Matthew Kolakowski

❓ 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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Are you interested in... - Data viz tepid takes? - Slightly humorous example graphs? - Pie chart hate? If you answered "yes" (or even "maybe") to any of the above, then you should check out my latest GovLoop article on data viz!

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Whoever claims building RAG systems is easy has never built one at scale. 🤯 There is a significant gap in the literature and existing frameworks. RAG architectures are designed for POCs, not for hundreds of thousands of documents or supporting tens of thousands of users. Designing these systems requires a cross-functional team with software engineering and data science skills blended with a product mindset. Earlier this summer, I spoke at the Databricks AI Summit, highlighting guiding principles we are taking at CVS Health for building enterprise-grade RAG systems. Every company is unique, and while there is no one-size-fits-all solution, here are three principles that will help you on your journey: 👉 Spend time on discovery—It's tempting to dive in and start building immediately, but you may get lost in the desert without doing your due diligence and understanding what delivers the most ROI. Our team spent approximately a quarter on discovery. 👉 Design for modularity—The LLM space is changing rapidly, and you want to build reusable components that can easily be swapped out or upgraded when something better comes along. 👉 Humans + Machines—Technology alone can not solve the knowledge search problem. Develop processes and governance to help teams curate the data, which will be ingested into your system. These lessons are just the tip of the iceberg. Watch my full Databricks presentation and leave your comments and questions below to learn more tips and tricks. #RAGSystems #EnterpriseAI #DataEngineering #MachineLearning #AIArchitecture #AI #DataScience #LLM #SoftwareEngineering #AIAtScale #KnowledgeSearch #TechLeadership #AIFrameworks #AIInnovation #DatabricksAISummit #ProductMindset #CVSHealth

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We make decisions weighing out uncertainties and risks all the time… we need models to present their outputs with uncertainties to make fully informed decisions as we use them

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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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🤔 Have you ever heard of a Ziggurat Chart or a Chichen Itza Chart?   📖 It's not really a recognized thing…   I made it as a gag to tease fellow tableau developer who had recently made waffle charts and plum pudding charts.   I figured, why not try a Triangle?   🤪 It doesn't really work that well, but it *does* have 100 squares, and it's a weird way to show a percentage.   🏈 This is a Throwback Thursday viz!   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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Check out this article by my Brother-in-law! If you’re interested in data analytics then it’s worth a read.

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🏃‍♂️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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𝐃𝐚𝐭𝐚 𝐬𝐨𝐮𝐫𝐜𝐞 𝐭𝐫𝐢𝐚𝐠𝐢𝐧𝐠 - Just as patients in an ER need to be triaged to prioritize care and stabilize those in need, data science demands a similar triage approach. Not all data sources are created equal, and some might require substantial cleaning, analysis and transformations before they're usable. The 80/20 rule of data triaging means we are going to quickly determine whether a data set will be useful by examining some key factors. ●     First is Availability: Is the data public? Is it behind a paywall? Do you need special permissions and are there security concerns? Knowing major logistics barriers can prevent chasing a dead end. ●     Second is Costs: Data can be surprisingly expensive. Are there usage fees? Storage costs? Will you need to invest in new software to process it? ●     Third is Utility: Does the data actually contain the information you need? Beware of proxies - just because it's related to your topic doesn't mean it's the right fit. ●     Fourth is Frequency of Updates: If you're predicting a real-time outcome, then stale, annually updated data probably won't cut it. Find data with a time resolution and update frequency that aligns with your problem. ●     Fifth is Geographic Resolution: Studying zip code level sales trends? Nationwide data won't help. Make sure the granularity of the data matches your scope.   I always find it is useful to obtain a quick sample of the data. This quick look can often reveal formatting issues, missing values, and whether it's worth a deeper dive. Data triage is about making tough calls. Perfectly clean, well-aligned data is the unicorn of our field. Be prepared to say "no" to a source that looks initially promising but will take an inordinate amount of time to fix for minimal gain. Being resourceful is key! Don't be afraid to ask colleagues, search online forums, and get creative. Sometimes a great data source is hiding in plain sight. #data #datascience #datasources

