Dylan Anderson

🔊 Over the past few months, I’ve connected with numerous Data Consultancies preparing to deliver Databricks services to their clients. Many are still testing the waters, just establishing their Databricks partnership, and reaching out to us for guidance on the market for talent. One way ON Data Staffing has been able to add value is by helping clients prepare for projects they anticipate starting soon. Even if they don’t have a live project yet, they know they’ll need skilled data engineers at short notice — and we’re here to support them with that. For two different clients, we recently completed four FTE placements, moving candidates from the first interview to the start date within 3-4 weeks — right in line with each client’s timeline. In fact, we’re currently working on a project that requires December and January start dates and already have the talent lined up. This is not luck, its due to extensive work in this space over the past year, we have a network of around 1,000 QUALIFED Databricks Data Engineers across the U.S, not all are certified, but all are passionate about Databricks and ML/Data Engineering. Therefore, if we haven’t been introduced and Databricks talent is something you’re considering, now or in the future, feel free to set up a time on my calendar to discuss how we might be able to help. (Calendly) https://lnkd.in/e7AVVhrS #Databricks #DataEngineering #DataConsulting #DataRecruitment

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I'm reviewing CVs right now for an optimisation consultant (x3) role that we have open and I thought I'd use this as an opportunity to provide real time feedback for anyone applying for roles. - Put your name in the filename. Don't just call it resume.pdf because I promise it will get lost in the "downloads" folder. - Don't stuff your CV with technology keywords. All that tells me is that you've mastered none. - If the screening question asks how many years of AB testing (or any skill) experience you have, and you put 2+, maybe put AB testing somewhere on your CV. - I don't believe in cover letters, but if you're applying for a role that you're not experienced in, try including a summary paragraph of who you are and why you're applying for a role you're not right for. I can't guarantee you'll pass screening, but you'll have a better chance than the other 80% who have applied without prior experience - This is just me, but I read EVERY single CV I get, and I'm largely scanning for some core things before I read the entire CV (I've just reviewed 40+ CVs). If I can't find certain words related to the role, I'm not going to read it. So try to use the language of the role (don't keyword stuff though). I'm not an idiot. I know tailoring your CV for every single role is not scalable, but most people are presumably only applying for certain types of roles which means you should have a few variations of your CV tailored to those roles.

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This week's post looks at how poor data culture (behaviours, tools, talent, & processes), not only prevents organisations being more data-driven but can create a drag on productivity. https://lnkd.in/ekEtinkY

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Is the 'Big Data' era over, considering all the focus on AI? The award-winning website, CIO, says it might be, but its underlying value and tensions still exist. While the Big Data era is fading, significant value can be obtained from basic data blocking and tackling. CIO suggests executives shouldn't be too quick to consign the technologies promoted during the honeymoon period of 2005-2015. More here: https://bit.ly/3X6ERs5 www.defined-calculus.com #BigData #Data

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How do you instill an maintain a successful culture in a growing team? It's a question that SO many leaders struggle with - Sam Maughan caught up with Robert McDowell to talk about routes to success in this really interesting interview about the ups and downs of growing an analytics consultancy. Link below to hear more!

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I've been having multiple conversations this week with data leaders across Scotland and there is definitely a shared frustration about a lack of opportunities being available. That's why staying connected to the market is essential for a data leader. Data leadership roles in Scotland are rarely available—and even less frequently publicly advertised. That’s why having a trusted recruitment contact is so valuable. It’s not about being actively on the job market or committing to a move. It’s about staying informed and connected. A good recruiter can keep you updated on opportunities as they arise, giving you the option to explore them without actively searching. By staying connected, you’re always aware of what’s happening in your industry and can make informed decisions about your career—on your own terms 🏴󠁧󠁢󠁳󠁣󠁴󠁿🤝 AI Connect The Data Gals | by AI Connect

