Aaron Wilkerson

🛑 ❗ Leaving technical names in your dashboard smacks of inconsideration for your audience. 👀 I’ve seen both inexperienced and more seasoned dashboard developers guilty of this underscore-toting offence and it makes me cringe every time! 😬 You can’t expect your business users to correctly guess what each graph is showing from alphanumeric-and-underscore soup. 🍜 Even for fairly straightforward terms like sales_num - the mature dashboard designer will ensure it is changed it to a properly capitalized and spaced business name label ---- before it ever rests on business users' eyes! 👩‍💼 👨‍💼 Check out my article for more info 👇

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In our latest Power BI tips series episode, we're exploring the power of Report filters. These tools are not just about narrowing down data; they're about sculpting your data to present a clear, impactful story. 📊✨ https://lnkd.in/gZgKFUHM

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Three essential steps in dashboard design I learned from baseball ⚾ Baseball games are long, needing some snack / bio breaks. 🌭🥜 A good scoreboard helps us catch up quickly when we return to the stands. Watching my teenage son's baseball games inspired me to reflect on how dashboard developers can learn from simple sports scoreboards. Here are three key lessons that can help you develop more effective dashboards: ⚾1 - Know the Rules of the Game Just as a baseball scoreboard makes sense if you know the game’s rules, your dashboard should align with your audience's mental model of the "game" they are playing. Ensure your data presentation is intuitive and meaningful by understanding the dynamics at play in your audience's success. ⚾2 - Figure Out How the Game Is Won Your audience is aiming for success, whether it’s expanding market share, nailing project milestones, or stopping the spread of communicable disease. Understand their objectives and "opponents" to create a dashboard that reflects their goals and helps them track progress effectively. ⚾3: Make It Easy to See Who Is Winning Present the data clearly so your audience can see who is "winning" and by how much. And don't forgot test your dashboard with new users to ensure clarity. If a user can't easily assess how well the team is doing based on your dashboard, you may need to rethink your design or KPIs. Reflect back on your recent dashboards. Can you tell who is winning and by how much? If not, follow the principles above and you'll be knocking it out of the park in no time💥 Read my full article on Substack 👇🏻 #DashboardDesign #DataVisualization #BusinessIntelligence #PowerBI #Tableau #QlikSense

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Building a Strong Data Team: My Insights from 10+ Years in the Data Field! 📣 For over a decade, I've been elbow-deep in the exciting world of data. And one thing I've learned for sure is that building a successful data team isn't about having everyone be the same kind of expert. The best teams win when everyone brings a different skill to the table, creating a well-rounded crew. Here's a breakdown of the key areas I've found to be crucial, based on my real-world experience. I have also provided course link in comments. 1️⃣ FOUNDATION SKILLS FOR EVERYONE: ✅ Python Programming: Think of Python as the common language many data folks use to work with data. It's a great skill to have, and there are fantastic resources to get you started, no matter your experience level. Even if you don't become a master coder, having a basic understanding is a huge plus. ✅ Data Warehousing Basics: This might sound fancy, but it's really just understanding how data is organized and stored. ✅ Product Analytics(Optional): If you want to understand how people interact with a product (website, app, etc.), this is your jam. It will teach you how to track user behavior and get valuable insights. ✅ Understanding Paid Ads (Optional): Data and marketing teams often work together, and knowing how paid advertising works can bridge the gap. It can help you understand the data coming from those campaigns. 2️⃣ BUILDING & MANAGING DATA SYSTEMS: ✅ Version Control: Imagine a "save point" for your data projects, like in a video game. Version control systems like Git and GitHub let you track changes and revert to earlier versions if needed. ✅ Automation: Let's face it, some data tasks can be repetitive. Automation tools like Jenkins/Airflow can help streamline those processes, freeing you up for more creative work. 3️⃣ DATA SCIENCE ESSENTIALS: ✅ Statistics for Data Science: Data is all about numbers, so understanding some basic statistics is key. ✅ Machine Learning 101: This is a powerful area of data science, but don't worry, you don't need a PhD to grasp the core concepts. The resources in comments will give you a solid foundation. 4️⃣ DEEP DIVES FOR DATA SCIENTISTS: If you're a data scientist who wants to specialize in a particular area, like machine learning or deep learning, there are tons of fantastic courses available. ✅ Supervised ML, Unsupervised Deep Learning ✅ Support Vector Machines, Hidden Markov Models ✅ Cluster Analysis ✅ Understanding Generative AI with LLMs The beauty of a strong data team is that everyone doesn't need to be an expert in everything. The true power comes from having a diverse group of people with complementary skills. Keep learning, stay curious, and be awesome!  For more insights follow me here - https://lnkd.in/gK2U8KtJ #LIPostingChallengeIndia #data #analytics #datajourney #datascience

