Modern Data 101

𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 𝐒𝐞𝐥𝐟-𝐒𝐞𝐫𝐯𝐢𝐜𝐞 was one of the biggest turning points in platform engineering, which boosted the adoption of Internal Developer Platforms at scale and led to much better experiences for developers and ops teams. IDP’s fundamental pillars highlight 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐬𝐚𝐭𝐢𝐨𝐧, 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 of repeatable processes, and 𝐀𝐛𝐬𝐭𝐫𝐚𝐜𝐭𝐢𝐨𝐧/Templatisation of complex underlying ops, enabling developers to focus on application development at large. We are at a point in the Data Industry where large infrastructures and complex processes are constantly running behind the scenes. Not surprisingly, life is challenging for both Ops and Data Developers with piled-up tasks and requests and barely much time to focus on data-facing applications. 💡 𝐖𝐞 𝐧𝐞𝐞𝐝 𝐈𝐃𝐏 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚! While the transient element of Data makes the reflection a little more complex than it ought to be, the core principles are extremely solid guidelines that have garnered wide support so far. Data Developer Platforms (DDP) was published last year as a standard for developing an IDP for data. It is open for platform developers and organisations to implement and customise to their specific requirements or priorities. Here’s a quick overview of DDP, briefly covering the basics of: ➡️ Why an IDP for Data ➡️ What’s a DDP ➡️ Who does it Impact directly Feel free to hop over to datadeveloperplatform.org for a detailed spiel! #IDPforData #DataDeveloperPlatform

🚀 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 This week's top data posts and conversations! 🔷 Julia Bardmesser on: 𝐖𝐡𝐲 𝐌𝐨𝐬𝐭 𝐃𝐚𝐭𝐚 𝐂𝐚𝐭𝐚𝐥𝐨𝐠𝐬 𝐅𝐚𝐢𝐥 Julia Bardmesser highlights two key reasons data catalogs often fail: difficulty in connecting business metadata with technical metadata (First Mile Problem) & ensuring catalogs are used effectively (Last Mile Problem). Success requires integrating catalogs into daily workflows & focusing on valuable business metadata to prevent underutilization and abandonment. Read more: https://lnkd.in/dxqqEPDn 🔷 Andrew Jones on: 𝐇𝐨𝐰 𝐔𝐧𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞𝐝 𝐔𝐬𝐞 𝐨𝐟 𝐊𝐚𝐟𝐤𝐚 𝐋𝐞𝐚𝐝𝐬 𝐭𝐨 𝐏𝐨𝐨𝐫 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 Andrew Jones emphasises that improper use of Kafka results in poor data quality. To use Kafka effectively, disciplined practices like designing appropriate schemas, implementing change management, & utilising a schema registry for data validation are crucial. Read more: https://lnkd.in/ddJ6eUU2 🔷 Malcolm Hawker on: 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐂𝐚𝐭𝐚𝐥𝐨𝐠𝐬 Malcolm Hawker discusses the commoditization of data catalogs, questioning their future as differentiation among vendors fades. While essential for modern data estates, catalogs may pivot towards knowledge management, capturing insights on business performance rather than just inventorying data. Read more: https://lnkd.in/dR3NFgc2 🔷 Maryam Miradi, PhD, on: 𝐆𝐫𝐚𝐩𝐡𝐑𝐀𝐆—𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭'𝐬 𝐍𝐞𝐰 𝐌𝐨𝐝𝐮𝐥𝐚𝐫 𝐑𝐀𝐆 𝐓𝐨𝐨𝐥 Maryam Miradi talks about GraphRAG, a tool leveraging LLMs to create knowledge graphs for answering complex questions. It excels where keyword searches fail but requires domain experts to verify responses. Though powerful, GraphRAG’s effectiveness depends on well-constructed indexing, making it resource-intensive. Read more: https://lnkd.in/dAKcYvzF 🔷 Chad Barendse on: 𝐈𝐬 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐂𝐚𝐭𝐜𝐡𝐢𝐧𝐠 𝐔𝐩 𝐭𝐨 𝐀𝐈? Chad Barendse discusses the challenge of data governance keeping pace with AI advancements. To manage risks, he suggests setting data guardrails, involving governance teams in AI initiatives, conducting risk assessments, and improving AI and data literacy