Connor Dickson

🧵 Hearing "GraphQL" for the first time years ago at a conference made me think I needed to dust off my old TI-85 and remember how to graph equations. Not really, but I did realize I was way in over my head. I was still early in my career as an Analyst learning SQL, and now there's this new Query Language? How do you use it? Why do we have it? What is the purpose and function? What does graphing have to do with any of it? Spoiler: nothing. The "graph" refers to graph theory. Now, I'm going back to basics to learn more about GraphQL: It's a query language for APIs that lets clients ask for the data they need, nothing more and nothing less. One endpoint, strongly typed, and self-documenting. Why dust this off now? - It's everywhere, with big tech companies running GraphQL in production - Microsoft Fabric now has native GraphQL support. You can spin up a fully typed API endpoint directly from your Lakehouse or Warehouse with zero code - It changes how you think about data modeling. Designing a schema forces clarity about relationships and what consumers actually need - It's strongly typed by default, which means better tooling, autocomplete, and catching errors before they reach production Looking forward to sharing what I learn, and hearing from others on this journey. #GraphQL #APIDevelopment #DataEngineering #MicrosoftFabric #LearningInPublic

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Fully aligned with this vision, regardless of the data-visualization tool being used, true value can only be achieved when insights are designed with—and for—end users. This means focusing on directly actionable KPIs that exist not just to look good, but to drive real decisions and spark meaningful actions.

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Big companies have data teams. You have... yourself and maybe a bookkeeper. Good news: You don't need a data warehouse. You need 3 simple layers. 𝗟𝗔𝗬𝗘𝗥 𝟭: 𝗥𝗔𝗪 𝗗𝗔𝗧𝗔 Where transactions happen. - Bank feeds - POS system - Invoicing software - Payment processors (Stripe, PayPal) This is messy and that's OK. It's the raw material. 𝗟𝗔𝗬𝗘𝗥 𝟮: 𝗖𝗟𝗘𝗔𝗡 𝗗𝗔𝗧𝗔 Where transactions get organized. - Your accounting software (QuickBooks, Xero) - Transactions categorized - Duplicates removed - Reconciled to bank This is your single source of truth for money. 𝗟𝗔𝗬𝗘𝗥 𝟯: 𝗜𝗡𝗦𝗜𝗚𝗛𝗧𝗦 Where you actually make decisions. - Monthly P&L - Cash flow forecast - Dashboard with KPIs - Trends over time Most small businesses stop at Layer 1. Some get to Layer 2 (eventually). Few ever reach Layer 3 consistently. 𝗧𝗵𝗲 𝗴𝗮𝗽 𝗶𝘀 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻. Raw → Clean should happen automatically (bank feeds, rules, integrations) Clean → Insights should be one click (reports, dashboards) When it's manual, it doesn't happen. When it's automatic, you actually use it. Which layer is broken in your business? #DataArchitecture #SmallBusiness #BusinessIntelligence #Automation

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AI is only as good as the context you give it. I've been refining a Tableau chatbot, and my biggest takeaway has been the power of Context Engineering. LLMs have a finite canvas. If you don't manage that space effectively, then you run into issues like context drift and degraded performance. To solve this, I’ve focused on learning ways to maximize the efficiency of the initial tokens passed so no critical insights are lost in the noise. As a first step I have been learning about context engineering in the form of role based tailoring. I have enhanced the chatbot to recognize whether the user is a Viewer, a Manager, or an Executive. - Smart Data Usage: We make the best use of the initial context window to prevent information loss. - Token Efficiency: We aren't just "dumping" data; we are curating the most relevant facts to keep the output sharp and cost effective. - Tailored Insights: The output matches the user's specific expectations and goals. I can now fine-tune these personas in minutes, which historically would have taken days or weeks. The speed at which we can now iterate is a total game changer!

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Being an AE isn't always SQL queries & data modeling. Many days I'm sifting through documentation to figure out what something is supposed to mean or taking a course to keep up with the vastly changing tech landscape. 🌪️ Other days I'm debugging a failed daily job or trying to figure out why my local dbt build won't pass (usually a comma or forgetting to put something in the yaml file.) 🤦🏾‍♀️ Either way, I believe it's important to stay curious and learn from whatever your day brings! #analyticsengineer #data

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Stop building dashboards that nobody trusts. 🛑 In the old world of data: Data Engineers built the pipes. Data Analysts built the charts. Everything in between was a "black box" of messy SQL and broken logic. Enter: Analytics Engineering. I’ve been diving into Module 4 of the DataTalksClub Data Engineering Zoomcamp, and it’s a game-changer. It’s not just about moving data; it’s about applying software engineering best practices to transform it. The secret sauce? dbt (data build tool). In this module, we didn't just write queries. We built: ✅ Modular SQL: Using ref() to create a lineage you can actually follow. ✅ Testing: Catching nulls and duplicates before the CEO sees them. ✅ Documentation: Automating the "What does this column mean?" conversation. ✅ Deployment: Moving from BigQuery/Postgres dev environments to production like a pro. The gap between "raw data" and "business value" is narrowing. If you aren't thinking like an Analytics Engineer yet, you're leaving reliability on the table. Huge shoutout to Juan Manuel Perafan spend a few more seconds to talk about IAM role that most educator just skip and Victoria Perez Mola for making this world-class content free. 🚀 Had moments of "productive struggle" going through this module🥹, I can celebrate Chinese new year with peace of mind now 😍🎉 #DataEngineering #AnalyticsEngineering #dbt #BigQuery #DataTalksClub #LearningInPublic

