Willis Jensen

I have to give props to Datacamp. I had been looking for helpful courses to provide some of my employees, especially analysts. Sometimes, people, even if they know SQL fairly well, have never been through a structured class on the steps to modeling data, drawing diagrams, or the factors that play into why you might choose or encounter a data model like data vault, stars, or plain entity relationships of transaction systems. The Introduction to Data Modeling in Snowflake was just the right balance of everything. I was surprised they even guided folks through a basic create and read from the three predominant models (ERD, Star, Data Vault). They even covered CTEs and subqueries, and pointed out the evaluation order of SQL commands so people can think through how much work a SQL server will go through to provide the answers. They even presented materialized views. The Snowflake course was not a magic wand to make you a master thing! I was not looking for that, but rather precisely what it was as a gentle and scoped introduction to the key aspects of dealing with data where Snowflake is concerned. The grrr part: * The Snowflake instance they used did not have the MATERIALIZED VIEW feature despite the exercise asking for it explicitly. ¯\_(ツ)_/¯ * The lesson checker sometimes failed to validate some of the answers successfully. This might mean that students may need some hand-holding or encouragement because their query will work in the window but not be accepted as a correct answer. Fortunately, they have feedback per screen, so when you get there, it might work! I would still recommend it.

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How much are people spending on platforms that do data lineage tracing? I was able to build a Jupyter Notebook that uses LLM services to read view SQL and trace the lineage back recursively to through logic, CTEs, subqueries, joins, nested views, and ultimately to the data dictionary to build a lineage JSON structure. That JSON then gets converted to a plain English definition of the field. Very useful! #GenAI

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I mentioned in a previous post that successful VBC organizations must strengthen their data pipelines in order to take advantage of analytics that can drive clinical results. To that end below are my 4 pillars of healthcare data pipeline design. Symptoms of a data pipeline with issues include delays in reporting, inconsistent values across your organizational domains, and a lack of trust from clinical users. 1) Raw Record Primacy - Healthcare data is continuously managed, massaged, and warehoused. When incorporating any healthcare data into your analytic structure favor data that is as close to the original source as possible. Trust fields from the billing data over fields from the warehouse (e.g. UB Type of Bill is of more value than an Inpatient Flag or ED Visit ID; MRN will track patient data through an EHR better than a payer member ID). 2) Transparency - Good analysts must understand the data that is being passed to them at the end of the data pipeline. To enable trust and understanding it is critical to provide accurate transparency as to how the data was processed at each step of the journey. Two techniques I have had success with include 'direct documentation' and data lineage. 'Direct documentation' is my term for using the same tables for both data processing and data documentation. There is no need to maintain separate documentation from your code stack as everything can be converted to a table (or file) driven structure. This prevents the inevitable disconnect between your documentation of the code and the actual operation of the code. This further allows for a data lineage engine that can walk specific fields from raw through analytic datamart to accurately explain how the analytic data was created. 3) Conceptual Design - Many healthcare datamarts suffer from concept agglomeration whereby mutiple fields accumulate over time that represent the same underlying concept. This frequently happens during data ingestion as engineers may be unaware of a field that already exists and mistakenly create another to serve the same need. This can happen at any point during data processing. Be ruthless in your conceptual design and create ontologies and hierarchies that organize the data into higher level concepts such that humans can drill down to the specific field they need when doing data mapping. A place for everything and everything in its place will prevent significant downstream confusion. 4) Fail as Fast as Possible - The key to this is to understand what your data processing algorithm 'knows' about the data at each step in the data pipeline. It is impossible to check PMPMs on raw data so don't design your QC process to only fail at the end. Check control totals and field gaps at the start. Once your data mapping is complete you can add field-specific validation. Your data processing algorithm should be learning about the data at each step of the process and the QC should be designed to fail at each step when possible.

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📝 Here is a Tiny Tableau Tip for Tuesday regarding workflow.   📃 If you are going to make a verbose entry in a tooltip with lots of editing, do it in a word processor and then paste it to your tip.   ❓ Why?   ⚠ If you accidentally hit Esc or the close X while editing a tooltip, Tableau simply discards all your changes.   😲 No prompt. No chance to undo.   👍 Hope that helps someone.   The example text in the simulated loss below is from a Tableau webpage regarding tooltips.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #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.   📝 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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🤔 Did you know that Tableau has no native way to move a reference line to the background?   😲 This is a bit surprising to me.   🔎 Consider bullet charts. They are very popular.   If you want to label the bar, no matter where you put the label, the value *could* be overlapped by the reference line.   That is not good.   I went with the simple solution (which is actually not pictured here): ✔ Adjust line opacity to 50% ✔ Darken the font of the bar label   There are other hacks out there involving dual axis. Feel free to research.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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I just discovered that Tableau calculations can use fields from different tables without needing explicit relationships or joins. This is incredibly handy for visualizations using a data template, such as a waffle grid table. Check it out to see this in action! https://lnkd.in/ghNz324T Tableau #viz