within teams. Read more: https://lnkd.in/dAchwbbu 🔷 Alireza Sadeghi on: 𝐒𝐢𝐧𝐠𝐥𝐞-𝐍𝐨𝐝𝐞 𝐄𝐧𝐠𝐢𝐧𝐞𝐬' 𝐑𝐢𝐬𝐞 𝐢𝐧 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 Alireza Sadeghi highlights the growing relevance of single-node engines like DuckDB & DataFusion due to advancements in hardware. With most companies processing less than 1 TB of data, these systems are becoming cost-effective alternatives to distributed engines. Read more: https://lnkd.in/dy5NETwQ 🔔 Follow 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 101 & stay updated with our weekly highlights of the most engaging discussions in the data community. Got a post or conversation that caught your eye? Share it in the comments! 💌 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐨𝐮𝐫 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dDT7FB3y

📚 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 𝐏𝐢𝐜𝐤𝐬: 𝐓𝐡𝐢𝐬 𝐰𝐞𝐞𝐤'𝐬 𝐌𝐮𝐬𝐭-𝐑𝐞𝐚𝐝𝐬 Dive into this week's top resources & be at the forefront of the data world! 🚀 𝐓𝐚𝐥𝐤 𝐃𝐚𝐭𝐚 𝐭𝐨 𝐌𝐞: 𝐓𝐡𝐞 𝐀𝐫𝐭 𝐨𝐟 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐓𝐫𝐚𝐧𝐬𝐥𝐚𝐭𝐢𝐨𝐧 Annie P. explores the critical role of analytics translators in bridging the gap between data teams and business stakeholders. Annie emphasizes that simply being data literate isn't enough; professionals must also communicate insights in a way that aligns with business goals. Read more: Click the first link in the comments. 🚀 𝐖𝐡𝐲 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬? Karthik Ravindran discusses the growing significance of data products in data transformation. He addresses the recurring question of why data products matter, introducing the CAP framework—Consumer centric, Audience oriented, and Purpose driven. Read more: Click the second link in the comments. 🚀 𝐁𝐫𝐢𝐧𝐠𝐢𝐧𝐠 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐭𝐨 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 Dorian Drost discusses the use of path models, also known as Structural Equation Models (SEMs), to explore and test relationships between variables. Drost introduces key concepts like mediators and moderators and demonstrates how the Python package semopy can be used to model these relationships. Read more: Check the third link in the comments. 🚀 𝐇𝐨𝐰 𝐭𝐨 𝐄𝐟𝐟𝐞𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐃𝐚𝐭𝐚 𝐟𝐨𝐫 𝐒𝐞𝐥𝐟-𝐒𝐞𝐫𝐯𝐢𝐜𝐞 𝐃𝐚𝐭𝐚 𝐓𝐞𝐚𝐦𝐬 Kristof Martens discusses how the traditional medallion architecture—categorizing data into bronze, silver, and gold tiers—may limit self-service data teams by imposing rigid structures. He suggests adopting a data product thinking approach, where teams independently manage and share data products with greater autonomy and flexibility. Read more: Check the fourth link in the comments. 🚀 𝐃𝐚𝐭𝐚-𝐅𝐢𝐫𝐬𝐭 𝐯𝐬 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 Eric Sandosham, Ph.D., explores the differences between "Data-First" and "Data-Driven" approaches. "Data-First" focuses on designing systems and processes with relevance and quality of data at their core, while "Data-Driven" emphasizes a cultural shift towards using data to inform decision-making. Read more: Check the fifth link in the comments. 🚀 𝐃𝐢𝐬𝐜𝐨𝐯𝐞𝐫𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐍𝐞𝐱𝐭 𝐂𝐡𝐚𝐩𝐭𝐞𝐫 𝐰𝐢𝐭𝐡 𝐒𝐨𝐥 𝐑𝐚𝐬𝐡𝐢𝐝𝐢 Lindsay Murphy indulges in an insightful conversation with Sol Rashidi, Head of Technology at Amazon's Startup Division. They explored the topics like: knowing your career's "shelf-life," the value of networking, and transitioning from technical roles to executive positions. To checkout the entire episode, click the sixth link. Discover MD101's resource page offering the best data wealth, curated just for you. Get weekly updates to stay ahead of modern data trends: https://lnkd.in/dWRBjVCb 🔔 Follow 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 101 & stay updated with our weekly highlights from the data space.

