Glenn Solomon

🏃‍♂️I'm a little late to the game, but I'd like to thank IMDb.com for featuring my Breakfast Club viz among several others on their website as part of the #dataplusmovies project with Tableau. 🙏 It's an honor to have my work alongside such noted Tableau developers. Links in comments. 🏗→🧠 Build to Learn! 💭🚶‍♀️🚶‍♂️ Follow for more. #tableau #data #analytics #VizoftheRay

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Very humbled and blown away to see Statistical Tableau featured as a book review from the Data Visualization Society. Huge thanks to Emmie Mercer, DBA, MSIS for the thorough review. How are you using #Statistics in your analysis? Let me know in the comments below and check out her review.

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I have been utilizing and maximizing Tableau containers in building my dashboards at work but I have learned and appreciated it more during today's Next Level Tableau session with Andy Kriebel. Thanks, Andy and NLT-mates for the productive learning session! 🤗🙏🏼 #NLT #Tableau #containers

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Simplifying a model doesn't necessarily trivialize it. A former Capital One executive once challenged my data & analytics team to make predictive models relevant by rendering statistical models relatable. The concept was to reduce the overall quantity of independent variables and describe in plain-English language (and with simplistic charts) how each impacted the dependent variable. His thesis was that models are abstractions of reality and unless explainable managers would be reluctant to use them to make business decisions (and furthermore: the smaller quantity of variables reflected the fact managers control a limited extent of factors). Statisticians rightly complained that limiting the quantity of dependent variables impaired accuracy and sensitivity. But when carefully parsed we learned the models remained directionally correct (very strongly so) with tolerable loss of fidelity. The overall result was managers confidently understood the relationships and trade-offs between models’ “levers” and the effect on forecasted performance.

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Get an inside peek at Tableau Conference #Data24! Geekwire highlights the latest in AI and analytics, news from Tableau, interviews with the #DataFam, and more. Hear what's next for data and AI: tabsoft.co/4dL6xZF

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❓ Did you know that Tableau's character sort is case-insensitive?   ⚖️ That means that it treats capital letters and lowercase letters as the same.   👍 That's probably a good thing and a useful default.   😀 The (somewhat funny) problem that I ran into is that has my RDBMS (Teradata) has case-sensitive sorting.   📃Capital letters sort first, lower case letters later.   ❌ I was testing my dashboard with some queries against the source data and many of my metrics were off for the first row.   🔍 In the Teradata tool, the text fields were also being truncated for display as in the "Output" section of the image.   👁️ So initially, I couldn't see the sort disparity.   👍Once I saw it, I wrapped my ORDER BY with an UPPER(variable), which eased the comparison, and everything was fine.   📝 Sometimes it's the little things (assumptions) that cause problems.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #data #datascience #analytics #VizoftheRay

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If you haven't already, check out this tutorial from Playfair Data where Ryan Sleeper discusses the parallels between data visualization and story telling. There are so many compelling insights to be gained from this strategic approach to visual analytics. Check it out from the link in the comments.

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🚀 Monday's ETL Tip: Stop Being Gotham’s Batman 🦇 Let’s face it: if you’re spending your nights watching ETL processes like Batman perched over Gotham, you’re not the hero your data deserves. You’re just losing sleep. Here’s a fun exercise: 👉 Log every time you had to check if an ETL process ran as expected. 👉 Log every bug or mistake you fixed that could’ve been avoided with proper checks. 👉 Add up the hours (or tears) over a month. If that number is more than a handful, it’s time to stop babysitting your system and start building one that takes care of itself. Set up robust system checks, notifications, and fail-safes. Yes, you might need to take days or even weeks to come up with better solutions. Yes, you might even have to convince a skeptical manager/team leader. Here’s your weapon: 📊 data on lost productivity. Show them the hours you’ve poured into patching leaks and late-night "heroics." Remember: the real heroes don’t just fix problems; they prevent them. 🛡️✨

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Moving beyond data and into storytelling is essential to bring visualizations to life. Putting "intent" before "content" is key. Recently, I had the opportunity to learn valuable lessons from Lee Feinberg of DecisionViz at Philly Tableau Day. Thank you, Lee, for the insightful session! #DataVisualization #datastorytelling #TableauDay

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I recently attended MeasureCamp New York and had the pleasure of listening to speakers from a variety of data backgrounds. Initially, I approached the conference with some skepticism, given that my past data conference (not Measure Camp in particular) experiences were heavily vendor-centric. Yet, I was pleasantly surprised to find a consistent emphasis on data strategy across most presentations and discussions. This common thread prompted me to reflect on a significant transformation occurring within the data industry. When I began my career at the height of the "Big Data" era, conference discussions largely revolved around leveraging an array of measurement and data tools, with an overarching goal of amassing as much data as possible. Today, the focus feels different. Rather than being gluttons for data, data conservatism is being practiced. Where previously the majority of focus in solving business problems was on data collection and exploration, it is now spent on data strategy. This change is characterized by prioritizing the right questions, identifying the appropriate stakeholders to address these questions, and utilizing data as supportive evidence for decision-making and storytelling, rather than making it the central focus. It represents a more structured and resource-efficient approach to data utilization. As I work to foster a strategy-driven culture within my team (see supporting article in the comments), it was reassuring to see my beliefs echoed at Measure Camp. I am hopeful that data strategy will continue to gain prominence in conferences throughout the year and become a strategic focus in all organizations. #data #datastrategy #analytics #measurecamp

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🗽 NYC data leaders: we just added a few more slots for our Data Leader Happy Hour happening tomorrow, July 10th, at a charming new venue in NYC. Let me know if you're interested. Comment below or DM me at [email protected].

