Profisee’s Post

🚀 Exciting insights from Microsoft General Manager of Enterprise Data Karthik Ravindran from this year's Data Hero Summit! 🌐    In his 24 years at Microsoft, Karthik has led transformative data projects.    Key takeaways:  📌 Cultural Transformation: Data journeys are more than tech — they're cultural and practice transformations powered by technology. Balance is crucial for success.  📌Champion of Enablement: Shift from a traditional Center of Excellence (CoE) to a champion of enablement. Data offices should activate a data culture across the organization, responsibly democratizing data.  📌Activate Data as Oxygen: Treat data like free-flowing oxygen, democratize it responsibly and enhance organizational skills through data literacy programs and maturity models.    Karthik's journey emphasizes the importance of aligning technical endeavors with cultural shifts for true business value. 💡📊    What's your biggest challenge or success story in navigating the cultural transformation of data within your organization? Sign up now to watch the full on-demand video at 👉 https://ow.ly/JElv50QiVMy   #DataTransformation #DataHeroSummit #DigitalTransformation

Yesterday, I had the opportunity to present on "Navigating Data Transformation Journeys: Lessons Learned from Microsoft" to a 100+ audience. The session was packed with valuable insights and strategies for organizations looking to embark on a #data transformation journey. During the presentation, I shared 12 important lessons that Microsoft learned while navigating their own data transformation journey. What is the most important lesson? A data journey is mostly about #dataculture, people, and organizational change. It's not just about #technology, but about creating a data-driven culture that empowers employees to generate value. For seeing the presentation, click the link below.

Once again Piethein Strengholt offers us a powerful insight on how to build a data strategy that avoids the thousands of failures and pitfalls of those who already went through the journey. Great work! #data #datastrategy #datavision

Empower your organization with data democratization! 🚀 Check out this insightful HBR article outlining the "5 Pillars for Democratizing Data." Learn key strategies to make data accessible and actionable across your teams. Drive innovation and informed decision-making by fostering a culture of data accessibility. Creating a data democracy requires teamwork and support from leaders. Though the five pillars don't have to be developed in a specific order, following them guides the way. Intentionally addressing all of them can help managers smoothly incorporate data across the organization. Azure OpenAI can help your organization by offering a comprehensive ecosystem that aligns with the principles of creating a data democracy, promoting accessibility, collaboration, integration, intelligent decision-making, and security across the organization. https://lnkd.in/g8C2ZUhC #azureopenai #microsoftpowerbi #DataDemocratization #Innovation #DataAccess #DecisionMaking

This is an excellent framework for maximizing productivity and delivery.

Why Intelligent Data is essential? "Why" is one of the most important questions that we shall be, frequently, asking ourselves. It is not only an invitation to deep conversations about the challenges we face today, but also an open door for offering solutions and providing a realtime change. As Paladins, we invite you to join us in exploring cutting-edge strategies for data utilization, tailored specifically for forward-thinking executives and data professionals. If you are interested in discussing about why intelligent data is essential and, how it could be applied to your organization, let's have a talk.  You may choose to comment on this post, send me a LinkedIn message or drop an e-mail to info@paladin.com.tr.

💭Learn why Microsoft chose the progression over perfection culture to get its data in order. Karthik Ravindran, General Manager, Enterprise Data at Microsoft, speaks with Robert Lutton, VP at Sandhill Consultants Ltd, and Editorial Board Vice Chair at CDO Magazine, in a video interview about practicing a culture of progression over perfection, the key considerations and challenges in navigating technology choices or data governance; and implementing data governance for AI and AI for data governance, combined with humans in the loop for value creation. Ravindran elaborates on practicing a culture of progression over perfection and how it impacts the culture and governance practices at Microsoft. He explains it from two perspectives: ➡️ Applying progression over perfection and navigating organizational change management ➡️ Progression over perfection in the context of applied data value 👉 WATCH the interview here: https://hubs.ly/Q02y9QV60 #Leadership #DataManagement #ThoughtLeadership #DigitalTransformation You can also find this episode on our podcast page! 🎧 LISTEN here - https://hubs.ly/Q02y9XSM0 Did you find this post relevant? Like it, share your views with the interviewee, and tag others who can benefit from it in the comments.👍🗨️

Sharing here the 3rd and final episode of the podcast (and accompanying write up) that Robert Lutton and I had the opportunity to do on navigating data transformation journeys. In this installment, we discuss the notion of adopting and practicing a "Progression over Perfection" approach to navigating the multi-dimensional change management facets of data journeys. Hope you find it useful!