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𝐃𝐚𝐭𝐚 𝐬𝐨𝐮𝐫𝐜𝐞 𝐭𝐫𝐢𝐚𝐠𝐢𝐧𝐠 - Just as patients in an ER need to be triaged to prioritize care and stabilize those in need, data science demands a similar triage approach. Not all data sources are created equal, and some might require substantial cleaning, analysis and transformations before they're usable. The 80/20 rule of data triaging means we are going to quickly determine whether a data set will be useful by examining some key factors. ●     First is Availability: Is the data public? Is it behind a paywall? Do you need special permissions and are there security concerns? Knowing major logistics barriers can prevent chasing a dead end. ●     Second is Costs: Data can be surprisingly expensive. Are there usage fees? Storage costs? Will you need to invest in new software to process it? ●     Third is Utility: Does the data actually contain the information you need? Beware of proxies - just because it's related to your topic doesn't mean it's the right fit. ●     Fourth is Frequency of Updates: If you're predicting a real-time outcome, then stale, annually updated data probably won't cut it. Find data with a time resolution and update frequency that aligns with your problem. ●     Fifth is Geographic Resolution: Studying zip code level sales trends? Nationwide data won't help. Make sure the granularity of the data matches your scope.   I always find it is useful to obtain a quick sample of the data. This quick look can often reveal formatting issues, missing values, and whether it's worth a deeper dive. Data triage is about making tough calls. Perfectly clean, well-aligned data is the unicorn of our field. Be prepared to say "no" to a source that looks initially promising but will take an inordinate amount of time to fix for minimal gain. Being resourceful is key! Don't be afraid to ask colleagues, search online forums, and get creative. Sometimes a great data source is hiding in plain sight. #data #datascience #datasources

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A big focus in the data world is on data literacy, and ensuring that anyone in an organization has a basic level of understanding and can use data effectively. This led me to check on regular literacy in the US. I was frankly shocked by some of the statistics: ✅ Illiteracy has become such a serious problem in our country that 130 million adults are now unable to read a simple story to their children ✅ 45 million are functionally illiterate and read below a 5th grade level ✅ Between 46% and 51% of American adults have an income well below the poverty level because of their inability to read ✅ Illiteracy costs American taxpayers an estimated $20 billion each year There are quite a few other statistics, and I've included them in the link below. The implications of low literacy (or complete illiteracy) has very negative consequences for those individuals, our economy, healthcare, etc. We've come a long way as a country when it comes to literacy, but there is still room for improvement and we should be focusing more of our education efforts on basic literacy. https://lnkd.in/gCURvMSf

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In microeconomics 101 (or 1001, for those of us whose school systems needed that extra digit) we learned that rational firms seek to maximize profit. And so us MMM providers tend to produce profit maximizing allocations as one our key prescriptive outputs. But the thing about "rational firms" is that they don't necessarily need to be maximizing profit _right now_. It is entirely rational to build a brand with a key audience in advance of a product being on sale, or to cut profit margins to under cut competitors in a category, or to focus on new sunscriver growth 'no matter the cost' if the strategic path to maximizing profit involves network effects or a known high re-subscription rate. Which is why when I advise on marketing allocation I prefer to directly align to goals the marketing team has bought into as part of the annual planning process -- instead of presuming to know what any given firm is trying to do, just ask them and then minimize the budget needed to accomplish that! #marketingscience

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The best data analysts aren’t just great with numbers, they’re great with people. Understanding the needs, challenges, and goals of your team and business leaders is just as important as the metrics and reports you create. This happened to me this week when I presented opportunities for metrics I thought might be important and the business leader met me with: "I need to do these other metrics first". Slow down. Listen before making suggestion or proposals. It's not that they don't need the metrics you are proposing, it's that some of the others are more important today. What’s one skill outside of data you think every analyst should have?" #DataAndPeople #LeadershipInAnalytics #GrowthMindset

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A new BIS paper by Gambacorta, Qiu, Shan and Rees finds that generative AI increases coder output by more than 50%, but that productivity gains are only statistically significant among entry-level/junior staff. In part, this reflects less frequent use of gen AI by senior programmers. This suggests that while generative AI can boost productivity, its adoption and usage patterns differ greatly based on experience levels. This highlights the importance of tailored strategies to maximize the benefits of AI across varying seniority levels in the workforce.

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Question for the R community. I am interested in adding telemetry to some of my data products (i.e. dashboard, sites, etc.). I played with <shiny.telemetry> for shiny apps (which works great), but I am wondering if there is such a thing for Quatro (i.e. dashboard or documents). Has anyone come across such a solution, or heard of any potential development for telemetry in Quatro? Ryan Johnson Rachael Dempsey Thomas Mock, PhD Hadley Wickham Wes McKinney Will Hipson Dan Reed Isabella V. Kristy Lee #datascience #datavisualization #quatro #telemetry

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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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