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Why your routines and systems aren't paying off. You know the drill. • You plan out a new project, weekly schedule, or new year's resolution list. • You jump right in with verve & commitment. • A few stages in, you hit a snag, get overwhelmed. • Progress grinds to a halt. The routine is abandoned. Where did it all go wrong? In high school physics, I fell in love with the concept of mechanical advantage. Wikipedia defines it thus: "Mechanical advantage is a measure of the force amplification achieved by using a tool, mechanical device or machine system." I was intrigued by the idea of a well-placed, small effort producing a magnified result. I now apply this concept to designing my routines. I focus on two aspects: accessibility and impact. We often refer to accessibility as relating to disability, but more broadly, I find that it relates to capacity. Many of our routines are designed based on: • an optimistic forecast, • how much we are able to do on a good day, • the maximum amount of effort we're capable of producing. But these conditions don't hold true every single day. What happens on a day with unexpected: • busy schedule and little time? • physical illness? • rough mental health? • emotionally draining emergency? • unshakeable procrastination? When a setback interrupts our rhythm or reduces our capacity, the optimistic routine falls apart. I design my routines for resilience, focusing on days when my capacity is not optimal. Key elements include: • Cost as little money as possible. • Cost as little energy as possible. • Low barrier actions that are doable when busy, on a very rough day, or at my weariest of body or spirit. This approach doesn't promote complacency or laziness. In practice, I work at high capacity on most days. But on the few days I'm not able to, my routine is a safety net, not an onerous chore. For impact, I aim for the smallest unit of effort with the highest effect. Think of tasks and actions as crowbars. I go for highest amplification of force. Examples: • Audiobooks. When my eyes are not up to it, I don't have to skip my daily reading goal. Lower effort ⇶ target met. • Sunsets. I guarantee quiet time & fresh air by sitting outside and watching the sunset daily. Free; low effort ⇶ meditation; clear mind. • Stretches. I guarantee movement with daily stretches. Even if I can't get my full workout in later, I eliminate fully sedentary days. Free; low effort ⇶ fitness; wellness. Discipline in your routine is as accessible as your expectations on yourself. How can you make your routine a little more accessible to you this week? Share below! -------------------- My name is Marissa and I am a data scientist. I help you craft decision pathways from your data with my analysis and insights. I am open to opportunities for asynchronous remote work. Career Momentum Assembly Awugo Julius Chiamaka Ikwuoma #30DaysLinkedinGrowthProgramWithCMA #CareerMomentumAssembly #Impact #Accessibility

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There's no "nice" way to say this, but it needs to be said. If you are looking to transition into the field of data analytics, please stop building your online portfolio around introductory projects that are a part of a skills-building course or a training website. It seems like the majority of portfolios I look at have the same projects with the same data resulting in the same data visualizations leading to the same conclusions. Data analysis isn't a step-by-step process, and showing that you can follow a step-by-step process from an online project does nothing to illustrate your technical skills and your critical thinking skills. If you really want to stand out from the crowd, first figure out what subject matter area you really want to work in. Next, find some novel datasets in that subject area, identify your own questions that you want to answer, and then answer those questions. Finally, put together a summary that illustrates not just what you did, but why you did it. Be sure to discuss the strengths and the weaknesses of the data, and comment on what you would do differently if you conducted a follow-up analysis. Looking for data? Check out Data.gov, the U.S. Government's repository of open data. There are well over a quarter million datasets available, so you are likely to find something that is interesting to you and that hasn't been done ad nauseam. https://data.gov/

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⚠️ New Viz Alert ⚠️   👀 Let's take a look at my take on this past week's #Makeover Monday.   I haven't seen anyone plot profitability over expectation, and doing so allows us to clearly rank which film gave the greatest return.   But what if the break-even point was different than the industry standard 250%?   And how does the profitability ranking compare to the ranking of other metrics?   ✔ This dashboard answers those questions.   🔗 Link in comments.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay Chimdi Nwosu Irene Diomi Harry Beardon

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The technology behind Databricks' #UnityCatalog supports a variety of business outcomes: faster innovation, cost reduction, compliance support and more.  Dive into some of the capabilities that make this possible, such as our data lineage, comprehensive monitoring + reporting, Feature Store and more.

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The Sensemakers, Keith Helfrich and Jonathan Drummey, leverage their combined 20 years of #tableau Conference experience to dissect and present their thoughts on current production direction. This one is a great full length conversation to watch on YT. #data #analytics #data24

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Most of the internet is generic BS. Gen AI is trained on the internet. Therefore, Gen AI produces an awful lot of generic BS. I'm going to submit this as an SAT question. Don't get me wrong - Gen AI is an amazing tool, but don't mistake using it for learning or for being original. My class finished this week, and I'm grading the last few assignments submitted late. There's a lot of Gen AI. You know how I can tell? 1. Lack of focus. A one paragraph answer is structured like (and as long as) an essay. 2. Generic answers. Technical details generic are unrelated to the problem at hand. 3. Lack of citations. Always know where your information comes from. I'm not saying anyone should cite sources in social media posts or business presentations, but the first two are very relevant. Gen AI is a tool to enhance creativity and learning, not a replacement for it.

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#datafam have you read The Benefits of Data Visualization for Data Scientists? This tutorial from Megan Spengler at Playfair Data is one that personally resonates with me for many reasons. Early in my career I saw so much hard work get shot down at the pitch because it was poorly presented to stakeholders. As data people it's our job to ensure we present our work in a way that resonates with our stakeholders and makes sense to the audience we are communicating it to. Time and time again data visualization can help get us there. Check out the tutorial from the link in the comments below.

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