Julianne Thouin

Secret analyst habits: - Will rewrite an entire SQL process just because they didn't like the way it was formatted. - Say "it depends" more often than a therapist with a philosophy degree. - Believe "clean data" lives in the same place as unicorns, fairies and leprechauns. - Still genuinely thinks they'll get around to "doing the documentation later."

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I've said it before and I'll say it again: data teams are more like creative teams than technical teams. Try answering "Why is customer retention dropping?" with defined requirements, a fixed timeline, and a scrum master. What happens when your analysis hits a dead end and you need to pivot? That's the thing. Dead ends are part of the process. I want IT to get solid requirements. Start with a well-defined problem. Build something efficient and secure. I want my data team to explore the problem. Test different angles. Hit some dead ends and figure out what actually matters. The problems are different, so the approaches should be different. If you're applying agile methodology to your data team, stop. You're optimizing for predictability when you should be optimizing for insight. Data teams need creative management, not IT project management. Want more? Check the comments.

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Why do entry-level data roles ask for 2+ years of experience? Because it’s not just about technical skills. I rarely hire analysts straight out of school with no work experience. They can code, but they haven’t had time to learn how the business actually works. When people ask me how to break in, I usually say: 1. Get a job in the business. 2. Use your data skills to stand out. 3. Learn how decisions are made, what leaders care about, and how workflows actually function. Good data work supports decisions and fits the messy, human reality of how business teams operate. Think of it as School, Part 2: Learning how your stakeholders think.

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I keep seeing posts about the top skills to be a data analyst and they're all wrong. Excel ranks pretty high. SQL is up there. Python is usually a little lower down. IMO the top skill needed to be an analyst doesn't involve any math or coding - it's curiosity. If you're curious, you can be a good analyst with a pen and a notebook. The best part is when you transition into leadership, it's a skill that continues to serve you well. Tools come and go. Curiosity sticks.

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I made a post on Sunday that upset a lot of people and I'd like to clarify... Or maybe double down. Your first data role doesn’t need two years of data experience, but in my opinion, it does need some business experience, even if it’s unrelated. Here’s why: When you’ve worked a front-line job - frankly, almost any job - you’ve dealt with rules, policies, workflows, and probably a decision support system. And a lot of those systems are shaped by decisions made with data. ...Through dashboards and models supported by data teams. ...By people like us. Spending time in the business gives you a very real sense of what those decisions actually feel like. Sure, you know customer service can be frustrating, but there’s a big difference between “Yeah, that must be tough...” and “OMG, I remember a call where…” Data work impacts people. First-hand experience with that impact makes you a better analyst.

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Under pressure, language decides everything. One phrase can calm. Another can break trust. Program managers, in high-stakes moments your language becomes the project. Here are 10 phrases that can kill authority: 1. ‘That’s not my call.’ 2. ‘We’ll figure it out later.’ 3. We can’t change it now.’ 4. ‘Just trust me on this.’ 5. ‘This always happens here.’ 6. ‘That’s above my pay grade.’ 7. ‘It is what it is.’ 8. ‘We don’t have a choice.’ 9. ‘Let’s not get into details.’ 10. ‘This meeting is a waste of time.’ Language like this can erode confidence. Instead, strong PMs reframe: From: ‘That’s not my call.’ To: ‘Let’s track who owns this decision and get clarity.’ Great PMs don’t win with perfect answers. They win with presence, language, and trust. What else would you add here? ➕ Follow RAJESH MATHUR for more real-world tools to lead when the pressure hits.

