Zyed Jamoussi

In a landscape where privacy regulations and browser decisions are reshaping digital marketing, early adoption of cookieless solutions is a clear signal that adapting to change is critical. Companies that embrace cookieless targeting now are not only future-proofing their strategies but also finding ways to maintain and grow customer trust. As more brands rely on first-party data and privacy-first approaches, those leading the shift are setting a new standard for resilient, customer-centric marketing. Commanders Act

3

What’s a Semantic Layer? 🧩 Think of it as a "translator" for your data! A semantic layer organizes and standardizes terms across all your data sources, so everyone is speaking the same data language. 🌎 Why It’s a Game Changer 🎮 Unified Language for Data 📖: With a semantic layer, "customer count" or "revenue" means the same thing for everyone, from execs to analysts. No more guessing or mixed definitions! Self-Service & Speed 🚀: Analysts can quickly find the data they need without hunting for details. Less tech support needed, more time for insights! Consistency in Reporting 📊: When everyone pulls from the same source, reports are aligned. The CFO, marketing team, and product leaders all see the same numbers and can act fast! Data You Can Trust 🤝: The semantic layer is like a “seal of quality” for data—no messy columns, no misunderstandings, just solid, accurate insights! In short, a semantic layer is the friendly data helper that brings harmony to your data world, making decision-making faster, easier, and way more fun! 🎉 What are your thoughts and tools and techniques that you have used? #semanticlayer #dataAnalysis #SourceOfTruth #dataEngineering #BuildOnceUseManyTimes #DataStrategy

20

Last week we took part in the third edition of McKinsey France's Tech Services Circle, which delved into the challenge of "Boosting Operating Margin Performance". This event has once again provided in-depth insights and actionable strategies to reshape the Tech Services Industry. The agenda for this third edition focused on: 🛡 Focus on Margin Protection: Strategies to protect your margins amidst changing market dynamics 📊 Pricing as a Game Changer: Leveraging pricing to drive operating margins higher 👥 Modernising People Supply Chain: Real-life case studies showing how modernisation can deliver a 3-5 point increase in margins On the stage, our CEO Jean-Philippe Couturier shared his insights with industry thought leaders: Charles-Henri Rannou, Marc-Antoine de La Chevasnerie and Pierre Watchi from McKinsey & Company. Each brought a unique perspective on using pricing strategies and modernising the people supply chain to deliver significant margin improvements. At Whoz, we are committed to turning these insights into action, setting new industry benchmarks and driving sustainable growth. 🔗 Want to dive deeper into how these winning strategies might help your business? Let’s connect! #TechServicesCircle #OperatingMargins #PeopleSupplyChain #BusinessStrategy

47

🌐 New Insight: AI & Advanced Analytics Transforming French Healthcare - Part 3 Released! Thrilled to share the third installment of our series, co-authored with Anna Lomsadze, exploring the transformative impact of artificial intelligence (AI) and advanced analytics (AA) in healthcare. This article highlights international success stories, showing how these technologies have been implemented to address critical challenges and providing considerations for their broader adoption in France. We highlight three key areas where AI and AA are driving significant impact: #OperationalEfficiency, #HealthManagement, #Innovation 👉 Read the full article here: https://lnkd.in/eRa_9K-Q Join the conversation with the #EnterpriseStrategy team of IBM France as we discuss how these transformative technologies can reshape healthcare. Your thoughts and feedback are always welcome! Eric Masson, Thibault Pironneau, Cécile Waquet, Alexandra Ruez, Alex Bauer, Angela Spatharou

41

5 commentaires

🏅 Paris 2024: When Data Meets Reality 🏅 In our final year at MBA Spécialisé Digital Marketing & Business at ESSCA Shanghai, Antoine Mignot, Ilan T. and I took on an interesting challenge: predicting the performance of France in the Paris 2024 Olympics. Based on historical data, we found that, on average, the host country improves its rank by 146% compared to its average rank when not hosting. This led us to predict that France would land in 4.6th place—which, when rounded up, would be 5th in the final medal standings. Now that the games have ended, France actually placed 5th—exactly as our analysis suggested !  While our model was only a beginner project, it’s a reminder of the fascinating insights data analysis can provide, even in the unpredictable world of sports. The exercise was a perfect introduction to data science — big thanks to Leandro Loss who taught this class — and I'm excited to share that I've been working in data analytics for over a year at fifty-five, where I'm continuing to explore how data can drive insights and inform decisions in diverse fields. #DataScience #Paris2024 #Olympics #PredictiveAnalytics #SportsAnalytics #mbadmb

