David Olodort

Market Insight: The average time to fill a permanent vacancy in the Data & Business Intelligence market has increased from 3-4 weeks to 7-8 weeks 😨 Add a 4 week notice period on top, you are now talking 10-12 weeks before your new hire opens their laptop for day one 😬 Advice: - If you want someone on board for September, get the ball moving now - Don't have a 3 round interview process spread out over 3 weeks - Work with specialist firms to avoid having to sift through 100's of cv's PS: They are average times, some businesses are already ahead of the game with streamlined processes & capitalising on a slow market #DataMarket #DataInsights #HiringTips #DataEngineer #MachineLearning

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What KPIs do you set when looking to hire top data talent? 📊🔍 In nearly every conversation with Data Leaders, one challenge stands out: hiring great data talent. Yet, it baffles me how few data leaders can clearly define the OKRs they’ve set (or agreed upon) to determine hiring success within their team or department. Instead, we regularly hear the same mantra: "We have hundreds of CVs." But is quantity really better than quality when hiring? 🤔📄 Data Leaders often help define KPIs and outcome targets across a company, so why are so few doing this for one of the most important tasks of any leader: acquiring the very best talent for their team? 🏅👥 If you are looking to hire quality data talent into your team, you need to be involved and not outsource the hiring work. Set objectives that will guide you to a high-quality hire. Review the key results that inform you if you are being successful. If you are not achieving the desired results, what can you change? 🔄📝 Here are some key results I advise our clients to consider. Remember, you often need the right balance of quantity and quality to ensure you are hiring the right talent in an informed way: 💫 First Interviews per Role: How many candidates are making it to the first interview stage? 👥 💫Percentage of Technical Tests Passed: What proportion of candidates are passing your technical assessments? ✅ 💫First to Second Interview Ratio: How many candidates advance from the first to the second interview? 🔄 💫Time to Hire: How long does it take to fill a position from the initial posting to the accepted offer? ⏱️ By setting and tracking these KPIs, you can ensure a more strategic and effective approach to hiring top data talent. 🚀

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My submission for the #mavenhospitalchallenge from Maven Analytics. Thank you Marjolein Opsteegh for saying "hup, hup, je kunt het". The basic thought was high level executive means 1 screen, as little filtering as possible. Some way to dive a little deeper, hence the 3 buttons to go a few periods back in time, it filters the page,. A filter on the filterpanel sets the year to 2022, in terms of completed months jan. 2022 was the last. Somebody, let's call him Jasmin Jusufbegovic, once said "I do not understand why people don't do the calculations in Excel and create a Powerpoint instead of all the fuzz in PowerBI". It is not a Powerpoint but I tried to create a nice summary with some insights apart from the dashboard. I believe insights should not be on dashboards. Dashboards are meant to stay for a few years, the world and insights can change a lot in these same years. #powerbi #data #analysis

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Would you like to run MMM or run after MMMs? I think a lot of companies run AFTER MMMs rather than letting the MMMs run FOR them. Naavics helps companies in three ways. 1. Identify and fix the delays that creeps in your data 2. Work with your teams to set Data quality standards that apply for MMM 3. Making sure the results timeline aligns with your Media planning #MMM #attribution

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PowerKPIs allows all your users to access your KPI grids and driver trees throughout the entire organization on every level. With this enhanced meta data embedding you lift your steering information to the next level. Want to know more? Contact me!

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If you think KPI cards in #tableau are great but require a lot of timw/effort to build, have a look at POWERKPIs It is basically a drag and drop KPI card and driver tree builder.