𝐃𝐨𝐧'𝐭 𝐓𝐫𝐮𝐬𝐭 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐘𝐞𝐭? 𝐆𝐚𝐦𝐞 𝐓𝐡𝐞𝐨𝐫𝐲 𝐌𝐢𝐠𝐡𝐭 𝐂𝐡𝐚𝐧𝐠𝐞 𝐘𝐨𝐮𝐫 𝐌𝐢𝐧𝐝 In this latest edition, Authors Animesh Kumar & Travis Thompson explain how failures in centralization, misinterpreted decentralization, and how data products can be the key to smart decentralization. Some of the key takeaways from the article: 🔍 𝐓𝐡𝐞 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐆𝐚𝐦𝐞 𝐓𝐡𝐞𝐨𝐫𝐲 𝐢𝐧 𝐃𝐚𝐭𝐚 Game theory has evolved beyond just mathematics—it’s now a framework for understanding competition and collaboration in various fields, including data. Von Neumann focused on centralization, while Nash shifted the focus to independent strategies, creating relevance for today’s complex ecosystems. 👥 𝐖𝐡𝐲 𝐂𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐅𝐚𝐢𝐥𝐬 Centralized data teams, meant to serve all domains, are overwhelmed. Just like in game theory’s outdated centralized approaches, organizations are finding this model too slow and inefficient for modern demands. What went wrong with the centralization approach? 🚫 𝐓𝐡𝐞 𝐑𝐢𝐬𝐤𝐬 𝐨𝐟 𝐌𝐢𝐬𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐞𝐝 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Complete decentralization can become costly and chaotic if not managed correctly. Each domain has its own independent infrastructure, which is a recipe for inefficiency as well as overwhelming and mostly hidden costs. 𝐌𝐢𝐬𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐢𝐧𝐠 𝐝𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐜𝐚𝐧 𝐛𝐞 𝐣𝐮𝐬𝐭 𝐚𝐬 𝐝𝐚𝐧𝐠𝐞𝐫𝐨𝐮𝐬 𝐚𝐬 𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 ⚠️ 🤝 𝐒𝐦𝐚𝐫𝐭 𝐃𝐞𝐜𝐞𝐧𝐭𝐫𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: 𝐓𝐡𝐞 𝐇𝐲𝐛𝐫𝐢𝐝 𝐌𝐨𝐝𝐞𝐥 The solution? Hybrid decentralization. In this model, domains control their own data products, while centralized governance ensures alignment across the organisation. How does this structure balance autonomy with collaboration? 🎯 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 𝐚𝐬 𝐏𝐥𝐚𝐲𝐞𝐫𝐬 𝐢𝐧 𝐆𝐚𝐦𝐞 𝐓𝐡𝐞𝐨𝐫𝐲 In this model, data products are like independent "players" in game theory, constantly adjusting based on the needs and strategies of other domains. Want to understand how this strategy brings efficiency to your data ecosystem? 🔄 𝐀𝐜𝐡𝐢𝐞𝐯𝐢𝐧𝐠 𝐚 𝐃𝐲𝐧𝐚𝐦𝐢𝐜 𝐍𝐚𝐬𝐡 𝐄𝐪𝐮𝐢𝐥𝐢𝐛𝐫𝐢𝐮𝐦 𝐢𝐧 𝐃𝐚𝐭𝐚 The key takeaway: as each domain optimises its data products, the entire system remains agile and evolves. How does this continuous optimisation benefit the whole organisation? Discover how the principles of Nash equilibrium create a thriving, decentralized data ecosystem. 𝐑𝐞𝐚𝐝 𝐡𝐞𝐫𝐞: https://lnkd.in/dea24EKw 📢 Do share what you feel about this take! 🗣️𝐒𝐡𝐨𝐮𝐭 𝐨𝐮𝐭: At MD101, we actively collaborate with data experts to bring the best resources to a blooming community of data practitioners. If you are willing to share an insightful perspective on anything about data, we’re all ears!! 🔔 Follow Modern Data 101 and stay updated with our weekly highlights from the modern data space. #datamanagement #datastrategy #dataproducts