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The Bygone Era of Legacy BI - Dashboards are Dead! If you've ever felt like you’re stuck in a time loop every time you open a dashboard, you're not alone. Dashboards, those flashy displays of KPIs and metrics, were supposed to be the knight in shining armor for data-driven decisions. Yet, here we are, years later, still shackled by their limitations. A stagnant adoption rate of 30%, an average report turnaround time of 4.5 days, and poor data experiences reported by 84% of frontline business workers make it painfully clear: DASHBOARDS are DEAD! ☠ Unpacking the Hidden Costs of Legacy BI Let’s get real for a moment. When was the last time your data team celebrated a dashboard? More likely, they’re cursing the operational nightmare it has become. Reports show that 92% of data workers spend their time on tasks outside their roles, while 68% of data teams lack time to implement profit-driving ideas. Your team isn't just buried in data—they’re drowning in it. Consider this: 50% of your data team’s headcount budget is wasted on remedial tasks instead of actual data analysis. Imagine hiring a Michelin-star chef only to have them spend their days peeling potatoes. The hidden costs of maintaining these dashboards extend far beyond just financials—they’re a productivity black hole. Read more in the link to CDO TIMES independent research on how you can leverage AI to achieve productivity gains and ultimately hand the keys over to your business teams to ask questions about your data and get petabyte scale data insight on an individual level. #aianalytics #aidriveninsights #cdo #datastrategy #cdotimes #AIstrategy #digitalstrategy

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Measuring the performance of promotions is critical for EVERY business. It's THIS EASY TO DO in Tableau with set actions. Want to learn how we did this in Monday's masterclass? Keep an eye on my feed FRIDAY for a very short window to purchase the recording and materials.

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In this week's episode of Boy Meets Data (link in the comments), I share a story about a challenging situation for data leaders. What happens when you take on a new role and your entire team leaves within your first few months? That happened to me many years ago. Data leaders have large aspirations about how they'll drive business strategy with data, make them "data-driven," and do all these wonderful things. However, if you don't have a strong team to lead, your actual job will be much different. These situations aren't discussed at conferences or webinars. Here are some key questions to ask when starting a new data leader job: ➢ Who was promised a promotion but wasn’t given one? ➢ Who applied for your role and didn’t get it? Were they told why? ➢ Who is frustrated with the way things are currently operating? Enjoy the article. If you like it, be sure to subscribe so that you can receive the new ones as they are published. #whatdidIsignupfor #dataleadershipstruggles

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Data people need to have a lot of tough conversations about challenging topics with high stakes on the line. Here are two books that are worth reading to improve your skills at this: 1) Crucial Conversations: Tools for Talking When Stakes are High 2) Difficult Conversations: How to Discuss What Matters Most I'd start with Crucial Conversations, but I've read them both, and they are both great. Look at the summary on Amazon and pick the one that you think will fit you best. Data leaders - anything you want to share that helped you get better at high stakes conversations?

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I've been experimenting in getting Custom GPTs to run code I've written as part of their data analysis tools. The best solution I've found is to: 1. Upload the sample code as knowledge. 2. Tell it to import the code as part of the GPTs instructions. 3. Knowing that won't work, have it fall back to reviewing the code / copying it when the import fails. Sharing because I know a few people were struggling with the same problem, but also wondering if someone has a better solution.

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I've written before that titles are meaningless in data and all that matters is your capabilities. Here's a deeper explanation... I've been a VP with zero reports. I've been a Director with 100 data people reporting to me while owning the P&L of a nine-figure data business. "Lower" title, but way more actual responsibility. There are people who get a senior data title after 12 months of work experience. I know a "Senior Manager" who ran data strategy at a >$50 billion company, after previously leading data strategy at a >$100 billion company. And countless other examples... This is why in data, anyone competent will calibrate your seniority based on your actual capabilities, achievements and impact - not based on your title.

