Steven Wyant

One of the first examples that most people learning about normalized forms in SQL is the "students" table. But what about ecommerce? There are different considerations, and most encounters with an existing system will have already made specific decisions about these. A general solution is going to include a lot of different data objects if it's to be sufficient, but these don't emerge fully formed from a platonic ideal of commerce. They have roots in both practical and legal aspects.

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A fun #datavisualization that doesn’t get much attention. I think it visualizes distributions in an easier to read format than box-and-whisker plots alone.

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Is it just me or is "shift left" a rather lackadaisical approach to data architecture? Shouldn't it be: "Approach your processes with a broad scope and a clean slate mentality considering which processes would work better to the left, which processes would work better to the right, and which processes should stay where they are." A simple "shift left" is like a management consultant recommending a simple "centralize your business." You know very well that next year the next consultant will be recommending "decentralize." You also know that both centralize and decentralize are valid but for different processes and different reasons.

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What a simple yet effective way of breaking this down!

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Do you speak data? The ability to be data literate is critical for everyone to progress in their career. Gartner reports that 82% of business leaders expect all of their employees to be data literate. That is why I was excited to speak on this topic to Best Buy's Emerging Careers ERG. Data Literacy is usually defined as “the ability to read, write and communicate data in context." It was exciting to experience the energy and enthusiasm of almost 200 young professionals eager to learn. Too often people misuse data by taking it out of context (if you are looking for an example listen to most politicians) - and I am hopeful that these future leaders at Best Buy will help transform our culture to making data-driven decisions. Data is often hyped as the "new fuel" or the "new currency" - but it is worthless if everyone does not know how to speak the same language. Thanks to Soren and the group for inviting me in. How are you helping your company speak the language of data? #dataliteracy #bestbuy #cdo #datafluency #datagovernance #cdo

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Do I need Compendium if a data catalog comes "free" (air quotes intentional) with my EMR? It's a legit question, and one that should be asked of anything that comes "free" with your EMR (a data warehouse platform, dashboard tool, etc.). Basically it boils down to whether you want to control your own destiny with a customizable, scalable solution that you can really make your own, or if you're willing to work under a lot of constraints. We wrote a post about this question—check it out and let me know what you think: https://bit.ly/3WC5ByR

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🤔 How many times have you reinvented the wheel in Tableau?   I thought the radial jittering in my IronViz qualifier was pretty cool.   But it looks like my IronViz mentor, Samuel Parsons, had done the same thing 5 years prior.   At least I settled on the same chart name as he did, so that's what it is - a radial jitter.   These are good for infographics showing a distribution that concentrates around a central point.   📝 In the near future, I hope to make a simple dashboard showing three variations of this chart type, including evenly distributed dots as well as dots on the periphery.   In this #makeovermonday case, I thought the idea of concentrated dots meshed well with firearms - I got the idea from seeing the bee swarms by Shangruff Raina and Sherzodbek I.   Anyhow, I adjusted the worksheet size so that the output is slightly elliptical to fit well in the target image.   ⚖️ I thought I'd go down the route of comparison to domestic purchases because I knew those numbers were high. They've been higher than 2022, but I used that year because it was the last year of the other range and I wanted something semi-current.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #makeovermonday #tableau #data #analytics #VizoftheRay

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🤔 How many times have you reinvented the wheel in Tableau?   🦚 I thought the radial jittering in my IronViz qualifier was pretty cool.   😲 But it looks like my IronViz mentor, Samuel Parsons, had done the same thing 5 years prior.   🤷 At least I settled on the same chart name as he did, so that's what it is - a radial jitter.   These are good for infographics showing a distribution that concentrates around a central point.   🛠️ I made another simple dashboard showing three variations of this chart type, including evenly distributed dots as well as dots on the periphery.   In this makeovermonday case, I thought the idea of concentrated dots meshed well with firearms - I got the idea from seeing the bee swarms by Shangruff Raina and Sherzodbek Ibragimov.   Anyhow, I adjusted the worksheet size so that the output is slightly elliptical to fit well in the target image.   ⚖️ I thought I'd go down the route of comparison to domestic purchases because I knew those numbers were high. They've been higher in the past than in 2022, but I used that year because it was the last year of the other range and I wanted something semi-current.   🏈 This is a Throwback Thursday viz!   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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What do we get wrong as data leaders? Predictability. Let me explain. When your business partners ask you how long it is going to take to build a dashboard, what do you say? Commonly, you'll hear a range (like 4-6 weeks). And, also commonly, something throws a wrench in this range and it has to be extended. What about when it comes to investment decisions? For how example, how much will this new data platform cost? We think it will cost $1 million, and then after implementation it was actually $1.75 million. Businesses (and business partners) crave predictability. Now the detractors will say "data is too complicated for this" or "it's impossible to forecast exactly how much investment we'll need." Guess what? I don't buy it. If large technology companies can forecast out their financials, and can set dates for product releases, why can't you? Predictability is a way that a data team will have greater credibility and more likely to be treated as another business team. The more that you can do this as a data leader, the greater success you will see. #data #dataculture

