Brandon Christensen

❓ Have you ever wanted nulls in Tableau histogram bins on the right?   The default is on the left.   In my use case, that didn't make sense.   I am showing the count of days of delay between two events.   So time increases to the right.   To me, it makes sense for the events that never completed (null) to appear on the far right - beyond the longest delay for something that actually happened, not before the earliest events.   One way to do this is to: ✔️Create a calculated field converting that null to a value greater than your biggest bin. Like: IFNULL([Days Delayed],99) ✔️Make that a discrete dimension ✔️Put it to the left of your bin pill so it sorts first ✔️Un-show headers ✔️Clean up formatting (like alias the null to "Never")   You can see the progression in the workbook link in the comments:   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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I had a great time at the Tableau Conference 2024 (TC24) in San Diego this week. I'm excited about the continuous innovation within the product and the overall 'buzz' in the space of data, analytics, and AI. For those of you who aren't as familiar with Tableau, I thought I'd summarize a few of the areas that stood out. Building a COE - Tableau Blueprint Blueprint is a methodology that organizations can use to assess their current data strategy and identify areas for improvement. Tableau has introduced a new AI tool to facilitate the assessment process and gap identification in data strategy implementation. Accelerators Ready-to-use dashboards that you can combine with your data and customize to fit your needs, to help you get to data-driven insights faster. My teams have leveraged Accelerators with fantastic results, rapidly delivering value and reducing costs while elevating our UX designs. Tableau Pulse Powerful newer product that takes Tableau Metrics to a whole new level with narrative and AI. Designed to democratize data analytics, it provides personalized insights to business users who aren't traditional data experts. The result is more intuitive and actionable insights for business users, that even integrates with apps like Microsoft Teams. Accessing On-Premises and VPC Data with Tableau Cloud Options for securely accessing external data from Tableau Cloud now include Direct Connect, Tableau Bridge, the newly launched Data Connect, and the upcoming Private Connect feature, each tailored to different security, manageability, and scaling needs. Tableau Bridge and the new Data Connect offer secure, scalable solutions for connecting to on-premise data without public internet exposure, with Data Connect providing Tableau-managed operations. It’s good to know there are several options to securely access data and maximize the value of your Tableau investment. AI - Einstein Copilot for Tableau Enables anyone to uncover valuable insights from their data and maximize their use of Tableau. Provides an AI assistant (available now in ‘beta’) that supports users throughout their data exploration and visualization journey, helping them create visualizations, generate calculations, and format dashboards through natural language commands, with a focus on increasing accessibility for users of varying expertise levels. Prioritizes data security and privacy with zero data retention, data masking, toxicity detection, and an audit trail for InfoSec teams. In my view, this is really the future of work, as it accelerates development and training, and enables SMEs to develop powerful data insights on their own. And on and on… There was a myriad of other innovations and great presentations – including in the keynote where Sophia G. introduced a great new data management cockpit. If your data, analytics, and AI capabilities aren't driving actionable insights and data-driven decisions, it might be time to explore Tableau. #Data24 #TC24 #insurance

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Powerbi jobs:get ready with analytics using Tally for high paying Powerbi jobs[more than 10 placements done] weighted average valuation SQL Server: https://lnkd.in/gpxiAUcv Our firm gets the Tally data into a single table and helps the end client understand how the Tally on fly calculations are done by bringing the same onto database. This has been done by studying the source TDL code. As a consultant you can then answer questions like: A.) How is sales number derived from raw tally data B.) How is purchase number from raw tally data C.) How is profit and loss derived from raw tally data. For example sales is defined from raw data as : Sales is Is active_vouchertype :Yes(excludes order vouchers), is optional:No(excludes any optional entries), is void(excludes void voucher):no,is deleted:no,is revenue:Yes(sales accounts should impact revenue acct),is cancelled:no,affects stock_voucher type:No(excludes voucher types like delivery notes, receipt notes),ledger primary: sales accounts. Understand how Tally gives sales numbers from raw Data. Contact apoorv :91-8802466356 for poc. Why Powerbi data module with Tally using Turbodata. https://lnkd.in/gvnaGxFe youtube channel:https://lnkd.in/dayHDj4J Sample reports link:https://lnkd.in/gNmVkWZx? Whatsapp channel: https://lnkd.in/gjfuHjwG Any bottleneck reports can be sent to our team(support@turbodatatool.com;support@mndatasolutions.com)