34

2 commentaires

Round up time! Four recent articles about data quality and my spicy take 🌶️ Marc Di Gaspero and Adrien Vermeirsch from Potloc have done extensive research on data quality, and now Marc is sharing some key insights! Out of over 26,000 entrants, the average was 22 daily survey attempts (!), with similar results across five major online panels and five sample exchanges/DIY solutions.  The best part: High-frequency survey takers is an easy problem to solve - why don’t we? Because less traffic = less revenue. 🤷 In the article “$urprise! Re$pondent$ are not altruistic,” Vignesh Krishnan at Rep Data discusses the significant drop in incentives over the past 10 years (his rough estimate is 71% 😮). While increasing incentives could potentially attract more fraud, he argues that as more good respondents participate, the proportion of bad respondents would decrease.  Fair enough—so why don’t we increase incentives? Because it will increase the CPI and not the profit margin. 🤔 Chris Atkins at Yonder Data Solutions writes about how “the research industry is focusing too much on managing the symptoms of poor data quality rather than addressing its root causes.” I agree 100%. Our response to the data quality crisis is to build bigger armies. We need fraud detection software and QC checks, but that’s not fixing the root cause of the problem.  😐 Terence R. McCarron at OpinionRoute writes about how ‘conversion rate’ is what dictates the CPI, not the old ‘Incidence Rate.’ This matters because quality removals lower conversion rates, which is generally bad for business if you’re a sample provider. He says, “quality is not a priority that’s assured through the economic model.” Amen to that. 🙌 Links in the comments 👇 Your thoughts? 💬 #rmx #dataquality #surveys 

98

22 commentaires

Data leaders significantly impact team success by understanding the business, acting as a liaison between business and data, and ensuring data quality. They focus on functional steps rather than technical details, define terms and functions clearly, and assign roles intentionally. This approach helps avoid decentralized processes and makes the data team irreplaceable in company initiatives

1

🎯 VN1 Forecasting Competition report The VN1 forecasting competition, organised by Nicolas Vandeput, is now in full swing. The datasets are available to download, and the first phase ends on 3rd October. It is not too late to participate, see the first comment below for more detail. The challenge is to make accurate 13-week-ahead forecasts for sales volume across over 15,000 combinations of client, warehouse and product. I made a submission yesterday which pushed me up from 20th place up to 8th place (out of 41) on the leaderboard. I still have more ideas for improvement (as presumably the other competitors also do), so it will be interesting to see how things evolve over the coming days. My objectives for the competition are: - Enhance further my Python coding skills for data cleaning and processing - Deepen further my understanding of machine learning forecasting models - Enjoy the challenge of understanding patterns within a new dataset Here are some takeaways: 1. Benchmark comparison is crucial. For over 20 years, my go-to benchmark has been a 4-week weighted average with weights of 0.4, 0.3, 0.2 and 0.1. If a forecasting model doesn’t beat a simple benchmark, it’s time to try something else. 2. Beating the benchmark can be challenging. My initial model amendments performed worse than the benchmark. It was a relief once I had beaten the benchmark. Finding improvements is an iterative process, and it can be frustrating when trying something new makes the error larger. 3. Python’s power and efficiency are excellent for modelling a large number of time series. Although getting the code right is taking me longer than performing the analysis in Excel, I find that any subsequent tweaking and rerunning the Python code is much more convenient compared with managing an Excel workbook. I’ll share a full report of forecasting findings at the end of the competition. I’m looking forward to finding out how other competitors have approached the challenge. I regularly share my thoughts on #Forecasting, #ResourcePlanning, and #PerformanceImprovement in #CustomerOperations. Please connect with me, hit the 🔔 on my profile, and select “All” to stay updated.