📊 𝐓𝐡𝐞 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐓𝐫𝐚𝐩: 𝐓𝐡𝐞 𝐂𝐨𝐦𝐦𝐨𝐧 𝐏𝐢𝐭𝐟𝐚𝐥𝐥𝐬 𝐨𝐟 𝐭𝐡𝐞 𝐒𝐡𝐢𝐧𝐲 𝐂𝐡𝐚𝐫𝐭𝐬! Organizations love dashboards, but fancy charts don’t always translate to real decisions. With just 30% adoption, a 4.5-day average report time, & 84% of frontline business workers dissatisfied, it’s clear we’re missing the point. Dashboards can be game-changers—but only if you address their flaws before they derail insights. In this post, we’ll dig into the common pitfalls that hold dashboards back & how to fix them so your team can turn data into real action. 💾 𝐓𝐡𝐞 𝐝𝐚𝐭𝐚 𝐨𝐯𝐞𝐫𝐥𝐨𝐚𝐝: Peter Fishman elaboratively talks about such debates on dashboards, where he focuses on how there is often an explosion of "critical" information to the point it becomes overwhelming & a noise (statistically & also in terms of meaningful information). Other times, data & pipelines become stale & erode trust in data-driven decision-making with unreliable insights. 📊𝐓𝐡𝐞 𝐊𝐏𝐈 𝐩𝐫𝐨𝐛𝐥𝐞𝐦: With overwhelming data overload, selecting the right datasets is already a challenge. On top of this, prioritizing KPIs and metrics that align with business objectives is critical. Data must be presented with clear context that explains the metrics displayed on the dashboard, ensuring actionable insights. 🕳️𝐘𝐨𝐮𝐫 𝐝𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐥𝐚𝐜𝐤𝐬 𝐜𝐨𝐧𝐭𝐞𝐱𝐭: A dashboard being extremely viewer-specific is a major challenge often, as your understanding of dashboarding might be clear, but your partners may not share the same analytics expertise. As Chris Dutton mentions, your dashboard might look very cool, but there can be a heavy disconnect between what the designer wants to build & what end users actually need! Clearly, there is often no connection to specific & measurable business outcomes. ⚠️ 𝐃𝐚𝐬𝐡𝐛𝐨𝐚𝐫𝐝 𝐚𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐥𝐢𝐧𝐞𝐚𝐠𝐞 𝐛𝐥𝐢𝐧𝐝 𝐬𝐩𝐨𝐭𝐬: In a previous post, we discussed how unclear data lineage leaves organizations lost in the data maze. Robert Sahlin highlights how this confusion seeps into dashboarding, making it difficult to identify which reports are affected when a transformation breaks—often taking as long to investigate as to fix. Combined with unclear ownership of upstream data, this slows down troubleshooting & impacts operational efficiency. 🌀 𝐂𝐥𝐮𝐭𝐭𝐞𝐫𝐞𝐝 𝐚𝐧𝐝 𝐝𝐢𝐥𝐮𝐭𝐞𝐝 𝐦𝐞𝐬𝐬𝐚𝐠𝐢𝐧𝐠: Tristan Mobbs mentions in a post that overloading dashboards with multiple charts & details makes it hard for key insights to stand out. Additionally, overexplaining every data nuance weakens the impact. What’s wise is to provide actionable recommendations with confidence levels & risks to guide decision-making, while avoiding overwhelming the audience with too much information for better clarity in messaging. What other dashboard pitfalls have you seen or experienced? Let’s talk in the comments! 𝐒𝐢𝐠𝐧 𝐮𝐩 𝐟𝐨𝐫 𝐨𝐮𝐫 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dDT7FB3y