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So I never thought I'd run into a case insensitivity issue in PowerBI, but I did today. I couldn't figure out why I had two distinct rows in my matrix table. I was able to fix the problem in SQL before I even identified what it was. To troubleshoot, I copied the SQL output to Excel, and also ran distinct values. Both times overlooked a lower case "i" in the raw data. Finally the third time I looked at SQL and I found the difference which wasn't obvious in Power BI. For some reason PowerBI capitalized the abbreviation. Afterward, I found the following article blog post by Chris Webb #PowerBi #DataAnalysis #DataAnalyst

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Mike and I will land in San Diego tomorrow to represent Swift Insights at Tableau Conference this week! If you are going to be there looking for: 1. Help with your business data 2. Fully remote job opportunities 3. Or just want to hang out and talk data Let's connect at #data24!

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Great article about properly designing a great dashboard. The article is geared towards PowerBI, but could be used for any reporting/visualization tool.

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I often get questions from folks who are just getting started in data engineering about what they should do for a portfolio project. My answer is always find a dataset and topic you find interesting and will keep your attention. If you need some ideas about what to do with data, here are dozens if not hundreds of ideas from Snowflake on what you can do in their platform:

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Before settling on a final visualization, exploring the data and trying out many types of visualizations (not by-the-number) can uncover unexpected patterns and insights. This exploratory phase is a crucial part of the process. It's okay if you know you'll use a bar chart in the end- while you're exploring, see what you uncover with other types of charts on that same data set.

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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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Data modeling is a must for a successful organization. Those that do not do it or do not do it well have a data illness. It could very well be a terminal illness in today’s tech, business and data climate.

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Timing has a lot to play in things succeeding in life. In this Tableau #viz, getting the underlying data and dashboard ready, with a relatively short deadline has been a challenge. Not to mention with any public viz, knowing when to stop creating 'one more viz!' James 'Jimmy' Anderson is one of England, and arguably cricket's greatest bowlers. ball His retirement from the international game came at the end of England's recent win against the West Indies. 🏏 Publishing this dashboard following his retirement, meant a somewhat enforced personal deadline to complete, not to mention delivering before the English football team play in the Euro finals ⚽... The key ball-by-ball data source from the incredible CricSheet also not yet available for the last game, so a manual data collation to complete summary detail. This dashboard uses a range of different visuals to guide you through his career, using different #visualisations These visuals range from a simple bar and time series charts, to some experimental visuals ('Tree Ring' and 'Magic Eye' and the Digital Scorecard) that these passion projects lend themselves to. Whilst I've had this dataset and idea for some time now, taking inspiration from Maryia Maziuk's recent retirement visualisation, really accelerated my delivery of this. 🚀 https://lnkd.in/e7AEB_rZ As always, gladly take on your views and thoughts on this! #cricket #tableaupublic #datafam #england #anderson

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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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Excellent explaination of filter context in DAX. 🔥🙌 I think of filter context as the cool little function that helps sort through the noise to present you with clear, actionable insights based on metrics that matter most to your business. But it's not just about numbers; it's about the narrative they tell and the strategies they inform. More importantly, the measure is written once and it can be used across all visuals and drill through operations breeding consistency across reports and semantic models. Win win!!!!