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One of the most common tactics we see when getting started with AI is dumping all your data into a model and hoping for valuable returns. But that's not best practice. Below, our Director of Data Science Bart Leusink explains the concept of Minimally Viable Data, and why quantity does not equal quality when it comes to AI. Learn more at: https://lnkd.in/eWhTP7Nj

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During our data leader mastermind group last week, we discussed the evolution of the modern data stack. Some of the main themes I took away are as follows: - The primary shift in the modern data stack is where transformations & data modeling take place (ETL -> EDW). - The modern data stack was accelerated mainly by parallelized computing and made more accessible by cloud computing. - The business drove the evolution to the modern data stack because data integration or ETL development took too long and did not meet the business's needs. - Building data culture presents the most significant challenge to driving business transformation despite modern data stack evolution. - The modern data stack has made data more accessible but may have further complicated managing the semantic layer, which makes data & AI governance harder. If this topic is interesting to you or you would like to join our mastermind group please DM me and we can find a time to chat.

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The Gartner® Magic Quadrant™ for Analytics and Business Intelligence Platforms 2024 is finally out. Recognizing Qlik as a leader for 14 years straight! Read up on our Qlik Partner Ambassador Olof Malmström thoughts on the movements below. #GartnerMQ #Qlik #DataAnalytics #BusinessIntelligence

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The knowledge or semantic layer is the foundation upon which all data infrastructure should be built upon. Unfortunately, knowledge graphs or semantic layers are seen as time consuming and “progress impeding”. Quite the conundrum…

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🔍 𝐎𝐩𝐞𝐧𝐀𝐈 𝐢𝐬 𝐬𝐡𝐢𝐟𝐭𝐢𝐧𝐠 𝐟𝐚𝐬𝐭. More funding, more pressure. Will progress speed up? Maybe. 💡 But leadership changes raise questions. 𝐒𝐚𝐦 𝐀𝐥𝐭𝐦𝐚𝐧 stays in charge. 𝐈𝐥𝐲𝐚 𝐒𝐮𝐭𝐬𝐤𝐞𝐯𝐞𝐫 and 𝐌𝐢𝐫𝐚 𝐌𝐮𝐫𝐚𝐭𝐢 have left. The challenge: balancing innovation with business. ⚖️ With more money, will they take fewer risks? For Data Scientists, this could mean: 🔹 More resources, but… 🔹 Less room for bold experimentation. OpenAI’s future is uncertain. Will it keep leading? 🚀 - - - 𝐖𝐡𝐲 𝐢𝐬 𝐎𝐩𝐞𝐧𝐀𝐈 𝐩𝐥𝐚𝐧𝐧𝐢𝐧𝐠 𝐭𝐨 𝐛𝐞𝐜𝐨𝐦𝐞 𝐚 𝐟𝐨𝐫-𝐩𝐫𝐨𝐟𝐢𝐭 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐚𝐧𝐝 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐦𝐚𝐭𝐭𝐞𝐫? https://lnkd.in/dkWzrsPX .

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Many data leaders are getting told there isn't $300k in the budget for two more data people - but there's probably $10k to train 5 of your existing team members to drive more business impact with data. That's how much it costs to send 5 people to the Top Data People six-month professional development program. We work with senior and mid-level data people to help them become much better at driving business impact with data. At that level, it's typically not the lack of technical skills that hold you back from being impactful - it's the countless business and leadership skills you need to develop as a data person that most learn the hard way. The program is based on my experience managing hundreds of data people and seeing what the top data people did differently that made them so effective. The price is cheap, I know... You get a six month professional development program for less than an early bird ticket to the Snowflake conference - and you don't even need to pay for flights, hotels and meals! I'll probably raise the price in the future! But we're still in the early days, so all the early customers get this insane discount vs. a typical 6 month professional development program. Get it while it lasts! And random tip - if you're being told no on additional headcount - AT LEAST negotiate for something. It's a bad signal if you ask, get told no, and then just give up. Try to find a different creative solution to help with what you needed the headcount for in the first place.