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I didn’t hear about Tom Gilb in any agile training, product bootcamp, or MBA course. I stumbled across his work buried in a PDF someone shared in a nerdy corner of the internet. And I was floored. While the rest of the industry was arguing over kanban boards and sprint rituals, this guy, decades ago, was already asking the real questions: • “What measurable value will this feature deliver?” • “How will you know it worked?” • “Why are you building it at all?” Tom Gilb was out here writing specs that actually meant something, not “the system should be fast”. But: “Response time shall be less than 0.8 seconds for 95% of users under load.” He invented something called Planguage, a brutal but brilliant way to write precise, testable, no-bullshit requirements. And his Evo method is agile before agile. Small weekly deliveries, real stakeholder feedback, every increment tied to a business outcome. The guy was literally shipping OKRs before they had a name. Why don’t more people know him? Probably because he never dumbed it down. He didn’t rebrand his ideas with startup gloss or write books full of Post-it note metaphors. He just did the work and expected you to think. If you’re tired of pretending progress is velocity… if you’re done with vague specs and useless features… go read Tom Gilb. Then come back and write requirements your team can actually build and your users will actually care about.

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I was a terrible Manager… I was once promoted. It was in my early career years. I excelled in my role as a software engineer and was promoted to product manager. This role meant I would lead 3 Product Developers. It seemed like it would be easy… I mean, I am smart and I’ll do it easily. I thought to myself… I was wrong… Things started crashing. I wasn’t giving clear direction. I wasn’t empowering well. I wasn’t listening enough. My team was frustrated. I was frustrated. 😭 I remember one team member telling me… “We don’t even know what you want.” That hit deep. After honest soul-searching… I requested for a demotion. It was painful and embarrassing, but one of the best career decisions I’ve ever made. It taught me humility. It gave me perspective. It taught me to be a better leader. What I’ve learned since then: Being smart alone doesn’t cut it in leadership. Early in your career, skills will take you far. Later in your career, people skills will take you further. Anyone can be a manager of processes, but not everyone can be a leader of people. As companies promote, they need to be more aware of this. There’s a smart guy… He will lead projects. He’ll get his hands dirty. He gets things done quickly. And yet… No one wants to work with him. People leave the company because of him. People feel small around him. People don’t grow under him. Then… There’s the average guy… He’s not as fast. He doesn’t seem as sharp. But everyone loves him as a leader. He makes people… Feel seen. Feel safe. And people stretch under his guidance. You need both, but more of the other. The unfortunate reality is… Usually, you can’t change one person to be the other. At least to a big extent. It’s just who they are. So you just need to figure out who you need and when. Or what kind of blend you need. Company culture is all about blending these. Based on where your company is. If you are early stage… Between 1 - 5 years — you need Guy One. You need speed, output, execution. But… The older your company grows, the more you may want to attract Guy Two. The one who builds people. The one who reduces attrition. The one who scales culture. Otherwise… You may end up spending more on rehiring than on retention.

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The role of the instructional designer is constantly evolving. We're no longer just content creators; we're becoming learning experience architects, performance consultants, and even data analysts. 🚀 This dynamic nature of our field is what makes it so exciting! I've personally seen my responsibilities expand beyond simply designing courses to include strategizing on learning pathways, curating external resources, and measuring the impact of learning initiatives on business outcomes. This requires a broader skillset and a more strategic mindset. 🤔 To thrive in this evolving landscape, continuous learning and adaptability are crucial. We need to stay curious, embrace new technologies, and constantly refine our skills. What skills do you believe will be most critical for instructional designers in the coming years? #FutureOfID #LearningExperienceDesign #PerformanceConsulting #InstructionalDesignCareer

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Heard this story a couple times now: Big consulting firm gets hired to "improve efficiency." They roll out textbook Scrum. Two-week sprints, story points, daily standups, the works. They collect their fee and leave. Six months later, employees are drowning in process overhead but efficiency hasn't budged. The problem: These weren't software companies. One was a research-driven hardware firm. The other, a large heavy-industry behemoth. At the hardware company, a researcher told me: "I can't even read a paper anymore. I have to write a user story about reading it, estimate how long it'll take, then report my progress every morning at standup." What's agile about that!? The industrial company? "Our work happens at massive scale with complex dependencies. Nothing here fits into two-week increments or neat user-stories." Now I'm watching companies make the exact same mistake with AI. Top-down mandates. One-size-fits-all playbooks from the same consulting firms. Going wide before going deep. Remember Gall's Law: Complex systems that work evolved from simple systems that worked. You can't jump straight to complex. Whether you're adopting agile or integrating AI, the approach is the same. Start with a small pilot. Learn what actually works for your organization. Build from there. Off-the-shelf transformations rarely transform anything except your budget. --- 📧 Daily newsletter on building AI/software projects that deliver. Link in profile.

