Michael Galvin

Great conversation today about data leadership! Big thanks to Celina Wong for thought-provoking questions. My biggest take-aways: 1) Data leaders in companies at every size and level of maturity have really similar issues - one of the biggest being the organization of data. Analytics Engineers/Data Architects are increasingly important to data success everywhere. 2) Leading with transparency and providing context and exposure are essential for retaining and developing talent - in any function, but in data especially. 3) Maintaining a balance between short-term priorities and longer-term infrastructure work is one of the most important skills for a data leader to develop

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The 5 Best Data Science Blogs To Follow: There are countless excellent resources on data science, and it can be a little overwhelming to know where to start. Here are some options: 1. Data Science Central Run By: Vincent Granville  Website link: DataScienceCentral.com Data Science Central does exactly what its name suggests and acts as an online resource hub for just about everything related to data science and big data. 2. SmartData Collective Run By: Social Media Today  Websitelink: SmartDataCollective.com SmartData Collective is a community site focused on trends in business intelligence and data management. 3. What's The Big Data? Run By: Gil Press Website link: WhatsTheBigData.com What's The Big Data? takes a different approach to data science and focuses on the impact of big data’s growth into the digital behemoth it is today. T 4. No Free Hunch Run By: Kaggle  Website link: Blog.Kaggle.com This blog is slightly different than the others, offering a look directly into the minds of data scientists, as well as tutorials and news. This is the blog of the data science website Kaggle, which hosts data science projects and competitions that challenges data scientists to produce the best models for featured data sets. 5. InsideBIGDATA Run By: Rich Brueckner  Website link: InsideBIGDATA.com InsideBIGDATA focuses on the machine learning side of data science. It covers big data in IT and business, machine learning, deep learning, and artificial intelligence. Guest features offer insight into industry perspectives, while news and Editor’s Choice articles highlight important goings-on in the field. Happy reading Karl Ramsaran - Data AI Growth Talent Partner

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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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As a fellow offender of the long email sin, I find the real problem isn't needing a shorter email. I shouldn't be asking a question at all. This is classic CYA behavior where I know the path forward but want it to be someone else's decision. But how would I expect my boss to be able to synthesize what I have researched better than I already did? So the only reason I need my boss's input on this decision is if there's information I don't have. In which case I should ask for the info I need, rather than trying to transfer my context or decision-making responsibility to my boss.

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Curious about integrating AI into your research workflow? If you're relying on basic summarization tools or just using ChatGPT, you're missing out on AI tools designed specifically for research. With Notably, you can collaborate with AI in 4 super specific and targeted stages of your research process: 1. Data Cleanup & Summaries: Clean up transcripts, scrub PII, and summarize calls using tailored AI prompts for usability sessions, stakeholder interviews, and more. 2. Analysis: Instruct AI how to highlight and tag raw data so you can uncover patterns and emerging insights, faster. 3. Synthesis: Experiment with clustering data with AI using various frameworks, tags, or by participants for rapid visualization. 4. Insight Generation: Generate insights using AI-driven prompts crafted to promote divergent, generative thinking. Join us this Weds at 12pm EST to break down each of these 4 steps in Notably. Plus, we’ll be launching a new feature during our session (what could go wrong?!) where you can choose from pre-made prompt instructions to highlight & tag and cluster data. RSVP to secure your spot! PS: If you can't make it live, still RSVP and we'll share the recording https://lu.ma/597pwyj3 #AIResearch #UXResearch #QualitativeResearch #AI #AIEvent #UXR #AITools

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🎉🧐 The Technician's MBA is Live! 🧐🎉 The first post of "The Technician's MBA" is live on Substack - https://lnkd.in/eG5vC4wp. It's more outlining what content to expect, but if you want to have read you can do so with the link above. If you want to subscribe its free to do so and you will get each new post direct to your inbox every Wednesday. I will also cross post here for reach purposes. #TheTechniciansMBA #ThoughtLeadership #SoftSkills #TechnicalPractitioners #Coders #Coding #Leadership #Mentoring.

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Generally agree with the sentiment of this. I assume the unpopular opinion for some is the revenue generation. It’s a North Star metric that only significantly mature data teams likely can realize. But value generation is still a spectrum, with revenue on the far end. If data teams can’t identify / articulate how operational efficiencies, cost avoidance, data literacy improvement, etc. are realized, then they are 1 Jupyter notebook model away from becoming a cost center. Don’t make consumer and product an afterthought

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I've been following the development of the #tidymodels framework for machine learning in R since the beginning, and it never ceases to amaze me how the team at Posit PBC has added more and more functionality to {tidymodels}. (Screenshot from Hannah Frick's talk about "tidymodels for time-to-event data" at posit::conf(2024) yesterday, which I just rewatched.) #PositConf2024 #rstats #DataScience

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The 6 daily non-negotiables that give me completely clarity in my life that Mark Gandy and I spoke about on his podcast: 1. German lesson 2. 45 minutes of exercise 3. Read 10 pages of non-fiction 4. Review short- and long-term goals 5. Write 3 posts on LinkedIn of variety of topics 6. Reach out to 5 people I don't know to build my network I started this habit about 300 days ago. Learn more about why in this interview.

