Saul Yeaton

I listened to two great podcasts yesterday and I realized that they also both represent the current change in the data community: Gene Kim was invited on The Beautiful Mess Podcast by John Cutler. Gene Kim talked about categories of problems that large organizations face when they have business critical IT built up over decades that are really, really hard, hard to change. John Cutler suggested - this made me smile - that these were "real" companies. And that startups in comaprison, from a data engineering perspective are... simple. It's nice to see these problems being at the forefront of a tech debate. Real enterprise IT problems should be way more in focus than ideal end-state-architectures. Ghalib Suleiman was invited on The Monday Morning Data Chat hosted by Joe Reis 🤓 and Matthew Housley This conversation was wonderful to listen to, for its simplicity and clarity in pointing out that the era of "the modern data stack" is not only fading away, but stone dead. And justly so - in the years with 0% interest rate, companies around the world spent too much money on data tooling with way too little ROI. Obviously, many technologies also deliver, and they will continue to change, adapt and survive. And then.. many will also fade. I think we have been in an era of data bluff. And I think it's over. links in comments. #data #moderndatastack #bluff

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Stakeholder: "We need this for the board deck this afternoon." Analyst: "I'm on it" Analyst thinks: Why did you wait until last minute to tell me about this? Analyst drops everything to get the data needed for the request. Next day.... Analyst: How did the board meeting go? Stakeholder: We ran out of time and never got to it. meanwhile... 3 other expectations were missed due to the urgent need. Sound familiar? Try these tactics: 1. Get the board meeting CALENDER from the CEO's EA 2. SCHEDULE a meeting with the stakeholder the week BEFORE the board meeting to go through metrics needed for the meeting. 3. LEARN what is IMPORTANT to the success of the business, not a person and build around that. 4. Be up front with the stakeholder about what expectations will be missed if you have to do urgent requests. 5. Track urgent requests so you can report these metrics to the business and its impact on productivity. Nothing is perfect, but these will help. #data #analytics #rockstar

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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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The best digital and data teams (those delivering most benefit) I know are actually working on the fewest projects and products. It is counter-intuitive but the teams doing the most work are often the ones struggling to show measurable impact because they're both spread thin over too many initiatives and working on lower value work. As the saying goes, if everything is important nothing is important. This is the best way I've found to show what it means to be ruthlessly value focused. In the last digital and data setup I was in, even when the central team scaled to over 200, the portfolio never had more than 10 active major projects or products. Less really is more. Yesterday the latest Datent Accelerator programme kicked off, and we spent a lot of time discussing what it means and takes to be 'transformational'. In my opinion, this is part of the answer.

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I'm happy to announce my next role: I'm going to open a boot camp to teach other people how to open data boot camps! I noticed that the TAM for this is bigger than for data boot camps. A new HBR survey shows that there are now more people running data boot camps than there are people with jobs in data. Sign up and you'll learn: 1) How to get past your imposter syndrome over getting paid $500k to do a job that didn't really add value so you quit after 1.5 years. 2) The importance of "ex-_____" in your LinkedIn headline. 3) Making sure your students don't find all those high quality, free YouTube videos by brilliant Indian dudes. 3a) Emphasizing that those guys are only "ex-FAANG offshore contractors" so totally different. 4) The art of the self-congratulatory LinkedIn post where you point out that making 7 figures as your own boss is so much better than when you were making mid 6 figures working for FAANG. It's the tech bro version of selling stock tips while standing in front of a Ferrari. Classy. My next venture is also coming up. It will be in the boot camp for Web3 boot camps space!

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Good morning! Like many of us, I have been trying out different LLM models. Here is my current take: 1. Perplexity: I have spent the past 6 weeks trying out different queries -- personal and professional. The sourcing and add-on features (e.g., Pages and Libraries) are awesome. For example, here is one on data mapping: https://lnkd.in/eH7KzaP9. As this is a problem we are always helping clients with, I will curate this one. 2. ChatGPT: I am finding ChatGPT great for brainstorming, and helping develop scenarios. The one "prompt engineering" idea I am working on is stress-testing a technical architecture based on scenarios and variables. Also, on a personal side, my daughter loves word searches and I had ChatGPT create a word search about aquatic animals that was outputted to Microsoft Word. 3. Microsoft CoPilot for Microsoft 365: Oddly, despite using ChatGPT, I am still not getting the same value as using ChatGPT with prompts. I imagine there are some ways to use it better, but I am still learning. 4. Claude: My father has been showing my philosophical conversations with Claude, and I have been very impressed. Let me know if you have any thoughts or takes on LLM models and how they have been supporting your personal and professional workflow. #ai #keeplearning