🌟 𝐃𝐚𝐭𝐚 𝐄𝐱𝐩𝐞𝐫𝐭 𝐨𝐟 𝐓𝐡𝐞 𝐖𝐞𝐞𝐤: Susan Walsh🌟 Meet Susan Walsh, also known as "The Classification Guru!" 🎓 With over 30 years of experience, Susan has worn many hats—founder, director, and all-around data guru. Her claim to fame? Mastering the art of cleaning and classifying messy data. 👩💼 She's an industry expert in spend data classification, supplier normalization, and taxonomy customization, with a track record that includes big names like Philips and Colgate-Palmolive. Simply put, if your data’s a mess, Susan’s the go to ‘guru’ to fix it! 🈺 In 2017, Susan took things up a notch by founding her own consultancy, "The Classification Guru Ltd," to help organizations tackle their messy data problems head-on. She even created 𝐂𝐎𝐀𝐓—a methodology focused on keeping data consistent, organized, accurate, and trustworthy—designed to prevent costly business mistakes caused by bad data. 👩💻 Susan's reputation as "The Classification Guru" goes far beyond her stellar work with data-driven organizations. When she's not busy whipping messy data into shape, she's actively sharing her knowledge with the data community. 🏅 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐠𝐥𝐢𝐦𝐩𝐬𝐞 𝐨𝐟 𝐒𝐮𝐬𝐚𝐧’𝐬 𝐢𝐦𝐩𝐚𝐜𝐭𝐟𝐮𝐥 𝐜𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐰𝐨𝐫𝐥𝐝: 📚 𝐑𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬 𝐇𝐮𝐛 - Susan is all about sharing her knowledge. Whether through insightful articles, her YouTube channel, guest spots on data podcasts, or sharp social media content, she spreads her passion for data far and wide. Her website is packed with resources to help data professionals stay on top of the latest trends in cleaning up messy data. Plus, she’s authored the must-read book "𝐁𝐞𝐭𝐰𝐞𝐞𝐧 𝐭𝐡𝐞 𝐒𝐩𝐫𝐞𝐚𝐝𝐬𝐡𝐞𝐞𝐭𝐬," a guide for anyone looking to master the art of fixing messy spreadsheet data. 🗣𝐂𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐒𝐩𝐞𝐚𝐤𝐞𝐫 - As a sought-after speaker, Susan regularly shares her expertise at major data and AI conferences like Big Data London. She dives into the current state of data cleaning and offers insights into the future trends shaping the data space. Her talks are always a hit, shedding light on how to tackle messy data and what’s next in the world of data management. To give you all a better access to her amazing work, we've compiled some of Susan's top resources including articles, podcasts, and talks! 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐦𝐨𝐫𝐞 𝐚𝐛𝐨𝐮𝐭 𝐡𝐞𝐫 𝐡𝐞𝐫𝐞: https://lnkd.in/ggRZWUzs Looking to connect with data superheroes? Explore our network of leading data rockstars and enthusiasts. 𝐉𝐨𝐢𝐧 𝐭𝐡𝐞 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲, 𝐟𝐢𝐧𝐝 𝐲𝐨𝐮𝐫 𝐭𝐫𝐢𝐛𝐞, 𝐚𝐧𝐝 𝐬𝐭𝐚𝐲 𝐚𝐡𝐞𝐚𝐝 𝐨𝐟 𝐭𝐡𝐞 𝐜𝐮𝐫𝐯𝐞: https://lnkd.in/dhBNHkQg Together, let's connect, learn, and grow!