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𝗛𝘂𝗺𝗮𝗻𝘀 𝘄𝗶𝗹𝗹 𝗮𝗹𝘄𝗮𝘆𝘀 𝗯𝗲 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽... I’m transitioning into a new company, but right now, let's talk about the "Annotation" role... As long as AI keeps growing, humans will be needed to label, review, and refine data. That part isn’t changing anytime soon. What needs to be clearly understood is the structure of the work. 𝗗𝗮𝘁𝗮 𝗮𝗻𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗺𝗼𝘀𝘁𝗹𝘆 𝗽𝗿𝗼𝗷𝗲𝗰𝘁-𝗯𝗮𝘀𝗲𝗱, 𝗻𝗼𝘁 𝗲𝗺𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁-𝗯𝗮𝘀𝗲𝗱. Yes, you can sign contracts with companies, you get to work on projects whenever it's available. Yes, the pay can be good. I’ve worked on projects paying $5, $10, $17–$20 per hour. But it's mostly project-based Some projects can last two months. Others run more.. A few stretches close to a year.  𝗔𝗻𝗱 𝗲𝘃𝗲𝗻𝘁𝘂𝗮𝗹𝗹𝘆... 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗲𝗻𝗱!, 𝗕𝘂𝘁 𝗛𝗼𝘄 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗮𝗿𝗲 𝘆𝗼𝘂 𝘄𝗵𝗲𝗻 𝘁𝗵𝗲𝘆 𝗱𝗼? One of the smartest ways to create security in data annotation is diversification. Instead of depending on one platform or a single annotation type, smart annotators combine: Data annotation, data review / QA, transcription, content evaluation, AI training support, even non-ai oriented work. When you build multiple income streams: You don’t panic when a project wraps up. You stay financially steady while waiting for the next opportunity. And you have options. 𝗜𝗳 𝘆𝗼𝘂 𝗰𝗵𝗲𝗰𝗸 𝗺𝘆 𝗽𝗿𝗼𝗳𝗶𝗹𝗲, you’ll see I’ve worked across multimodal annotation: LLMs, video, audio, image. Different techniques, different industries, different project lengths... 𝗔𝗻𝗱 𝗼𝘂𝘁𝘀𝗶𝗱𝗲 𝗮𝗹𝗹 𝘁𝗵𝗮𝘁? 𝗜’𝗺 𝘀𝘁𝗶𝗹𝗹 𝗮𝗻 𝗙𝗫 𝘁𝗿𝗮𝗱𝗲𝗿  Smart work! 😉 You can book a consult with me to learn more about our algorithm (TITA). 👉🏽 https://lnkd.in/egAuK7ak #DataAnnotation #AITraining #HumanInTheLoop #AIWork #FutureOfWork #FXTrader #TechAndFinance #TITAAlgorithm #MachineLearning #LLMEvaluation #AIAnnotation #TechCareers #RemoteWork #SmartWork

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Stop lumping data analysts in with IT. Analysts are business strategists. They don’t troubleshoot hardware. They don’t manage logins. And they’re not tucked away cranking out dashboards no one uses. Their role is to embed with marketing, product, ops - wherever decisions happen and translate data into actionable insight. If they’re siloed in IT, disconnected from the business, you don’t get clarity. You get lifeless spreadsheets. The real value shows up when analysts are in the mix: → Marketing says, “We’re rolling out a new initiative.” → Analyst says, “Here’s what succeeded before, here’s where you’ll burn budget.” That’s influence. And that’s the gap between “data support” and “growth partner.” If your analysts don’t speak the language of the business, they’re just pricey report machines. — ♻️ Repost if you’ve seen analytics buried in IT. → Follow me, I share interesting stuffs that actually makes sense

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𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗜𝘀 𝗛𝗮𝗿𝗱𝗲𝗿 𝗧𝗵𝗮𝗻 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 Hot take. Most people get excited about regression models and predictive analytics. But in healthcare? Data wrangling determines whether your model even makes sense. You can build the most beautiful model… But if: • ICD codes were grouped incorrectly • Null values weren’t handled • Cohorts weren’t defined properly Your prediction is noise. In my experience, 60–70% of the work is data cleaning and validation. Modeling is the final layer — not the foundation. Agree or disagree? 〰️Kosi Clean data. Clear decisions. Better healthcare.

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