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Check out my newest blog post on how to color the ends of a bar chart in #Tableau. One of the 5 simple design hacks I shared during a recent TUG talk. https://lnkd.in/eS7EJAxK

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Tuesday thoughts: < 2 projects down for the week! Woo hoo! I have a ways to go in meeting goals, and the most challenging ones are ahead, but I'll take this as a "W". < As someone breaking into data analytics, I believe it's sound advice to have business knowledge, and that helps you stand out. However, when you're (kind of) new to this and not in a data job, it's hard enough to maintain and grow technical skills (have to stay consistently on top of it), but it's even harder to gain that additional knowledge and learn how to merge the business and technical. #dataanalytics #data #analysis #learning #motivation #support #goals

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Sometimes it's the simple things that bug me. Like default color legends. Out with the defaults and in with the customization! Check out my latest blog post on how to create custom dynamic legends in #Tableau. #dataviz #learntoviz #vizzendata https://lnkd.in/eSU2CykC

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Data & Analytics skills rank third in the most requested types of skills, accounting for 11.8% of job postings. This underscores the growing importance of data-driven decision making across industries. How is your organization leveraging data analytics to gain a competitive edge? What challenges are you facing in building a data-centric culture? Read the full report here: https://lnkd.in/ebsD2ikj Mitchell Martin Inc. #Data #Analytics #MitchellMartin

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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 often say that we slice data to the point of transparency, and so it is with our analysis of all non-Early Decision application outcomes. We link you to a viz on our Tableau Public page. Spoiler: the regular decision landscape is treacherous. https://ow.ly/1LnV50TsJK4

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Data is more than numbers—it’s a story waiting to be told. One of the things I’ve always loved about working in analytics is uncovering the narratives hidden in the data. Every dataset, no matter how complex or overwhelming, has the potential to reveal insights that can drive decisions, spark innovation, and create real impact. But here’s the thing: understanding data isn’t just about having the technical skills. It’s about asking the right questions and understanding the context behind the numbers. How does a specific metric impact patient care? What story does a revenue trend tell about operational efficiency? The value of data lies in how it connects to the people and processes behind it. Every day, I’m reminded how important it is to not only master the numbers but to build relationships with the people who bring those numbers to life. #DataStorytelling #AnalyticsLeadership #DataDrivenDecisions #Collaboration #GrowthMindset

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A great review of mine and Adnan Masood, PhD. book ‘Responsible AI in the Enterprise’. This is all becoming increasingly relevant as AI regulation becomes clearer globally and Enterprises are increasingly shaping up how they risk assess and govern AI. ‘Responsible AI in the Enterprise by Adnan Masood, PhD. and Heather Dawe was a great treatment of all the things that can go wrong in AI / ML projects, important principles in the design and operation of these systems, and ways of detecting and mitigating fairness issues in AI Systems.’ Thank you Matt Eland! https://lnkd.in/eWefyccD #responsibleAI #AIEthics #AI

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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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How did I not notice that Tableau Prep can't do spatial calculations before? Are they not in because they can't be "seen" in Prep? Why do I need them? Trying to do #IronQuest and get ready for Data + Maps Tableau User Group. I wanted to dynamically calculate the difference between the #F1 GP tracks to see optimal "next race" routing. Since Tableau (Desktop) can only do spatial calcs on a row level, I wanted to calculate distances between each and then PIVOT them so I can get a max/min. I can get the distances in Desktop but then I can't pivot calculated fields. This seems like it should be easier?!!? Sarah Bartlett Dennis Kao Tore Levinsen

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✔️ So you loaded your data into your viz tool.   🤔 Now, how do you start your analysis?   🪜 The 1st (and often skipped) step is to verify that the data arrived unscathed.   🧩 You may have had lots of joins across disparate sources that caused an issue.   🔧 Or maybe someone implemented a "fix" and didn't inform you.   Basic checks include: ✔️Number of Records of entire data source ✔️Universe of values within subsets of major dimensions (i.e. group by).    Same check within sub-dimensions ✔️Number of Records within subsets of major dimensions against known values in another tool (Excel, Python, R, SQL in database connection)    Same check within sub-dimensions (possibly as a crosstab) ✔️Validate at least a couple measures within dimensions - sums, means, ratios ✔️Trends include entire expected time range ✔️General scan for unexpected nulls   You don't have to do all of these every time, but simply forging ahead without any validation of whether the data arrived properly is guaranteed to bite you.   ⌛ It's just a matter of time.   ⛵ Happy Exploring!   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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