🚀 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐨𝐧 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 This week's top data posts and conversations! 🔷 Vin Vashishta on: 𝐍𝐚𝐯𝐢𝐠𝐚𝐭𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐂𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 Vin Vashishta emphasizes the crucial role of contextual data in successful model training and business intelligence (BI). He argues that data cleaning is insufficient for model training without understanding the business problem and context. Vashishta advocates for leveraging pretrained models and domain-specific knowledge to achieve reliable AI outcomes. Read more: https://lnkd.in/dPi7pcEX 🔷 Julia Bardmesser on: 𝐀𝐬𝐬𝐞𝐬𝐬𝐢𝐧𝐠 𝐂𝐨𝐦𝐩𝐚𝐧𝐲 𝐅𝐢𝐭 Julia Bardmesser advises job seekers to gauge company fit by observing how customer experience (CX) is perceived during interviews. A data-driven approach to CX signals readiness for data-centric transformation, whereas a focus on superficial initiatives may indicate a lack thereof. Julia emphasizes that true data centricity extends beyond technology to encompass organizational culture, crucial for driving meaningful change. Read more: https://lnkd.in/dcrGFcvD 🔷 Jérémy Ravenel on : 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐋𝐚𝐲𝐞𝐫𝐬 𝐯𝐬. 𝐎𝐧𝐭𝐨𝐥𝐨𝐠𝐢𝐞𝐬: 𝐊𝐞𝐲 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞𝐬 𝐚𝐧𝐝 𝐒𝐲𝐧𝐞𝐫𝐠𝐲 Jeremy Ravelnel explains that a semantic layer simplifies data for business users, while an ontology structures and defines relationships within a domain. Though distinct, they can work together—semantic layers improve data usability, and ontologies enhance understanding through reasoning and inference. Read more: https://lnkd.in/d52jnhmS 🔷 Rebecka Storm on : 𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐟𝐨𝐫 𝐄𝐚𝐫𝐥𝐲-𝐒𝐭𝐚𝐠𝐞 𝐒𝐭𝐚𝐫𝐭𝐮𝐩𝐬 Rebeka Storm shares insights from a recent panel on leveraging data in early-stage startups. Emphasizing practicality, she advises against over-investing in complex data platforms initially. Instead, focus on tangible use cases, start small, and measure ROI directly. Read more: https://lnkd.in/dkP7EbJ8 🔷 Peter Baumann on : 𝐊𝐞𝐲 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠𝐬 𝐟𝐫𝐨𝐦 𝐁𝐀𝐑𝐂 𝐂𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐨𝐧 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 Peter Baumann discusses the importance of Data Products over Data Mesh, emphasizing their independent handling within companies. He highlights continuous exploration in Data Product development, the need for version management, the role of data marketplaces, and the value in moving beyond traditional reporting. Read more: https://lnkd.in/dWrHsezT 🔔 Follow 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 101 & stay updated with our weekly highlights of the most engaging discussions in the data community. Got a post or conversation that caught your eye? Share it in the comments! 💌 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐨𝐮𝐫 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dDT7FB3y

🚀 Data Governance: It's All About People, Communication, and Heroes! 🦸♀️🦸♂️ In the world of data, we often focus on policies and rules. But here's the truth: Effective Data Governance is a people business. 