Steve W. Kim

#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel

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#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel

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#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel

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#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel

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#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel

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"Data is a precious thing and will last longer than the systems themselves." — Tim Berners-Lee In the data and analytics space, we often focus on the latest tools and technologies. However, the true value lies in the data itself and the insights we can extract from it. As professionals in this field, we must bridge the gap between technical innovation and business impact. Let’s continue to push the boundaries of what’s possible, but never lose sight of the business problems we aim to solve. Our work should always translate into actionable insights and tangible results. To business leaders and other professional across organisations, embrace the power of data-driven decision-making. Invest in understanding the potential of analytics and data science to transform your strategies and operations. Collaboration between technical and business teams is crucial for harnessing the full potential of our data assets. Together, just like my team at Turner & Townsend and I, let’s build a future where data not only unlocks some progress but creates lasting value. How are you leveraging data to make a difference today? #DataScience #Analytics #Leadership #BusinessIntelligence #DataDriven

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#ArtificialIntelligence #DataScience #Technology 🎉👏🎉👏🎉 𝗧𝗢𝗣 𝟭𝟭 #BigData 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻𝘀 via Avi Chawla! 𝗜𝗻𝗰𝗹𝘂𝗱𝗲𝘀: ▶ 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗕𝗲𝗿𝗻𝗼𝘂𝗹𝗹𝗶 ▶ 𝗕𝗶𝗻𝗼𝗺𝗶𝗮𝗹 ▶ 𝗣𝗼𝗶𝘀𝘀𝗼𝗻 ▶ 𝗘𝘅𝗽𝗼𝗻𝗲𝗻𝘁𝗶𝗮𝗹 ▶ 𝗚𝗮𝗺𝗺𝗮 ▶ 𝗕𝗲𝘁𝗮 ▶ 𝗨𝗻𝗶𝗳𝗼𝗿𝗺 ▶ 𝗦𝘁𝘂𝗱𝗲𝗻𝘁 "𝘁" ▶ 𝗟𝗼𝗴 𝗡𝗼𝗿𝗺𝗮𝗹 ▶ 𝗪𝗲𝗶𝗯𝘂𝗹𝗹 Be sure to check out more gems like this on the blog for Daily Dose of Data Science! https://bit.ly/3Hz1cpt Enjoy, T. Scott Clendaniel/ #TScottClendaniel #AI #Analytics #BigData #DataMining #MachineLearning #Programming 

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In your Analytics career you can choose between 1. working within a single industry or domain, or 2. jumping around between different industries There are advantages to both! Sticking to one industry: - You probably get more replies when applying - You hit the ground running faster in a new job - Your domain knowledge compounds over time Jumping around: - Wider pool of job openings available to you - Get exposed to a wider variety of tools & methods - On average, it helps you grow more as a generalist Based on your experience, what else would you add? And what do you prefer? #data #analytics #career

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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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This week I used a set in Tableau. In my case, I wanted a dynamic top N, with everything else as "Other". Sets are great for this type of scenario. I don't use these very often, but they are great for certain edge cases. Viz in tooltip grand totals being another edge case, where a set action can make this work. If you want to know more about sets and set actions, follow Andy Kriebel. He writes about these regularly; their number 1 fan. #tableau #sets

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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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Data-focused recruiters and hiring managers - what skills are you looking for in your new hires? What sort of skills and experience are you looking for? What sort of skills and experience are not so important? I've had some new graduates ask me about getting roles, and I've seen a few questions come up in other forums I participate in. Candidates are applying to role after role and mostly just hearing nothing back from the companies they're reaching out to. I know for our open roles we get a LOT of people we don't feel are qualified, but we do try and reach back out to let them know they were rejected. Are good candidates getting buried in a sea of other resumes? #recruiting #datascience #jobs #newgrad

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Fascinating article from HBR on the cost of misaligned goals as companies transition from medium to high "data maturity". Quote from the article: "Companies at all levels of data maturity... ...may need to pump the breaks [probably they meant "brakes" - CB] on further talent and technology investments and ensure better alignment between senior leaders and those tasked with more operational data roles." My own paraphrasing here: when organizations attempt to reach higher sophistication levels in their ability to handle data and analytics, the more important the alignment between leadership and actual work on the ground becomes. The authors include an "alignment checklist" which notably includes the simple question that probably resonates with anyone who has experienced misalignment of this sort - the need to "standardize and speak the same data language." This is something senior leaders should note as they mature their own organizations.

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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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🤔 Have you ever noticed that if you add borders to vertical bars, the base of the border gets cropped?   🔎 This happens on vertical bars with all positive values.   ❌ It doesn't happen on horizontal bars or verticals that go negative.   ⚠️ If you try a workaround like a zero line or an axis ruler, that doesn't show up either!   However, have no fear. Edit the axis, set Range to Custom, Fixed start to a negative that is about 2-5% percent of the maximum positive value depending on your bar size. You can leave the upper limit Automatic.   Let me know if you are aware of another solution!   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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🔥 HOT TAKE: INDEPENDENT DATA CONSULTING 🌶️🌶️🌶️ Every data professional should consider starting their own Independent Data Consulting gig at some point in their career-- Especially if you come from traditional Consulting. ... Whether it's building out a startup mind & skillset, Potentially 1.5-2X+ your overall income (on average) for decade(s), Or you desire additional job security & opportunities by Building out a sales pipeline (funnel) & client-base. ... Insightful Video from Benjamin Rogojan for those starting Independent Data Consulting (or considering starting): https://lnkd.in/gCv2kKB8 #data #consulting #dataengineering #datascience #businessanalytics

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🎉🧐 The Technician's MBA is Live! 🧐🎉 The first post of "The Technician's MBA" is live on Substack - https://lnkd.in/eG5vC4wp. It's more outlining what content to expect, but if you want to have read you can do so with the link above. If you want to subscribe its free to do so and you will get each new post direct to your inbox every Wednesday. I will also cross post here for reach purposes. #TheTechniciansMBA #ThoughtLeadership #SoftSkills #TechnicalPractitioners #Coders #Coding #Leadership #Mentoring.

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If you missed it, the Tableau Public June TUG recording is up! Patrícia Gogová, Jessica Moon & Chris Westlake did not disappoint. They all share amazing stories from their IronViz journey and insights into their equally amazing vizzes. Check it out. https://lnkd.in/eKH9K4dZ #Tableau, #DataFam, #DataVisualization, #TableauPublic, #VizOfTheDay, #DataViz, #Data24, #IronViz

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