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The proliferation of definitions and terms in the realm of data is both insightful & confusing at the same time. In rare cases, it may not be helpful at all. We could be strongly disagreeing about the same thing. More recently, there is a greater recognition that Data, Technology and Algorithms alone are not enough. What really matters are the reasons for using data in the first place and the outcomes or decisions that we make as a result of it. More specifically, Decision Science and Decision Intelligence. I first learnt about #decisionscience from Cassie Kozyrkov's earlier videos; and concur with her views. But change is the only constant, Decision Science is evolving as well. Watch the video and let me know what you think or decision intelligence vs decision science? Keen to hear. FYT Consulting Michael Lee FYT Jeremy Poon Civil Service College Valerie Yeo Zhengyi Tan Huda Osman Sareth Kumar Singapore National Employers Federation (SNEF) Ben Liew 刘菲明

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The new #Planner has the ability to #Baseline! I'm jumping up and down seeing this new feature that PMs have been asking for! 👏🥂🎉 Reminder - Learn more about the new Planner with me April 30 at the Generative AI Day for PMs conference - https://lnkd.in/eMsC_ART

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As digital and AI transformations accelerate, the potential of hashtag #DataSharing is becoming more evident. Pioneers like Walmart and Procter & Gamble have, over the years, reaped significant benefits from embracing data sharing, yet many organizations remain hesitant. Thanks to new technology, data sharing has become more secure and efficient, easing operational hurdles and regulatory compliance. These advances also redefine the role of trust. In my most recent Fortune column, co-authored with Riccarda Joas, Guillaume Sajust de Bergues, and François Candelon, we explore how these technological advances are impacting the risk-reward calculus. We provide arguments that might encourage executives to reconsider their stance on data sharing. Read more on Fortune: https://lnkd.in/eWP2K6KF

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Excellent analysis by Parul Kaul-Green, CFA : the insurance industry need to get up to speed on the CPO role (Chief Product Officer) 🙏 While working in the industry industry (with experience in a few more mature digital industries) I often noticed the lack of this role ... You had a very old skool split between business (often Product) and IT (CIO/CDO). There was no separate role in between the CIO/CDO & the business lines with a lot of discussion on roles & responsibilities for value creation as a consequence .... (including very low impact programmes and transformations as a result). "When will businesses realize that delivering technology and connecting technology with value are two different capabilities? " (Vin Vashishta) Gathering the 2 capabilities in 1 leadership role only make things even more complicated. "Invariably insurers will conflate project management with product management and hire completely wrong skill set, and create poorly performing digital, data or AI programmes." (Parul Kaul-Green, CFA) Insurance companies also seem to get confused on the PRODUCT role .... it's more than the insurance policy, the underwriting and the claims part ... it's about creating business value on top, NOT just about the insurance product. Now that insurance companies need to evolve to 'tech companies with an insurance license and a customer obsession' (my belief), timing seems right for more CPO roles. Do you know any good examples of insurance companies where the #CPO function has emerged and made a difference at the side of a CTO/CDO/CIO ? 👀 Looking forward to the examples in the comments ... 💬 #insurance #cpo #leadership

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Check out this animation below! At Climavision, explainable AI is paramount in our approach to forecasting. AI is not a black box, in fact, I would argue that many would struggle to explain why NWP does what it does, through expansive physical equations. By building trust with our customers through explainable forecasts and technologies, we have enabled the use of AI forecasts for critical decision making, creating not only the world's most accurate forecasts, but the most useful. If you want to learn more about our long range forecasts, their impeccable accuracy and how they are explainable, Ben's DMs are open!

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Sometimes it's hard to explain why data is not just concrete and exact. Non-data people can get the wrong impression that data is not right when it's really nuanced by the question being asked. It's fluid and just like a liquid, it really depends on the container. Example: "How many orders did we have this month?" - In what time frame? - What segment/division of the business? - Everything? Or should we segment out subscriptions from one off orders? Here is an example from a very basic point of view. Imagine you have a big toy box at home. Every time you want to play, you ask your mom to pick a toy for you based on your mood. Some days you might ask for a toy that makes noise because you're feeling energetic. Other days, you might ask for a quiet toy because you want to play calmly. Each time you ask, the toy you get depends on what you asked for and how you described what you want. Now, let's think of a data query like asking your mom for a toy. The big toy box is like a huge computer database where lots of information is stored. When you ask a question or make a request to this database—like asking for certain types of information—the results you get depend on how you ask the question. Different Questions: Just like how asking for a noisy toy vs. a quiet toy gives you different toys, changing your question a little bit can change what information you get from the database. Time of the Query: Imagine if new toys are being added to your toy box regularly. If you ask for a noisy toy one day, you might get a different one another day if a new noisy toy has been added. Similarly, data can change over time in a database, so if you ask the same question on different days, you might get different answers. Who’s Asking: Sometimes, your mom might give you a small toy because it’s safer for you, while your older sibling might get a bigger one. In databases, who is asking the question might change the results too. For example, some information might only be available to certain people, like teachers or scientists. #data #analytics #rockstars

