Will Elnick

📝 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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I recommend this course for those looking to learn PowerBI quickly and effectively.

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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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✔️ 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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🤔 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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❓ Do you know how big your text is on the screen?   🤔 I was trying to decide how much outer spacing to use in a Tableau text box, and I knew had 12 point font.   You can work for years in data visualization and not know how many pixels that is - because that was the case with me!.   It turns out that 1 point = 1.333 pixels, so my 12 point font is 16 pixels (or 1/3 bigger).   That made calculating my padding a lot easier.   👍 I hope that helps.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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Watch Damien Bird's latest video demonstrating how to trigger a flow from a button in SharePoint for a selected item or file. The video provides a step-by-step guide on integrating Power Automate flows directly into SharePoint Lists and Document Libraries, including some code walkthroughs and tips on using Copilot to speed things up. 👌

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Is taking snapshots of analytic results part of your data engineering toolkit? The impact of analysis and actions is often underestimated. Measuring systems have a hidden effect on results that may seem intangible until you see the progress made. #analytics #datawarehouse #measuredbenefit

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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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What a simple yet effective way of breaking this down!

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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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📊 Ah, Excel… the gateway drug to data analysis. If we just look at numbers without context, well, congrats—you’ve got yourself a really nice spreadsheet. 👏 But let’s be real. That’s like saying, “Hey, if you put ice and salt together, it melts. Cool, right?” ❄️🧂 Sure, but why does it happen? What does it mean? The magic happens when you bring analytics tools into the mix and add context to those numbers. That’s when real insights come alive—because data without context is just noise. Ready to step out of novice mode and find insights that actually matter? Drop your thoughts on the best tools for cutting through the noise! 👇 #ExcelLife #DataInsights #AnalyticsTools #DataScience #DataWithContext

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#powerbi Scatter plots are really great check out SQLBI's video on them here by Kurt Buhler https://lnkd.in/gwCKHEmg #businessintelligence #dataanalytics

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🩺 The downloading of your dashboard data should be diagnosed.   🤒 Here are two symptoms to look out for:   ❌ People downloading data to repeatedly create the same chart. ❌ People cutting and pasting charts to create a new view.   Your users are just trying to solve a problem.   I love problem solving; that's a good thing.   Let's consider the first case though.   If they need data to create a 1-time, 1-off analysis, fine. That's what download is for.   If they are going to repeatedly create whatever they are doing in Excel, it should be in the dashboard itself.   Similarly in the second case, if they are creating a mashup view one time, that's fine.   If it's needed repeatedly, it should be in the dashboard itself.   The reasons in both cases are the same: ✔ Users will get the view faster. ✔ It will reduce possible errors in a manual process. ✔ Other people may also benefit from the new view. ✔ The ultimate recipient of the output can get it for themselves (i.e., self-service!)   Admittedly, there is always a tradeoff between expediency and getting something completed from a development backlog.   That said, if you want to detect these problems, attend customer meetings and keep your eyes open!     🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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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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🧐 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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Chances are you didn't invent the database. But there are still so many ways you can use one to automate what you do. Automating makes it simpler to get things done, gives your work more credibility (you took the time to automate it, so you show that your time is valuable), and primes you and prepares you to make incremental improvements. #automation #efficiency #productivity #dataanalytics

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