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Five data terms every business leader should understand: 𝐃𝐚𝐭𝐚 𝐰𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞 - centralized place where your data goes for aggregation 𝐈𝐧𝐭𝐞𝐫𝐨𝐩𝐞𝐫𝐚𝐛𝐢𝐥𝐢𝐭𝐲 - allowing your software/tools to speak to one another 𝐃𝐚𝐭𝐚 𝐬𝐢𝐥𝐨𝐬 - no visibility or cohesion across teams/departments 𝐀𝐏𝐈 - programmatically moving data (create/read/update/delete) 𝐄𝐓𝐋 - end-to-end process of aggregating data

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2010: “the problem (for analytics) is that our data is messy, siloed and all over the place” 2016: “the problem (for BI and ML) is that our data is messy, siloed and all over the place” 2024: “the problem (for AI) is that our data is messy, siloed and all over the place”

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Friday night, publishing night, make sure you're not invest much more time. The Maven Analytics #mavencommuterchallenge in plotly/dash. I could have made a video instead of deploying, all screens would have looked neat. There is a small problem left in the layout, which is only in the deployment environment. Legend acts differently & plot titles not aligned above. Those two plot parameters were not happy in the deployment environment. Imagine the light mode, I removed it, sorry. Thank you Adam Schroeder for your clear video about deployment to pythonanywhere. #plotly #dash #bootstrap #data #analysis https://lnkd.in/eg5NfQhp

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Unpopular opinion: Most data podcasts these days are B-O-R-I-N-G. Same CEOs of big data companies on what's happening in the data space and predications on what's next. We're fully guilt of this 🙋‍♂️ we ran one ourselves... So we decided to make a podcast that’s not... ✨People of Data is something completely different. We're excited to put the spotlight on the people who are using data day in and out—data leaders at some of the most recognizable brands in the world. We want to give you an inside peek of what it's like working in the data teams of these companies. Discover how they are using data, their achievements, and the challenges they face. Season 1 is dropping on September 26—and we’re doing it Netflix style: all 10 episodes at once. We’ve brought in the best data minds from leading organizations like MoonPay, Gong, Pleo, WeWork, Financial Times, Freshworks, Virgin Media O2. These are conversations you won’t find anywhere else. Each episodes is 20 minutes, so it's perfect for commutes, morning routines, or in between meetings. We're already more than half way done with season 2 but tag a data leader or company you want us to feature and we'll try to squeeze them in #peopleofdata. If you want to be notified when season 1 drops, sign up in the link below .👇

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NO, YOUR 3 PERSON DATA TEAM DOES NOT NEED 6 TOOLS TO MANAGE THEIR DATA INFRASTRUCTURE. Focus on getting your data where it needs to go, and investing in your processes - build your stack as your needs arise. To start out, your team usually needs an ingestion tool, a transformation tool (e.g. dbt Labs) and a data warehouse and/or lakehouse (you cannot go wrong with Snowflake or Databricks). That’s it.

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😤 Frustrated with Tableau actions? You’re not alone. Set and Parameter actions are some of the most powerful tools in Tableau. But many users struggle with knowing when to use each. Misunderstanding these can leave your dashboards feeling less interactive, and your insights less impactful than you would like. It’s time to stop agonizing over which action to use when! 🙌🏻 In my upcoming 𝗳𝗿𝗲𝗲 𝘄𝗲𝗯𝗶𝗻𝗮𝗿, I’ll break down the exact differences between Set and Parameter actions, showing you how to leverage each for maximum impact. Ready to stop the confusion and start building interactive dashboards that captivate your stakeholders? 📅 𝗝𝗼𝗶𝗻 𝗺𝗲 𝗼𝗻 𝗦𝗲𝗽𝘁𝗲𝗺𝗯𝗲𝗿 𝟮𝟰 𝗮𝘁 𝟮𝗽𝗺 𝗕𝗦𝗧 / 𝟭𝟭𝗮𝗺 𝗘𝗧 as I unveil the secrets of set actions vs. parameter actions. You'll come away with tons of ideas that you can implement immediate. 🔗 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://lnkd.in/enZmKQVj

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Less is more. A lot of data teams have data stacks like the Raptor 1. In reality, only 5% of teams have a business complicated enough to need that level of complexity. Most teams can get more done with less and should work with less.

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