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐭𝐡𝐞 𝐬𝐞𝐜𝐫𝐞𝐭 𝐬𝐚𝐮𝐜𝐞 ➡️ Bridges gaps between data producers, processors, and consumers ➡️ Prevents bad decisions due to lack of context ➡️ Helps understand motivations and pain points 🌟 Key Roles in a Star Data Governance Team: ➡️ Data Governance Manager: The master connector ➡️ Data Quality Manager: The problem-solving enthusiast ➡️ Data Discovery Manager: The metadata maven ➡️ Data Owner: The accountable business leader ➡️ Data Steward: The domain expert ➡️ Data Custodian: The tech-savvy flow master ➡️ Data Product Owner: The value-driven collaborator ➡️ Platform & System Owner: The system guru *𝐂𝐀𝐕𝐄𝐀𝐓: Every business is unique in many ways, structure, resourcing, capability - so the above noted 'roles' may not be individual 'roles' 💡 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐘𝐨𝐮𝐫 𝐃𝐫𝐞𝐚𝐦 𝐓𝐞𝐚𝐦: ➡️ Initiation: Find your champion ➡️ Foundation: Create a culture of governance heroes ➡️ Autonomy: Empower through self-sufficiency Remember: It's not about boiling the ocean! Start with high-impact use cases that have: ✅ Clear business requirements ✅ Quantified business impact 🛠️ 𝐀𝐫𝐦 𝐲𝐨𝐮𝐫 𝐡𝐞𝐫𝐨𝐞𝐬 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐰𝐞𝐚𝐩𝐨𝐧𝐬: Self-service data stacks are the perfect enablers, shifting focus from gatekeeping to strategic guidance. What's your take on building data governance into your business? Special credit (again): Animesh Kumar (post) and Tiankai Feng (article within) #DataGovernance #DataPrivacy #DataOperatingModel #DataEnablement

Building 𝐠𝐨𝐥𝐝𝐞𝐧 𝐩𝐚𝐭𝐡𝐬 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 isn't the responsibility of domains or business teams. Once more for emphasis. Building golden paths for Data Products isn't the responsibility of domains or business teams. If it is, a rupture is impending 💥 Businesses with a good grasp of data realise that today, data delivers an unmistakable edge (=more wins). Many giants absorbed this fact long ago and have dedicated major projects and verticals to becoming Data-First. 𝐁𝐮𝐭 𝐰𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐃𝐚𝐭𝐚-𝐅𝐢𝐫𝐬𝐭 𝐦𝐞𝐚𝐧? As the name suggests, it is putting 𝐞𝐟𝐟𝐨𝐫𝐭𝐬 𝐛𝐞𝐡𝐢𝐧𝐝 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐮𝐬𝐚𝐠𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚 (e.g., applications, products) first while 𝐝𝐞𝐩𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐬𝐢𝐧𝐠 everything else (maintenance, plumbing, debt management, etc.). This is done either through: ✅ Abstractions at data consumption layers ✅ Delegation to the underlying platform ✅ Eliminating Overwhelm: Standardisation, Modularisation Replicating data-first organisations, the likes of Uber, Google, or Airbnb, which took years to develop their platforms customised to their own data stacks, is 𝐍𝐎𝐓 𝐭𝐡𝐞 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 since their stacks were and are catered to their specific internal architectures. 𝐈𝐧 𝐚𝐧𝐚𝐥𝐨𝐠𝐲 𝐭𝐨 𝐩𝐫𝐨𝐯𝐞𝐧 𝐈𝐃𝐏 𝐩𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 (Internal Developer Platforms), a DDP is designed to provide data professionals with 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐛𝐥𝐨𝐜𝐤𝐬 that they can use to build data products, services, and data apps quickly and efficiently. By providing a unified and standardised platform for managing data, a data developer platform helps better use data assets and drive business value. 🫂 𝐃𝐃𝐏 𝐟𝐨𝐫 𝐃𝐚𝐭𝐚 𝐨𝐫 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐬 DDP enables a seamless experience for data developers by 𝐚𝐛𝐬𝐭𝐫𝐚𝐜𝐭𝐢𝐧𝐠 𝐚𝐰𝐚𝐲 𝐫𝐞𝐩𝐞𝐭𝐢𝐭𝐢𝐯𝐞 𝐚𝐧𝐝 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐦𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞/𝐢𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 𝐨𝐯𝐞𝐫𝐡𝐞𝐚𝐝𝐬 while allowing familiar interfaces to programmatically speak to qualified data and focus on building applications that directly impact business decisions and ROI. 🫂 𝐃𝐃𝐏 𝐟𝐨𝐫 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫𝐬 𝐨𝐫 𝐈𝐓 𝐓𝐞𝐚𝐦𝐬 If the organisation builds an internal DDP instead of adopting one from the market, the platform team is in charge of building and maintaining it. With a DDP in place, IT teams significantly reduce costs and operational cycles. Platform and ops teams 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝𝐢𝐬𝐞 design, infrastructure, and SLOs and 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 repetitive tasks, such as spinning up resources or environments for developers. You can learn much more about Data Developer Platforms on the official site at datadeveloperplatform.org #datastrategy #dataplatform #datamanagement