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Data scientists hate meetings. But reducing meeting overload is not about: - story points and backlogs - declining invites with no agenda - the CIO saying "we need to reduce meetings" I've watched all these approaches crash and burn. I've watched myself fail to reduce meeting overload in my teams. I've watched CIOs make this their top priority and fail to create lasting change. Why? We had consensus on the problem. We had the tools. We had the will. But we didn't solve the root causes of meeting overload. What are the root causes? They don't teach you about this in business school. They don't teach you about this in bootcamps. So I will try. You'll never reduce #meetings until you reduce: 1. Clear Return on Politics 2. Unclear Ownership 3. Unclear Value Let's get into them. 1. Clear "Return on Politics" Credit to Safi Bahcall's "Loonshots" for this. If you get more benefit from spending one hour on politics than the same hour on "real work," people will politick. A lot. Politics can never be fully async. No one wants to be held accountable for having something less-than-savory written down. Solutions to politics depend on culture and require a book. But in general, you must align on the following things, in this order: - values (shared worldview) - vision (shared outcomes) - ownership (clear execution) Which brings us to the next root cause of meeting overload: 2. Unclear Ownership A lot of meetings are spent discussing who should do what. This is a sign of an immature operating model. It often happens with new or evolving spaces, like GenAI in the enterprise. Focus on maturing the operating model rather than solving for your one-off situation. Align teams around a model, even if it's imperfect. Then iterate as a group at defined checkpoints (e.g., a retrospective). Actually act on the feedback you receive. The operating model must very clearly connect process (how you work) to value (why the work matters). Which is why you must eliminate... 3. Unclear Value Many meetings are wasted on sharing status with different groups of stakeholders. These time parasites multiply when they worry you're not doing anything valuable. To calm everyone down, focus only on these 4 things: - value of the work - key bottlenecks & risks to value - anything that's gone wrong, and - a plan to move forward Send async updates on the above points before status meetings a couple times, then ask if the meeting can be eliminated. Eventually, track this async in a dashboard. Over time, you will claw your time back. Which will let you focus on bigger picture work and get promoted. --- Root causes of meeting overload: 1. Clear Return on Politics 2. Unclear Ownership 3. Unclear Value Found this valuable? Share this with someone drowning in meetings. Good luck, #thinksenior

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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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🤔 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.   🛠️ I made another 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 Ibragimov.   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 in the past than in 2022, but I used that year because it was the last year of the other range and I wanted something semi-current.   🏈 This is a Throwback Thursday viz!   🏗→🧠 Build to Learn!   💭🚶‍♀️🚶‍♂️ Follow for more.   #tableau #data #analytics #VizoftheRay

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Tableau + map layers is spatial analytics at its best. Here are some quick insights about crimes in Chicago. Over two 1-hour classes in @Next-Level Tableau, we used data from the Chicago data portal about crimes in 2024. Overall, you can see a hot spot of crime on the east of Chicago. The highest crime area falls between a 1.25 mile buffer from two police stations. It might be a coincidence, but it's a signal that more analysis should be done. Despite there being more crime in the east, homicides trend towards the west and southeast. Almost all prostitution crimes happen in the SW corner of district 11, which is near one of the districts with more murders. This was created with 5 map layers: 1. Crime locations (density heat map) 2. Police station locations in each district (stars) 3. A buffer around each station colored by the number of crimes in that buffer. 4. The borders of each police district are colored by the number of crimes in that district 5. The streets across the city, grouped by police district. Because they were separate data sources, I used a parameter to filter across the data sources. Have you ever done that? It's amazing what you can do in a couple hours when you combine open data with Tableau. Check it out → https://lnkd.in/eqXtCVgF __________ Hi, I'm Andy Kriebel Click my name + follow + 🔔 I help people become Tableau experts 10x faster. Sound good? Book an appointment and let's see how I can help. Like this post? Like 👍 | Comment ✍ | Repost ♻️

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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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In data, FOMO is real. Specifically when it comes to AI. Where was all of this hype years ago when those of us in data were constantly trying to prove our value and being the first budgets to be cut. Now, companies are trimming data teams to make room for speculative AI projects that have little to no true business value. It doesnt make sense. If you are going that route, be sure to specify WHAT you are going to do and the IMPACT it will have on the business and/or customers. What most companies should be doing (not all, but most): 1. Sound foundational analytics pipelines that move data efficiently. 2. Standardize data entry and capture to reduce data inconsistency. 3. Standardize data contracts to allow for easy, clean, adaptable data objects. 4. Augment your own data with outside data sources (paid or public). 5. Get a data product manager. (critical for everything) 6. Define an AI / Data Science budget and stick to it. Within this, define what your 3-6 month goals are to bring value to the business (with stated outcomes). Without 1, 2 and 3, you cannot do number 6. #data #analytics #ai #rockstars

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A great milestone for Ontologize! Ben Thomas Thomas’s tutorial just hit 1k views, with Gena Coblentz being my personal Favorite close behind with her amazing Ontology tutorial. Grateful for their work making Palantir easy to learn! Big shoutout to Taylor Gregoire-Wright for having these. If you’re looking to dive into Palantir, their tutorials are a must-watch. Follow Ontologize on YouTube and LinkedIn for the best learning materials! 🌟 #Palantir #Tutorials #Deeplearning #Ontologize #Data #Foundry

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"Data is a precious thing and will last longer than the systems themselves." — Tim Berners-Lee In the data and analytics space, we often focus on the latest tools and technologies. However, the true value lies in the data itself and the insights we can extract from it. As professionals in this field, we must bridge the gap between technical innovation and business impact. Let’s continue to push the boundaries of what’s possible, but never lose sight of the business problems we aim to solve. Our work should always translate into actionable insights and tangible results. To business leaders and other professional across organisations, embrace the power of data-driven decision-making. Invest in understanding the potential of analytics and data science to transform your strategies and operations. Collaboration between technical and business teams is crucial for harnessing the full potential of our data assets. Together, just like my team at Turner & Townsend and I, let’s build a future where data not only unlocks some progress but creates lasting value. How are you leveraging data to make a difference today? #DataScience #Analytics #Leadership #BusinessIntelligence #DataDriven

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