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Your Data Infrastructure (or lack thereof) Is Costing You BIG TIME This simple adjustment led to 110% more purchases and a 33% reduction in Cost Per Purchase. What was the change? Was it new creatives? Nope. Was it new copy? Nope. Was it simplifying their offer? Nope. The real game changer? Hiring our team of specialists to optimize the D A T A infrastructure & strategy Your Data Infrastructure is the foundation of ALL your marketing and growth activities & a robust first-party data strategy is crucial for your growth efforts. Here's the issue. . . Marketing and growth teams are stretched thin— managing clients, creating content, testing creatives, developing strategies—the list goes on. And what inevitably happens is that the very foundation of all of their efforts is left unmanaged. The consequences of neglecting your data infrastructure and data strategy include: > Erratic ad performance > Rising acquisition costs > Missed growth opportunities All of which directly impact your bottom line. After the release of iOS 14.5, brands have adopted a variety of strategies to overcome tracking and data challenges, typically falling into three main categories: Bucket 1: Ignore the need for effective first-party data systems & endure rollercoaster ad performance. Bucket 2: Use standard CAPI through platforms like Shopify. Better than nothing, but still limited and will never deliver optimal performance. Bucket 3: Rely on tracking, CAPI or attribution tools just because a well known podcast or thought leader recommends it. Spoiler alert. . .most of these tools are just glorified tag managers. Most of our clients come to us from Bucket #3—using tracking, CAPI, or attribution tools recommended by influencers or podcasts. When they put these popular solutions to the test against us, we've never been outperformed. Upgrade your data strategy today with audienceOS & join our clients who experience: ✅ More consistent ad performance ✅ Lower costs per acquisition ✅ Enhanced ability to scale Bottom line, an optimized data strategy & infrastructure are no longer 'nice-to-haves'—they’re essential for consistent, predictable growth. Don’t let your data infrastructure be your Achilles’ heel. Invest in your data infrastructure today & unlock massive growth and stability for your brand or clients. Comment below or send me a DM & let's see how much more profit we can help you squeeze out of your paid acquisition efforts.

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If you are a dataviz 📈📉📊 developer, PLEASE, be aware of the alignment of elements in your dashboard! Depending on your audience, you might be losing the attention to the insights because some very small details that are very easy to address. #dataviz #dashboard #powerbi #tableau

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Really like this concept. Data labels which only highlight key values will be coming soon on my reports.

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When I first started doing #DataGovernance I was really bad at it. I made almost every mistake in the book. But, here's the silver lining. I've made those mistakes so you don't have to. I can guide you on what truly works, what doesn't, and what can sometimes work, depending on your organisation's unique needs. Attendees of my Live Online Data Governance Training and Clinic can tap into my 20+ years of experience and learn the methodology I've developed. This will give you the building blocks to design a tailored #DataGovernanceframework that's right for your organisation. Find out more about what else you get from attending my Live Online Data Governance Training and Clinic by watching the video below. And you can find out more about my upcoming course here: https://lnkd.in/eSQJNWWA #tdgc

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When I first started doing #DataGovernance I was really bad at it. I made almost every mistake in the book. But, here's the silver lining. I've made those mistakes so you don't have to. I can guide you on what truly works, what doesn't, and what can sometimes work, depending on your organisation's unique needs. Attendees of my Live Online Data Governance Training and Clinic can tap into my 20+ years of experience and learn the methodology I've developed. This will give you the building blocks to design a tailored #DataGovernanceframework that's right for your organisation. Find out more about what else you get from attending my Live Online Data Governance Training and Clinic by watching the video below. And, you can find out more about my upcoming course here: https://lnkd.in/eSQJNWWA #tdgc

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'Data isn't easy. It's incredibly complex.' Yes, I'm just stealing Dylan Anderson's newsletter headlines for my posts these days. And data is complex. Yet, as with everything we overcomplicate it massively. And we do so in a variety of ways. Processes We have been doing 'data' for decades, and technology helps us scale and do things faster than we used to. But this means we now attempt to do everything. If you previously had to wait a day for your query to run, you made damn well sure it was worth running. Tooling How many ways can you complete a simple data task? And in how many different tools? Yet the fundamentals are still the same. Now we have to join a load of disparate systems together. But you can get another tool which can do that... Job Roles Anyone know where to start with this one? Dylan makes the point that in order to deal with such complexity we develop silos to specialise in each area. This makes the problem worse as communication lines fail. Data teams end up siloed and further away from the areas in which they can have a business impact. And then we overcomplicate things as this gap widens. Some processes require complexity. Most don't. Where are you making your work more complicated than it needs to be? Discover more of my posts by following hashtag #datatranslator #data #analytics #technology P.S. check out Dylan's newsletter, it will get you thinking

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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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