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🚨 NEW BLOG POST! It's certainly been a while, but in my newest post, I share a solution that helps Tableau users quickly filter tables and lists using high-impact, multi-filtering, parameter-driven buttons to improve user experience. Check it out #datafam! This post will also be shared on HealthDataViz, a Sellers Dorsey solution blog shortly so be sure to also subscribe for more Tableau and #datavisualization content. You can subscribe to it here: https://lnkd.in/eqU-_bvd

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The great myth of analytics is that there is some utopian future of self-service BI products that will collectively solve every business problem. And it's always just one dashboard away: "We're so swamped in ad hocs now, but after we finish this dashboard, it will free up so much time!" ...so much time for what? 🤔 Somewhere along the line, "ad hoc" gained a negative connotation. But is the future of a gazillion dashboards even one we want to live in? And how well do those gazillion dashboards answer the most valuable business questions? Some dashboards are super valuable, undeniably. The other 80% might be us forcing BI onto an ad hoc. 💡 Ad hocs are never going away no matter how much BI you build. And that's a good thing, because ad hocs often answer the most pressing and most valuable questions the business has. #analytics #data #BI

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🌟 What’s the one piece of data you understand better than anyone else—something overlooked but packed with potential? 🤔 Now, think about this: how often do you fall into the 90% of people repeating the same tasks daily—without even realizing it? Are those routines helping you unlock that data’s value or keep you stuck in the cycle? 🔄 The secret might not be in new tools but in rethinking how you use the ones you already have. Nimble thinking and a fresh perspective can transform even the most routine task into an opportunity for innovation. 💡 Is there one step you can take today to break free, spotlight your hidden insight, and turn it into action? #DataInnovation #BreakTheRoutine #StrategicInsights

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😱 I once made a really bad dashboard on purpose.   🏆 Then someone challenged me to explain how to fix it.   🔨 That was a lot of extra work, but I did it.   🖱️ I loaded it up with show/hide text buttons with explanations and links to external resources.   🚛 It's a truckload of data viz design knowledge in one place.   🔧 The dashboard has been adjusted after each of several presentations to university students in the U.S., Canada, and the U.K.   🏈 This is a Throwback Thursday viz!   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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While working through individual projects, making sure to take the learnings to build and iterate on the necessary foundations will allow growth in both the verticals and horizontals.

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Starting a new role is exciting—and challenging. One of the biggest hurdles when stepping into a new position is getting used to an entirely new data architecture and datasets, and processes. There are new systems, KPIs, metrics, and OKRs to learn and understand, all of which play a role in how the business operates and manages success. It’s a vast amount of information, and it takes time to truly grasp it, but that’s part of what makes this work so rewarding. What excites me most isn’t just one big project or initiative, it’s all the little tasks and opportunities to drive improvement across the organization. From enhancing patient care to supporting doctors and improving corporate functions, there’s a lot to tackle, and it’s almost overwhelming in the best possible way. I’m thrilled to be part of an organization that values data so highly and is committed to growth through data. As I settle into my role, I’ve been fortunate to meet so many new colleagues who are supportive, collaborative, and driven by a shared set of goals. It’s not every day you find a team so aligned and positive, and I’m grateful to be a part of it. 💡 To my LinkedIn network: I want to continue learning from all of you. Whether it’s insights on data and analytics, data engineering, or business functions like revenue cycle management, finance, and operations, I’m always eager to grow. I truly believe that being the best data leader means understanding every aspect of the business and how data can help drive success across every possible function. I’d love to hear your thoughts, strategies, keys to success, and even just some positive thoughts. Even better, teach me something new about your area of expertise! #DataLeadership #AnalyticsStrategy #GrowthMindset #Teamwork #BusinessIntelligence

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🧐 Every data project comes with its set of challenges. Overcoming such hurdles is part of what makes working with data exciting and rewarding. My mom said I loved puzzles when I was little. The pieces look a bit different, but tables, joins, DAX, etc. are just parts of a larger puzzle. #DataQuality #DataChallenges

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What do Diet Coke, Twizzlers, Night Shift, and Data Quality have in common? Well I learned about data quality from being the Night Shift Manager at a Warehouse where I processed returns. While drinking Diet Coke and eating Twizzlers, I hand jammed through hundreds or thousands of returns in a night, tracking down clues about orders from hand written notes from dissatisfied customers. In my eight week stint, I cleared a months-old backlog of unprocessed returns. The most important lesson that I learned? You can correct a lot of data quality problems with a little elbow grease, a few excel macros, and an excessive amount of Diet Coke (considering the time of day). Other things that I learned at that job included how to avoid paper cuts and to turn in my time card every night.

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Tech debt is like the government, it never gets smaller. You can include data as a part of that tech debt. As a data leader, what you don't want to do is go complete austerity and just get rid of everything that isn't standardized or a source of truth. Austerity isn't effective (for both data leaders and governments). You'll never have the time, resources or investment to fully get rid of tech debt. Instead, the goal should be to manage and mitigate it strategically. This means prioritizing high-impact areas for improvement and creating a roadmap for addressing critical issues over time. Managing tech debt is an ongoing process. It's about making informed decisions that align with your long-term business goals while keeping your systems robust and scalable. Most importantly, it is about maximizing value from your data assets. #data #dataculture

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