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Organisations are drowning in data. But only 1% are truly harnessing its power. Here’s why generative AI is the key to truly inclusive data: 1. Data democratisation Data democratisation promised to remove barriers and make data accessible to all. The reality? • It’s still too complicated, too slow, and too technical • Its use remains siloed and niche Organisations just aren’t using their data to its full potential. To truly free the data we need to personalise how people can ask questions and receive answers, aligned to the pace of decision making. 2. Complexity blocks engagement Endless charts, Excel sheets, backward looking dashboards, slideware and technical language simply put way too many barriers in the way for most. 3. Time lag The time lag between asking a question and getting a meaningful answer means decisions are often made on instinct and experience. 4. Software providers have offered various solutions • More intuitive interfaces sometimes supporting natural language • Bespoke decision making solutions • Accelerated querying engines But none have fully delivered. 5. Answers are often complex, limited and even incorrect • Answers come back wrong and dent credibility • Data limitations stifle analysis and lead to dead-ends • Technical language intimidates users The barriers remain. 6. GenAI can learn and customise There's an emerging answer in the form of GenAI. The ability to learn, personalise and remove technical barriers offers a chance to make data truly inclusive for all. 7. GenAI reduces time lag Low latency between asking questions and getting answers. Faster insights mean better decisions made with confidence. 8. GenAI builds credibility Accurate and reliable answers boosts trust in data-driven decisions. This supports everyone in the organisation to be data informed. 9. GenAI tailors responses GenAI assistants can change their style of language to meet the needs of their users. • Keep it simple and to the point for the more business focused users • Go deeper and more technical for analysts 10. GenAI improves data interactivity A key element of the solution is in how people interact with the data. Easily switch between graphical, text or verbal communication and work in your language of choice. The need to impose a single solution on all will be removed. So the next time someone recommends a dashboard or an Excel sheet, ask yourself: will this really help my team make consistently better decisions? If the answer is no or only partly, it may be time to turn to GenAI. See for yourself in the link below. https://www.harley.so/

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We’ve had several conversations recently with businesses looking to invest more heavily in data. They often start by saying, “We’ve been thinking of bringing this in-house but are also looking externally to see which suits us better”. Here’s my thoughts… Companies are already struggling to get away from data being seen as an operational cost with no clear ROI, and if you bring in a team too early, you’re almost guaranteed to end in the same spot. Whilst it feels like a natural step, it’s a much bigger undertaking than it immediately seems: 💰 Data is one of the highest-paid fields right now, and finding good talent (especially seniors) is REALLY tough 🦄 You probably need a range of skillsets you aren’t going to find in a single person, so the investment will likely need multiple full-time positions ⏱️ You’re often not in a position that sets new starters up for success 😵‍💫 It takes a while for new starters to get up-to-speed with any new business (3-6 months in reality) Start by asking yourself the following questions: ❓ Who’s going to manage them? ❓ Where does data sit within your leadership team? ❓ Who’s responsible for the transformation across the rest of your business? ❓ Who defines and prioritises requests? ❓ How do you protect your data team from low-value requests? If it’s not immediately obvious, it’s a clear sign that there’s work to do, but the good news is that this is where consultancy arrangements shine. They can deliver lots of value while helping define a more strategic path and getting you to a point where you're ready to bring things in-house. Big consultancy's main aim is to become irreplaceable, making you completely dependent on them. At N-ZYTE, we want you to see us as value creators. We want to deliver quick wins and make you self-sufficient as fast as possible. Our whole methodology is about setting you up for success on your own when you’re ready, and we even support you in the recruitment process for internal positions. We help you define and build your systems, processes, and methodology so that embedding new talent is seamless. What you get by working with us: ✅ Strategic thinking - using industry know-how from a pool of clients to help guide your strategy ✅ Defining ROI - identifying the exact ROI for specific data initiatives rather (i.e. quick wins vs trying to do everything at once) ✅ Talent on demand - the right skills for the job, exactly when needed ✅ Delivering immediate value with clear ROI ✅ Flexibility - scale up and down as and when you need ✅ Recruit from junior and up-skill ✅ Data Governance Framework embedded as part of our standard consulting process If you’re thinking about making investments in your data function internally and you’re interested in hearing more about what I've said, drop me a message. I’d be happy to share some more examples and have a discussion 👍

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🌟 Data Quality Tips for Your Business 🌟 We've recently spent a day with one of our clients working on their data management strategy. We had a great session discussing how they currently organise, manage and analyse their data. 📊 Part of the DataFit framework focuses on data quality, so we discussed pain points with their data and put together a data quality strategy. Part of that strategy meant scheduling in activities that would immediately look to improve the quality and management of their client data. 🧽 🌱 Ensuring your data is of good quality is crucial for making informed business decisions and driving growth 🌱 Here are some quick tips to keep your business data clean and accurate: Regular Audits 🔍: Schedule frequent data reviews to identify and correct errors. Consistency is Key 🔑: Standardise data entry formats and procedures. Validate Inputs ✅: Use automated checks to verify data accuracy at the point of entry. Employee Training 👥: Educate yourself and your team on best practices. Need help with your data quality? Message to arrange a free demo ☕ #DataQuality #SmallBusiness #DataManagement #BusinessGrowth