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There is an interesting trend right now that I’m always getting in the middle of.   What is data and analytics leadership?   It’s not a straight forward answer and for years the technical side was considered the answer. However, I always pointed out, that it works if your org is having good times, when tough times come, then it stops to work. Many companies are finding that out now.    Leadership in this space is a scale that changes based on the maturity and complexity of what is needed.    A simple org with simple needs can get away with having a technical person lead the team. Keep in mind, you are kind of advertising that you are simple and that means those that want to build big and complex, will not come to your org. Those that want to hone a specific skill will. Now, that’s not a negative if it works with your culture.    Now, if you want to grow and be a player in your industry, the technical alone is never going to get you there.    That’s where a blend of skills is really important and the nature of the team often changes from a technical support player in the org to one of a business driver. Business driven teams need people who understand the business. For some reason I get a lot of pushback from people on this point. This is not an either-or but a must have skill. You are not building stacks or writing code at this point, you are building a business, that means you need to have the skills to build a business. Sorry if this ruffles feathers but nobody wants to bet millions on you being lucky, they want to see that you can scale a business.   You need to know how to build a team that focuses on customers, understand market trends, customer trends, negotiate with partners, interact with investors, future proof the business for long term success. That last part, is where you have the most fun and challenges.   Often your own team becomes an issue as they want to build things, they find cool or helps to pad their resume. That doesn’t always translate into what is best for the business to grow.    I remember the deep learning craze and companies spent millions on projects. When I got in the weeds, it was driven by, someone who knew some guy who knew someone who heard of someone making seven figures building deep learning and they wanted that kind of money too. It didn’t matter that it was a total waste of money for the company. But that’s where the challenge between the business mindset and technical often come out.   As a business leader, I killed those deep learning projects as I knew there were faster and more impactful methods. The technical leaders often found it cool and interesting invested money in something that often didn’t pan out.   When hiring, companies really need to ask themselves, which leader do they need? If you want simple and basic and just need some order takers (again, nothing wrong with that) go technical. But if you want some build data as a strategic asset, go business. #data #analytics #leadership

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I always thought Snowflake was just another data warehousing solution (what could be so different?), but it surprised me with some of its unique approaches to making ETL processes easier while maintaining efficiency and scale. It comes in handy for a current analytics project I have with one of the regional companies. I highly recommend this course!

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Isn't Gartner the company telling everyone else how to run their data teams? Shouldn't they be able to find a CDO internally? Why are they are paying LinkedIn to recommend this job to me 😂 If you really believe your people have the answers for how data teams should operate, one of your own people should be promoted to CDO. If you can't eat your own dogfood, what does that signal to the rest of us when you put it on our plates? p.s. Wish me luck, this will either put Delivery Layer on the blacklist for the Magic Quadrant, or send it straight to the top because the new CDO is a big fan 🤞

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When you work within the Department of Defense's PPBE process, there is an important question you always have to ask: "What Year Are We Talking About?" It is easy to get confused between the plans for FY26 and the execution of FY24. And dealing with the frantic MAJ without asking what Fiscal Year he means...that can lead to chaos. #militarylife #governmentwork #pentagon

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AI/ML are made possible through the reuse of data. This month's DATAVERSITY column addresses the ethics of reusing data. We take a look at #datalineage, consent, purpose use limitations, the #databroker economy and the recent push for anyone with a big dataset to sell access to it for training #generativeai. #dataethics #aiethics #responsibleai #privacy #consent #artificialintelligence "One new use case that is being driven by generative AI is the reuse of customer data to train AI models. Some organizations have been quietly changing their policies to disclose this new use case. However, the Federal Trade Commission (FTC) has said this could be constituted as an unfair or deceptive practice: “It may be unfair or deceptive for a company to adopt more permissive data practices—for example, to start sharing consumers’ data with third parties or using that data for AI training—and to only inform consumers of this change through a surreptitious, retroactive amendment to its terms of service or privacy policy.” (FTC) There are also many ethical questions raised around the licensing of communally created data being repurposed as training data. Reddit has signed a $60 million/year deal with Google to sell its users data to train AI models, while Automattic, owners of Tumblr and WordPress.com, are moving down this pathway with OpenAI. The onus appears to be on individual users to “opt out” if they don’t want to take part, but is that practical or reasonable to expect people to do? This is a use case that would not have been clear to anyone who launched a Tumblr blog in 2007!" https://lnkd.in/gU23gq67

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I spent 6 months asking data teams for the top traits of effective Product Managers.Here’s what I found: Most data teams I talked to said they haven’t worked with an effective data and AI PM. Their biggest negative traits? Product Managers who treat the data team as an after thought, take credit for their work, ignore their feedback, and need to be the smartest person in the room. However, data teams who said they have worked with an effective data and AI Product Manager never wanted to work without one again. The top traits all relate to understanding users and translating their needs into something the data team can build without constraining or micromanaging the solution. I detail the top 9 Data and AI Product Management traits and how PMs can build these behaviors in my latest article. https://lnkd.in/gu-4qati #ProductManagement #Data #AI

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