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Most dashboards die within 30 days of launch. (And it's rarely about the data.) I've built dashboards that got hundreds of views. I've also built ones that no one opened after week one. The difference wasn't my SQL skills or data quality. It was whether I solved a real problem or just built something that looked impressive. 𝐇𝐞𝐫𝐞'𝐬 𝐰𝐡𝐲 𝐦𝐨𝐬𝐭 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 𝐟𝐚𝐢𝐥: ↳ Built without asking "What decision will this help you make?" ↳ Created for executives who never asked for them ↳ Packed with metrics nobody acts on ↳ Designed to impress, not to inform 𝐖𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐦𝐚𝐤𝐞𝐬 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝𝐬 𝐠𝐞𝐭 𝐮𝐬𝐞𝐝: → Start with a decision, not a dataset → Talk to the stakeholder before writing a single query → Ask: "If this number goes up or down, what would you do differently?" → Build for one user, not the whole company → Less is more. 5 metrics that matter > 50 that don't 𝐌𝐲 𝟐 𝐜𝐞𝐧𝐭𝐬: The best data professionals I know spend more time in conversations than in SQL editors. They understand that a dashboard is just a tool. The real work is figuring out what problem you're solving. Better data doesn't lead to better decisions. Better questions do. Have you ever built something that nobody used? What did you learn from it? 👇 ♻️ Repost to help a data professional avoid this mistake — 📚 Get 150+ real interview questions (with solutions & frameworks) in our Data Analyst Interview Prep Book: https://lnkd.in/dyzXwfVp 𝐏.𝐒. I share insights on data analytics & career growth in my free newsletter. Join 21,000+ readers here → https://lnkd.in/dUfe4Ac6

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Fix submitted. Ready for review. You'll know, because we'll tell you! AAArdvark now notifies accessibility testers when an issue they filed has been marked "Ready for review" by a developer. That means: • No more guessing if something's been fixed. • Optional email and in-app notifications (on by default for testers). • Workspace admins can opt in too - perfect for oversight or coordinating QA. • Faster feedback loops and smoother collaboration. Because accessibility work doesn't stop at issue creation - it continues through validation, accountability, and progress. Want to see how it works? Try it on a free account: a20y.com

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Ever hired a data engineer who turned out to be more of a coder than an architect? Or a data scientist who could talk models but couldn’t clean messy datasets? It happens all the time. On paper they all look great, but in practice things kinda stall... We flipped the process at Agilyti: - We test every developer on real-world challenges - We check problem-solving beyond the CV - We only match talent to companies where they’ll actually deliver impact You have to understand the cost of a wrong hire isn’t just salary, but it is also wasted time, lost momentum and even missed opportunities. If you’re building data teams, the right people make all the difference - and we make sure you get them. Stop hiring on potential and start hiring on proof.

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You don’t need more tools. You need a map. In late 2022, I was called into a large U.S. bank. The QA team was missing sprint goals. Releases were late. Budgets? Blown. Everyone was frustrated; no one knew why. The work looked fine on paper. But something was off. So, we did one thing that changed everything: We used Value Stream Mapping (VSM). Not to track tasks. But to see the flow. To surface what no dashboard could show. We mapped 7 QA processes. Here’s what jumped out: 1. Vague user stories 2. Late reviews 3. Too many handoffs 4. Quiet rework piling up Tiny issues were creating massive waste. The team saw the truth and owned it. They simplified. Streamlined. Collaborated better. The impact? 1.    100% of sprint goals delivered 2.    Less than 1% defect leakage 3.    Teams felt proud again No new tools. No heroics. Just clarity. If your team feels stuck, don’t start with solutions. Start with a map. What would your value stream reveal?

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