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As an Analyst, if you want stakeholders to take your advice then help them understand that you're willing to bear the consequences of the decision with them. This is why I prefer my teams to be decentralised. Centralised analytics teams have no skin in the game, so stakeholders are less likely to take their advice as they feel like the analysts don't have to bear the consequences. If you can't change the structure, change their perception. Use language like "we" and "us". Follow up on the analysis. Ask them for feedback. With my clients, I take the same approach. You put me on the team, I have your back. We win together, we lose together. ------------------------------------------------- Follow me and LeanConvert if you want to hear more on analytics and experimentation.

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❄ Snowflake Vs Databricks 🏗 I've noticed a bit of a shift in the last 6 months or so. Databricks requirements seem to be (for us at least) more prominent than Snowflake. I had a really interesting chat with one of the founders of a leading Snowflake partner at Big Data LDN on where he thinks Snowflake is heading in comparison to Databricks. Is Databricks going to rule supreme? Will Snowflake be back? Is Fabric going to overtake them both? What are your thoughts? #snowflake #databricks #fabric

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The best thing about working in Data Management is seeing people's reactions when it really starts paying off. Those lightbulb moments when even the sceptics can't help themselves but say: "Wow, this is actually pretty awesome, isn't it?!" Especially when the benefits were delivered far faster and easier than expected, or have turned out to be dramatically bigger than people thought they would be. Even better when previous attempts to deliver value through data have failed, and people were losing faith... that moment of realisation that the potential IS still there; it IS possible; and all the effort and investment WAS worth it, after all... It's a great feeling. What are you doing to make this happen, for your organisation? _________________________________ Baringa have a world-class Data, Analytics, AI and Solutions Engineering capability. We are different in the way we work: we put people first, bring the best from across our industry and capability specialist teams, and deliver impact that lasts. If you are interested in how we could help you, please do get in touch. _________________________________ Please do follow me if you’d like to hear more tips and thought leadership on Data Strategy, Data Management, Data Analytics, Data Culture and Artificial Intelligence. Also, if you liked this post, for 100+ more like it, check out my book, Data Value Success, available to buy on Amazon! #Data #Value #Success #DataValueSuccess #DataValue #Analytics #AI #DataManagement #DataArchitecture #DataLiteracy #DataCulture

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Are you relying on adverts to hire your Data & AI teams? Over the last few months, this image has been seen in more adverts. In our conversations with Data Leaders, they are seeing that posting a job ad and hoping for the best isn't cutting it anymore, especially for top-tier Data & AI talent. We have found: - Top Data & AI talent isn't actively job hunting. - The ‘Passive Talent Pool’ is growing, many with a wait-and-see approach to see how the UK economy performs over the next 6 months. - They're often approached through trusted networks. - Generic job postings won't attract the best. 100+ applicants put top people off applying. - 10 - 15% of ‘Active Talent Pools’ won’t apply to job adverts. If you're looking to grow your Data & AI teams, then this calls for a more strategic & proactive approach. At Animo Group, we specialise in connecting Data & AI talent with innovative businesses. We don't just post job ads; we build relationships, understand your unique needs, and deliver tailored solutions. #hiring #data #ai #talentsolutions

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Question for the Tableau people out there. Something that was possible, and I'm not sure if it remains possible... I want a dashboard on the server to be viewed via another (internal) application. All simple enough, use an iframe and display it. Yes, each possible user needs a viewer licence, but I have a solution to that. However, I don't want the users to see the dashboard on the server. The user needs View rights to see the dashboard in the app. I can put the dashboard in a project, and hide that project, so they can't find it via Projects. But if the user chooses to see Workbooks or Views via the Explore menu, the dashboard appears. Is there a way to set up the permissions to prevent the user viewing the dashboard on the server? Not relevant any more, but in a prior version of Tableau Server, it was possible simply by hiding the project. But a change to the server interface means that no longer works. #tableau #tableauserver