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❓Did you know that you can sort by nested *Measures* within Tableau?   📅 I've been using it for 7 years, and my first need for it came up this week.   ⭐ Sometimes I think I know the product pretty well.   ⤵️ Then I’m hit with a functionality curve ball that really surprises me.   😀 In this case, I am happy that it works the way it does.   ⚖️Paradoxically, it is both intuitive and non-intuitive at the same time.   ☑️ It's intuitive for the novice, who expects an alpha sort to work like a numeric sort.   ✅ It's non-intuitive to the more seasoned analyst, who would expect to need to left-zero-pad the numbers for the order to be preserved.   🔎 I searched for a solution and found something like the following on Tableau's site. I thought, "That will NEVER work, and I'm going to show them!" STR(RANK([Sub-Category Count])) + '-' + STR(RANK([Profit Ratio]))   🤯 It worked.   ✏️ BTW, I always use single quotes in my Tableau calculated fields in case I ever have to convert them to SQL; it's one less thing to fix.   🤔 I was expecting it to alpha-sort like every other system I ever saw: 1-34 10-53 11-98 2-77   And within any repeating first number: 4-10 4-117 4-23   😲 But, no! Tableau alpha sort of dimensions detects and respects numeric values! It would work like this: 1-34 2-77 10-53 11-98   and 4-10 4-23 4-117   ❌ So I didn't have to create: 01-034 02-077 10-053 11-098   and 04-010 04-023 04-117   🔨 Anyhow, I made a dashboard to simulate my actual use case within SuperStore.   ⚠️ I DID have one case where I had to zero-pad to the same length if the numbers were all decimals less than 1.   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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Give infoFluency's new data strategy scorecard a try! It's designed to highlight areas of business where analytics could most move the needle for your organization.

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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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Dear Rising Data Star, Clarity is a sign of maturity. It is okay to lack clarity for sometime. This is why you need to find counselors in the line of anything you do, even so as a Data Professional. You need to have a network of people in different areas of Data Practice. Application of Data is complex, it involves putting together past or current events from different data points to retell a story for user purposes. You need a lot of people, subject matter experts in different areas of business depending on what you are trying to achieve. Becoming a sterling data professional is becoming a people person. Stay on the journey and take people along. I wish you the best. Omolola The Data Demystifier... May 2024

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Paul Marcombes has been doing a good series on Big Functions in Big Query. BigFunctions in BigQuery is one of the lesser-known but incredibly powerful features. Though not a secret, it's often overlooked due to historical concerns about inefficiency with User-Defined Functions (UDFs). UDFs have had a reputation for being suboptimal as they were not always tailored for specific tasks. However, BigFunctions changes the game. Leveraging a range of open-source libraries, BigFunctions adds a plethora of functionalities to BigQuery, making it a versatile tool for various tasks. From data generation and retrieval of App Store reviews to data cleansing and sending notifications, BigFunctions can handle it all. Here are some of my favorite functions within BigFunctions that I frequently use and highly recommend exploring: - Faker : Use Open Source Faker library to generate fictious data - get_latest_partition_timestamp : Retrieve the timestamp of the latest partition in your dataset. - Chart: Create visualizations directly within BigQuery without having to export - Json2XML and XML2Json: Convert JSON data to XML format and vice versa. - ROC_AUC and ROC_CURVE: Perform Receiver Operating Characteristic (ROC) analysis to evaluate the performance of your models.

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https://lnkd.in/gSAcfbxa Part 10 of my series Why Data Science Projects Fail is available today on INFORMS Analytics Magazine. Tom Davenport once said that "Models make the enterprise smarter, but models embedded in mission-critical business processes and systems make the enterprise more economically efficient." That is the end game of data science models--- generating significant business value and economic impact, and to achieve that, repeatably, reliably, and sustainably, requires that models are deployed as systems. Today's installment addresses the most challenging part of implementing model-based systems: Getting from Sandbox Model to Production System. Part 10 encapsulates many aspects of data science project complexity that were addressed in prior installments such as data availability, project management, and change management, and introduces new dimensions that determine how long it will take and how much it will cost to get from a sandbox model to a fully deployed and operational enterprise-grade production system. Levels of Dynamism, Integration, Mission-Criticality, and Problem and Model Complexity are addressed as four of the key determinants of how easy or difficult it will be to get your model from the sandbox to being a fully deployed, enterprise system, and integrated as part of the overall corporate technology ecosystem. The article provides examples of projects that span the continuum of relatively easy and inexpensive to deploy to extraordinarily complex and expensive to deploy. Successfully deploying and operating data science models as part of enterprise systems requires a holistic, comprehensive team of not only data scientists, but also data engineers and analysts, business process (domain SMEs) analysts, managers and executives, cloud engineers, ML Pipeline & Ops engineers, change management pros, software engineers and system integrators to fully realize the benefits such models promise.

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