𝐓𝐡𝐞 𝐒𝐤𝐢𝐥𝐥-𝐒𝐞𝐭 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫 𝐘𝐨𝐮𝐫 𝐃𝐚𝐭𝐚 𝐏𝐌 𝐑𝐨𝐥𝐞 | 𝐀 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐏𝐌'𝐬 𝐆𝐮𝐢𝐝𝐞 The role of Data Product Managers is gaining momentum as more organizations embrace the data products approach. With job postings rising and internal transitions happening, there's still plenty of curiosity about the specific responsibilities and skills required for the role. To clear up the key responsibilities and skills, Gaëlle SERET from Decathlon Digital shares her expert insights in the latest Modern Data 101 edition—covering everything from managing the data lifecycle to ensuring data quality aligns with business needs. Get ready to take notes as Gaëlle shares her insights! 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: 💠 𝐓𝐡𝐞 𝐄𝐯𝐨𝐥𝐯𝐢𝐧𝐠 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐚 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐝𝐮𝐜𝐭 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 Gaëlle illustrates how DPMs are pivotal in turning raw data into actionable insights. She says that their role is not just about managing data but delivering it with purpose, and ensuring it meets both present & future organizational needs. 💠 𝐓𝐮𝐫𝐧𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐢𝐧𝐭𝐨 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧 DPMs collaborate with multiple teams, including data engineers, analysts, & governance teams, to transform data into valuable knowledge. Gaëlle explains how this collaboration can facilitate to turn data into relevant ingredient for strategic decision-making. 💠 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 𝐓𝐡𝐚𝐭 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐃𝐏𝐌 𝐑𝐨𝐥𝐞 Gaëlle suggest that the core responsibilities of a DPM involve managing the data lifecycle, from understanding business processes to ensuring data quality & transparency. 💠 𝐒𝐤𝐢𝐥𝐥𝐬 𝐑𝐞𝐪𝐮𝐢𝐫𝐞𝐝 𝐭𝐨 𝐌𝐚𝐬𝐭𝐞𝐫 𝐭𝐡𝐞 𝐑𝐨𝐥𝐞 Gaëlle opines that the role demands a wide range of competencies, including cross-functional discovery, agile practices, & data management. DPMs must be able to navigate technical challenges, while maintaining a user-centric approach, focusing on business needs & outcomes. 💠 𝐀𝐝𝐯𝐚𝐧𝐜𝐢𝐧𝐠 𝐢𝐧 𝐚 𝐃𝐚𝐭𝐚-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐂𝐚𝐫𝐞𝐞𝐫 She outlines how DPMs can progress into leadership roles, such as Head of Data Product, where responsibilities expand to managing broader business & data strategies. This career path involves aligning data strategies with business goals, improving governance practices, & engaging operational teams. 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐞𝐧𝐭𝐢𝐫𝐞 𝐛𝐥𝐨𝐠 𝐡𝐞𝐫𝐞: https://lnkd.in/d2C4kBA2 📢 Do share what you feel about this take! 🗣️ 𝐒𝐡𝐨𝐮𝐭 𝐨𝐮𝐭: At MD101, we actively collaborate with data experts to bring the best resources to a blooming community of data practitioners. If you are willing to share an insightful perspective on anything about data, we’re all ears!! Note: All submissions are vetted for quality & relevance. 🔔 Follow 𝐌𝐨𝐝𝐞𝐫𝐧 𝐃𝐚𝐭𝐚 101 and stay updated with our weekly highlights from the modern data space.