master data. It is the most important data any organization has.
From the lightning round
The best advice Scott has ever received
“When you walk into a sales call, never give them the magazine.” What that means is, customers can’t listen and look at a product simultaneously. They can’t focus on two things at once.
What motivates Scott
Doing fun stuff. I love the reaction I get from people from my crazy stuff.
Advice that Scott would give to his 18 year old self
Get focused, and recognize where your true strengths are.
The book Scott recommends you to check out
“Big Data, Big Dupe: A Little Book about a Big Bunch of Nonsense ” by Stephen Few
Where to find Scott online
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The transcript for this episode can be found here.