48

10 commentaires

Unpopular opinion: Just because you have beautiful dashboards that present 1500 data points doesn't mean you're in control. The number one challenge when dealing with data is identifying which data points to truly focus your efforts on. A question I like to ask in Conquête (or to my clients) is: Which data obsesses you?! In this excerpt, three particularly interesting examples: Guillaume Roques - Senior Marketing Director @ Google Cloud Thibaut Davoult - VP Growth & Ops @ Livestorm Maxime Baumard - CMO @ Pennylane Find the full version here 👉 https://lnkd.in/dvNT7rpg Or on all podcast platforms. PS : English Subtitles are availbale on Youtube

7

1 commentaire

🌐 AI & Advanced Analytics in French Healthcare System – Part 3 is Here! Proud to announce the release of the third part of our series on the transformative role of AI and advanced analytics in french healthcare, co-written with Luis Jimenez. This journey has been a great learning experience, and I’m excited to share insights we uncovered. In this article, we explore: ✅ Innovative ways AI and AA are revolutionizing healthcare through improved operational efficiency, enhanced health management, and breakthrough innovations. ✅ Global success stories showcasing impactful implementations. ✅ Key considerations for scaling these solutions in France. 📖 Read the full article here: https://lnkd.in/eRa_9K-Q Eric Masson Thibault Pironneau Cécile Waquet Alexandra Ruez Alex Bauer Angela Spatharou IBM

17

Here are 3 reasons why I am switching my content in English : (Petit sondage à la fin!) Little survey at the end 👇 3 reasons : 1️⃣ Industry Standard: In the tech and data fields, most of the literature, tools, and conversations happen in English. 2️⃣ Access to the Latest Information: Most cutting-edge research, case studies, and innovations in data science, machine learning, and AI are published in English first. 3️⃣ English-language content offers a wider variety of educational resources, from technical documentation and tutorials to expert blogs and podcasts. For my information, what if your level of english ? Celebrate (Bravo) : fluent, I can make jokes in English Love (Coeur) : professional level, I can speak and understand easily Insightful (Idée) : basic level Like (Pouce bleu) : difficult but I try to manage Support (Soutien) : je ne connais pas l'anglais Funny (Drôle) : je fais mes requêtes SQL en français

13

3 commentaires

Learning about root-cause analysis will turn you into a problem-solving expert. Here’s how to uncover the real reasons behind your issues! 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: Start by clearly stating the issue. For example, “We’ve observed a significant decrease in sales in France over the last few days.”     2. 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: Collect the relevant data like order processing times, customer service interactions, inventory levels, and active marketing campaigns.     3. 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: Use tools like SQL, Python, or Excel to analyze the data. Look for patterns, trends, and anomalies that could point to the root cause.     4. 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: Brainstorm all possible reasons for the issue. Use methods like the 5 Whys technique to dig deeper into each potential cause.     5. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Test your hypotheses against the data. Does the data support your assumptions? If not, refine your hypotheses and test again.     6. 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: Once you’ve identified the root cause, support the business by showing possible solutions to address it. Monitor the results to ensure the issue is resolved. 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: You notice an increase in customer lead time. Here’s how you might tackle it: • 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺: “Customer lead time has increased by 20% in the last three months.”    • 𝗚𝗮𝘁𝗵𝗲𝗿 𝗗𝗮𝘁𝗮: Collect data on order processing, sales deviations from the forecast, and shipping times.    • 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝘁𝗵𝗲 𝗗𝗮𝘁𝗮: You find that the actual sales are in line with the forecast and  shipping times have remained constant, but order processing times have increased significantly.    • 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗮𝘂𝘀𝗲𝘀: Consider factors like outages in the warehouses,  staffing issues due to high sickness rates, or process inefficiencies due to operating close to the maximum capacity.    • 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗛𝘆𝗽𝗼𝘁𝗵𝗲𝘀𝗲𝘀: Data reveals that a spike in the sickness rate has reduced the available workforce.    • 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀: You propose an increase in capacity planning puffer from 5% to 10% and the hiring of additional temporary workers to fix the situation in the short term. Monitor lead times to ensure improvement. By learning how to perform proper root-cause analysis, you will become an important problem-solving partner for your stakeholders. How do you ensure you’re addressing the root cause of an issue and not just the symptoms? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #rootcauseanalysis #problemsolving #careergrowth