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💡Decisions driven with Data🔌 Many businesses struggle with the concept of 💾Data. The very word can banish what they produce (data) into something greater than being specifically relatable to bring them benefit.... Data Cultures, Data Literacy courses and organisations look to help solve this problem but it still remains a problem for many. James Miller post and self plug 😉 below outlines the things that businesses can easily relate to and that is their inability to make decisions. Procrastination is the evil twin of Ambition and it's much easier to see from a business perspective that the needle hasn't moved. Therefore Business decisions enhanced by Data could form a more interesting angle on curiosity to find out how/why the addition of Data can help?? What do you think? #decisionsbydata Tristan Mobbs, Sally Eaves, Anna Gevorgyan, Jeffrey Roberts, Tom McKay, Mark Dexter

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How will LLMs change the future of the Market Research industry? 🤖 A comprehensive article here from Kantar which covers these key points: 🔍 Summarization & Reporting: Quickly condense survey responses and organize data. 🔍 Theme Identification & Prediction: Extract insights and predict outcomes from large datasets. 🔍 Text Cleaning & Creative Writing: Improve data quality and assist in drafting materials. 🔍 Conversational AI: Enhance intelligent interviewing and natural language queries. My key takeaway from this is that the potential is immense, but we must navigate risks like data quality, biases, and ethical concerns. The future is bright, but careful implementation is key. Let's embrace the AI revolution in market research! 🌟 https://lnkd.in/eMMQHKN3 #AI #MarketResearch #FutureOfWork

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I just registered for Big Data LDN 2024! Very excited to attend my first Analytics and AI event in the UK. If anyone in my network is planning to go, reach out—I’d love to connect with as many of you as possible! 🚀 #BigDataLDN #Data #BI #Analytics #BusinessIntelligence #DataRecruitment #AI #ArtificialIntelligence

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In recent years, majority of my work has been focused on delivering change, mainly focused around Snowflake - a cloud based data platform. Maybe I could start calling myself a 'Snowflake Business Analyst' if such a title existed. Yesterday, I had a very inspiring and insightful time at the Snowflake World Tour London event at Excel London. Just listening to global thought leaders discussing pioneering work, not just in theory but in practice through demos and real-life stories was truly invaluable for me. Here are just some of the topics that really fascinated me: Impact of Knowledge Graphs on LLM, Arctic, Cortex Search, Document AI, Dynamic Tables, Iceberg, Implementing Datamesh, Snowflake Horizon and Market for Data Products. My key takeaways: ✨ Data is the most powerful asset an organisation can leverage to gain a competitive advantage. To fully utilise the power of data, you need a single source of truth which is managed and governed efficiently - Snowflake does exactly that and MORE. ❄️ Snowflake isn't ONLY intended for technical people who know a lot of SQL, rather it is for ALL users especially those who are non-technical. With the use of AI and other changes, there are some brilliant user-friendly features which are opening up the power of data to ALL business users (e.g. Cortex Analyst). Snowflake is really focused on democratising data for all. 🤖 AI is great but getting the data engineering right is a prerequisite. As the Snowflake CEO, Sridhar, started the day reminding us "No AI Strategy without Data Strategy." #SnowflakeWorldTour #London #Data #AI Will Riley Juan Sequeda Nadia Moses Matteo Carucci Miguel Morgado Lauren Roberts Guy Adams Guy Molyneux Vipul P. Venkatesh Bhimaraju Alex McCarthy Cristián L Canivell Gutierrez Achilles Varun David Ruthven Côme Grace Fawad Rebecca data.world

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One for the morning coffee... Fascinating insights from the latest TIG study! Surveying 700+ C-level executives across the UK, US, Germany, and the Netherlands, it sheds light on key trends: 🏢 Remote Working: Notable difference - 64% US vs. 33% Netherlands returning full-time to the office. 💼 Expanding Industry: 81% report increased tech investment. 🤖 Generative AI Boom: 62% prioritize hiring AI Consultants, 78% investing in automation. 🔄 Strategy Synchronization: 33% note misalignment between business & tech strategies (value creation vs value optimisation). 💡 Undervalued Tech?: Surprising stat - 63% C-Suite Tech Leaders report to CEO, 34% to COO/CFO. The disparity in tech leadership reporting structures is particularly striking. Why aren't more tech leaders reporting directly to the CEO in this tech-driven era? Thoughts? #TechLeadership #BusinessInsights #StrategicAlignment #ExecutiveSearch #CIO #TechLeaders #Technology #Boardroom #Survey #CDO #InterimManagement #Insight #Leadership

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