📊 Exciting Paradigm Shift in Data: Introducing Data Mesh! 🌐 Hey #LinkedInNetwork! 👋🏼 As we navigate the ever-evolving world of data, I'm thrilled to share a game-changing concept - Data Mesh! 🚀🔍 🔹 What's Data Mesh? Traditionally, organizations have managed data in a centralized manner, often leading to bottlenecks, scalability challenges, and siloed knowledge. Enter Data Mesh - a decentralized approach that treats data as a product and empowers teams to take ownership of their data domains. 🎯🌐 🔹 Key Principles: Domain-Oriented Ownership: Data domains are owned by cross-functional teams, fostering accountability and domain expertise. Data as a Product: Treating data like a product allows for clear expectations, APIs, and self-serve consumption. Self-Serve Data Infrastructure: Empowering teams to manage their data lifecycle independently, reducing dependencies. Federated Computational Governance: Decentralized governance ensures data quality, security, and compliance. Product Thinking: Applying product management principles to data, enhancing discoverability and usability. 🔹 Why Data Mesh Matters: ✨ Scalability: Seamlessly scale as data volumes grow without overwhelming central teams. ✨ Agility: Accelerate insights by enabling teams to iterate and innovate within their data domains. ✨ Collaboration: Break down silos and foster cross-team collaboration, unlocking new possibilities. 🔹 Implications for the Future: Data Mesh isn't just a tech trend; it's a mindset shift that aligns with our rapidly changing data landscape. By embracing Data Mesh, organizations can future-proof their data strategies, enabling us to harness the true potential of data-driven decisions. 🌍💡 Excited to embark on this journey of reshaping how we think about and leverage data! Let's connect and dive deeper into this revolutionary approach. Share your thoughts and let's explore the future of data together. 💬🤝 #DataMesh #DataStrategy #DigitalTransformation #DataInnovation #FutureOfData #DecentralizedData Looking forward to hearing your insights, comments, and experiences! 👇🏼👇🏼

🔍📊 Navigating the Data Ops and Observability Landscape: Insights for the Modern Data Ecosystem 🌐🔍 The synergy between Data Ops and Observability is shaping the way organizations extract value from their data. Let's delve into this dynamic duo and explore how it's evolving the way we manage and glean insights from data. 💡🛠️ Data Ops: Bridging the Gap Between Data and Operations: Data Ops is a paradigm that bridges the gap between data engineering and operations. It's about ensuring data availability, quality, and agility throughout its lifecycle. By integrating DevOps practices into data management, organizations achieve faster data delivery, enhanced collaboration, and reduced friction between teams. 🚀📊 Observability: Peering Beyond Data's Surface: Observability, on the other hand, is like equipping data with X-ray vision. It's about going beyond traditional monitoring to gain deep insights into data's behavior and performance. By collecting, visualizing, and analyzing data about data itself, organizations gain a holistic understanding of their data pipelines. 🌐🔍 The Art of Unveiling Data Behaviors: Imagine you're sailing a ship in uncharted waters. Without instruments to gauge the winds and currents, it's a risky journey. Similarly, data pipelines navigate through complex landscapes, and observability acts as the compass. It detects bottlenecks, anomalies, and potential failures, enabling swift action before issues escalate. ⚙️🌊 The Data Ops-Observability Symbiosis: Data Ops and Observability share a symbiotic relationship. Data Ops ensures data flows smoothly, while Observability ensures visibility into its journey. This harmonious synergy results in a proactive approach where potential disruptions are spotted early, data quality is maintained, and operations are optimized. 🤝🔗 Empowering the Data Ecosystem: In a world driven by data-driven decisions, the combination of Data Ops and Observability empowers organizations to unleash the full potential of their data. It fosters collaboration, accelerates innovation, and ensures data integrity across the board. With a strong foundation, businesses can confidently steer their data-driven endeavors. 🚀📈 #DataOps #Observability #DataEcosystem #DataDriven #Innovation #Collaboration #ModernDataManagement