93

52 commentaires

🌟 𝗪𝗲𝗲𝗸𝗹𝘆 𝗔𝗜 𝗨𝗽𝗱𝗮𝘁𝗲𝘀 (#13) 🌟   Welcome in the 13rd edition of the weekly AI updates, and now let’s dive into the latest news about AI on LinkedIn:   1️⃣ We start with Redha C., with a nice question: What really is analytics engineering ? 🖥️ https://lnkd.in/eCyhADBQ   2️⃣ Let’s continue with Yasmine DOUADI and the migration of Mistral AI in US while OpenAI goes on Paris: 🇫🇷 🇺🇲 https://lnkd.in/eRkFhKeX   3️⃣ Why are Novartis, J&J, and BMS biopharma patent leaders in 2024? To get the answer, let's take a look at this brilliant piece of article from Brian K. Buntz: 📔 https://lnkd.in/e6Qmcfh4   4️⃣ In LLMs, having too many weights could be cumbersome for several reasons... 😵‍💫 Then, what happens if we remove 50% of Llama ? The answer is here with Philipp Schmid: https://lnkd.in/etfMHFmQ   5️⃣ Andrés Marafioti recently announced the release of SmolVLM, a small 2B VLM that generates 17 tokens/sec on a MacBook and is 7.5 to 16 times faster than Qwen2-VL ! 🚀 https://lnkd.in/eZ3CKpsU   6️⃣ Congratulations to Enveda, an AI-driven drug discovery startup, who secured $130 million to transform drug development. 👏 Thanks to Ollie Gearing for your post. https://lnkd.in/e4k2PCs8   7️⃣ From Isomorphic Labs: Take a look at this nice video where Max Jaderberg, CEO of the company, explains how AI is saving billions of years of human research time. 💡 https://lnkd.in/eQKUWJ6V   8️⃣ And we finish with Max Jakobs and a really nice article on the state of AI in Drug Discovery, and especially where we are now. 📔 https://lnkd.in/eE3unNzB   🙏 Thank you for reading and your support, and please share if you find it insightful! Wishing you all a great day ! 🫶   #LLMs #DataAnalytics #AI #DrugDiscovery #AIweeklyUpdates

18

2 commentaires

Proud to introduce you to (part of) the team working on the next evolution of Starzdata 🚀 All of this would be a waste of time without the big clients and friends who shared their use cases for a better utilization of Data and AI… thank you Deloitte Oliver Wyman AlixPartners EIGHT ADVISORY AXA en France Zelros Heaviside Centolla Kayrros Carrefour for the precious feedbacks 🙏 🤭 It’s amazing to see that those user pain points still fail to be addressed by multi-billion dollars companies and well funded startups… but that’s the theory of #leanstartup 😀. We found our answer with this amazing group (please apologize our poor diversity ranking), leveraging the last Open Source frameworks and the great Cloud infrastructure of OVHcloud and Google Cloud 💪 : it’s not a data catalog, it’s not a RAG, it’s not an AI Studio… it’s something that anybody can use, provided you have a question to answer or a dataset to enrich. If you want to test our solution, please reach out to me directly and we’ll open a dedicated secured instance for your company. For those working in New York, I’ll be happy to meet with you in July and make a private demo 🇺🇸 #data #generativeAI

67

8 commentaires

Keen to discover how to harness Data Science and Machine Learning to Improve demand forecasting? 📈 Watch the replay of our latest webinar where we delve into this topic and unveil our solution FaaS4U, combining the robustness of AI with a fresh perspective to ensure effective stock management. 💡 Or reach out for a private session of knowledge sharing! 💬 Stay ahead with FaaS4U! #DataScience #MachineLearning #Forecasting #AI #StockManagement #axialyze #teamingforvalue #fromvisiontoreality

9

Question How long time does it take to build a beta version of a specific-business web site, and to make it available online for testing ? Answer Less than three minutes ! In this demo Alkhemy is used to develop a beta version of a software quality management tool and deploy it for testing. Check it out at https://www.alkhemy.io 🔥 #artificialintelligence #aigeneratedcode #ai #largelanguagemodels

12

1 commentaire

Balsam Brands chief technology officer France Roy needs its data and analytics team to do more than just pull reports. The team needs to easily access its metrics and help its business teams make data-driven decisions, like identifying high-value customer segments and then market to them appropriately. "Everything is ingested, cleaned up, and then any new data set that needs to tie that model, we have one place to go and do that... [so] business users have data at their fingertips and can make those decisions in real time versus waiting weeks or months to get the right data to then make a decision." https://lnkd.in/gkc99sdb

28

1 commentaire