Eckerson Group

Notice anything missing? In our view, an Enterprise Data & Analytics Team should include data architecture, engineering, BI, analytics, platforms, and program services. But governance, MDM, and security? They aren’t part of this team. Instead, these functions typically reside within business domains rather than being centralized within the enterprise data team. The goal is to build a federated operating model, not a centralized one. ✔️ Federated Model – Business domains manage their own data functions, ensuring alignment with domain-specific needs while adhering to enterprise-wide standards. ❌ Centralized Model – A single enterprise team controls all data functions, often leading to bottlenecks, slower decision-making, and disconnected business teams. If your business sponsor questions this approach, remind them that in an optimal model, many enterprise developers work within business domains—not under a chief data officer (CDO). Understand how to create a scalable, business-aligned data & analytics team by downloading Wayne Eckerson’s eBook: Operating Models for Data & Analytics: https://lnkd.in/ecYpb57t #operatingmodel #enterprisedatateam #federated #coreteam

Organizations often approach software selection as a feature comparison exercise. But the most common regrets after purchasing a tool aren’t about missing features—they’re about hidden costs, poor adoption, and a misalignment with business needs. A structured evaluation process ensures that decision-makers: ▪️Define and prioritize selection criteria that truly matter ▪️Identify total cost of ownership beyond the sticker price ▪️Validate vendor claims through real-world testing ▪️Gain stakeholder buy-in to ensure adoption and long-term success Our latest guide outlines a proven methodology for selecting data & analytics tools—one that goes beyond marketing claims and ensures the right fit for your enterprise. Read the full guide and get access to evaluation templates: https://lnkd.in/eNhUmZZu #DataAnalytics #ProductSelection #EnterpriseSoftware #VendorEvaluation #TechStrategy

Your data strategy matters more than ever—especially in the age of AI. Wayne Eckerson’s tutorial, “Constructing Your Data Strategy: A Business and Technical Foundation for Success,” outlines the key steps (assessment, strategy, design, implementation, and value measurement) organizations need to create a robust data strategy. As you get these steps right, you are laying the foundation for AI success. Clear roadmaps, robust governance, quality data models, and well-integrated pipelines—these elements ensure your organization is ready to meet AI’s demands for high-quality, well-governed data. Download Wayne’s full presentation to review or refresh your data strategy: https://lnkd.in/ew8FeTjn Need help with your data strategy? Our consultants can help: https://lnkd.in/e2Q-fRQG #datastrategy #AIready #EnterpriseAI

Modern businesses move fast, and batch processing can’t keep up. Today’s applications need fresh, reliable data—and streaming pipelines make that possible. Take logistics: A company needed better shipment tracking amid unpredictable weather. They built a chatbot-enabled routing system to help customers adjust plans in real time. But without streaming data, it would rely on outdated information. By feeding live shipment records, weather updates, and sensor data into the system, streaming pipelines kept it informed—helping deliveries stay on track. In his latest blog, Kevin Petrie explores why streaming pipelines are critical for GenAI success: https://lnkd.in/eYpbVJss 📊 See the diagram below to understand how streaming pipelines enable RAG and GenAI workflows. Sponsored by Striim #realtime #RAG #streamingarchitecture #GenAI

60% of organizations regret a software purchase within the first year. The top reasons? Unexpected costs, unclear pricing models, and misalignment with enterprise needs. That’s why a structured evaluation process is critical. Beyond comparing features, teams must assess pricing models, support costs, scalability, and vendor reliability.  - Explore our Product Evaluation Topic Page, which is a curated collection of articles, templates, and deep dive reports to help you navigate vendor selection across different technology categories. Start here: https://lnkd.in/e2PsXYf5 Source: Gartner Digital Market’s 2024 tech trends report, Gartner. #buyersremorse #evaluation #dataanalytics

Large Language Models (LLMs) don’t manage themselves. That’s where LLMOps comes in—a discipline that ensures fine-tuning, deployment, monitoring, and optimization of LLMs at scale. But who makes LLMOps possible? 👉 Data engineers. To support LLMOps, data engineers must: 🔹Refine vectors – Transform and enrich data to improve LM fine-tuning. 🔹Optimize pipelines – Configure infrastructure to meet latency, throughput, and reliability needs. 🔹Enable adaptability – Build modular pipelines that evolve as data, LMs, and GenAI applications change. The role of data engineers in AI is evolving fast. As LLMOps matures, they will be the key to scaling, maintaining, and improving GenAI applications. Read more here: https://lnkd.in/ekbKu3nb 

Even the most seasoned data leaders feel the pressure when choosing the right enterprise product. With countless options in each category, how do you make the right call? The key: a structured evaluation framework that aligns business needs with technical feasibility. In our upcoming webinar, we’ll break down: - How to define and prioritize evaluation criteria - A step-by-step vendor selection approach - Common pitfalls to avoid - Ready-to-use templates to simplify the process 📢 Join industry experts Wayne Eckerson, Jay Piscioneri, and Sean Hewitt in this afternoon’s live conversation.: https://lnkd.in/e9efQriC #tips #productevaluation #dataanalytics #seaofoptions

A team coach adjusts strategies based on the matchup—no single game plan works for every situation. Similarly, AI/ML projects need different data integration strategies to meet diverse use cases. In his timely article, Kevin Petrie explores three styles of data integration to train and feed AI/ML models in complex environments: → ELT + CDC – Keeps AI models current by capturing incremental updates instead of relying on bulk data loads. → ELT + Data Virtualization – Creates a seamless view of distributed data without requiring heavy data movement. → Streaming ETL – Feeds AI models with continuous, real-time data for instant decision-making. AI/ML success depends on the right data, delivered the right way, and at the right time: Read the breakdown on multi-style data integration for AI/ML: https://lnkd.in/e8ihwdtK #dataintegration #gameplan #AI

AI and ML teams want the flexibility to experiment. Risk and compliance teams demand governance to protect the business. And data architects? They have to make it all work. The key lies in a data architecture that balances both. 🔹Access & Governance: Prevents bias, protects privacy, and ensures compliance 🔹Integration: Keeps data flowing smoothly for real-time insights 🔹Infrastructure: Scales AI workloads without chaos It’s a tightrope walk—but the right architecture makes all the difference. 📊 Read Kevin Petrie’s blog on how to design a flexible yet governed data environment for AI/ML success: https://lnkd.in/e-c_mxAY #balance #dataarchitecture #guardrails

An Open-Book Test for GenAI? Retrieval-Augmented Generation (RAG) enhances GenAI accuracy by retrieving domain-specific inputs—much like referencing a textbook during an open-book exam. A RAG architecture typically includes the following: 🔹 Source Data – Documents, emails, images, logs, and database tables serve as inputs. 🔹 Data Pipelines – Transform data into AI-ready formats, such as vector embeddings or graph structures. 🔹 Databases – Vector, graph, and relational DBs organize and retrieve relevant data when queried. 🔹 Applications/Agents – Inject retrieved data into GenAI prompts, improving accuracy and explainability. However, implementing RAG workflows isn’t simply a plug-and-play decision—it demands careful design and orchestration. As guest blogger Kevin Petrie explains, organizations must decide whether to build a custom solution or buy a commercial product. The right choice depends on complexity, cost, and long-term needs. 📖 Explore the trade-offs in his latest article for BARC: https://lnkd.in/ecQyjfrN #RAG #buildorbuy #GenAI