Trends in Data Analytics for Enterprises

Data Integration Revolution: ETL, ELT, Reverse ETL, and the AI Paradigm Shift In recents years, we've witnessed a seismic shift in how we handle data integration. Let's break down this evolution and explore where AI is taking us: 1. ETL: The Reliable Workhorse      Extract, Transform, Load - the backbone of data integration for decades. Why it's still relevant: • Critical for complex transformations and data cleansing • Essential for compliance (GDPR, CCPA) - scrubbing sensitive data pre-warehouse • Often the go-to for legacy system integration 2. ELT: The Cloud-Era Innovator Extract, Load, Transform - born from the cloud revolution. Key advantages: • Preserves data granularity - transform only what you need, when you need it • Leverages cheap cloud storage and powerful cloud compute • Enables agile analytics - transform data on-the-fly for various use cases Personal experience: Migrating a financial services data pipeline from ETL to ELT cut processing time by 60% and opened up new analytics possibilities. 3. Reverse ETL: The Insights Activator The missing link in many data strategies. Why it's game-changing: • Operationalizes data insights - pushes warehouse data to front-line tools • Enables data democracy - right data, right place, right time • Closes the analytics loop - from raw data to actionable intelligence Use case: E-commerce company using Reverse ETL to sync customer segments from their data warehouse directly to their marketing platforms, supercharging personalization. 4. AI: The Force Multiplier AI isn't just enhancing these processes; it's redefining them: • Automated data discovery and mapping • Intelligent data quality management and anomaly detection • Self-optimizing data pipelines • Predictive maintenance and capacity planning Emerging trend: AI-driven data fabric architectures that dynamically integrate and manage data across complex environments. The Pragmatic Approach: In reality, most organizations need a mix of these approaches. The key is knowing when to use each: • ETL for sensitive data and complex transformations • ELT for large-scale, cloud-based analytics • Reverse ETL for activating insights in operational systems AI should be seen as an enabler across all these processes, not a replacement. Looking Ahead: The future of data integration lies in seamless, AI-driven orchestration of these techniques, creating a unified data fabric that adapts to business needs in real-time. How are you balancing these approaches in your data stack? What challenges are you facing in adopting AI-driven data integration?

I’ve put together this visual map of the Data and AI Engineering tech stack for 2025. It’s not just a collection of logos — it’s a window into how quickly this space is evolving!!!! Here’s why we felt this was important to create: - Data and AI Are Converging -- Once, data engineering and AI engineering were separate disciplines. Now, they’re overlapping more than ever. Teams are using the same tools to build pipelines, train models, and deliver analytics products. - Modern Orchestration and Observability -- Today, orchestration isn’t just about scheduling jobs. It’s about managing complex dependencies, data quality, lineage, and integrating with modern compute environments. Observability has become essential for trust, compliance, and reliability. - A Surge in MLOps and Practitioner Tools -- The ecosystem of tools supporting machine learning practitioners has exploded. It’s not just model training anymore — it’s about reproducibility, monitoring, fairness, and deploying models safely into production. The rise of vector databases and new analytics engines reflects how AI workloads are changing infrastructure demands. - Metadata and Governance Take Center Stage -- As data volumes grow, the need to manage metadata, ensure governance, and maintain data quality has become a top priority. The number of solutions focused on catalogs, lineage, and privacy is rapidly expanding. - Architectures Are Evolving for New Workloads -- Generative AI, real-time analytics, and low-latency applications are putting pressure on traditional batch-oriented systems. We’re seeing significant shifts in compute engines, storage formats, and streaming technologies to keep pace. The takeaway is simple: this ecosystem is in constant motion. New categories emerge. Existing ones blur. Enterprises and practitioners alike have more choices than ever before. We created this visual to help make sense of it all — and to spark discussion. I’m curious: - Which parts of this stack do you see transforming the fastest? - Are there any categories where innovation feels especially urgent or overdue? - Which tools have changed how you work over the past year? Let’s discuss where this fast-moving world is headed next.

This year, the State of Data and AI Engineering report has been marked by consolidation, innovation and strategic shifts across the data infrastructure landscape. I identified 5 key trends that are defining a data engineering ecosystem that is increasingly AI-driven, performance-focused and strategically realigned. Here's a sneak peek at what the report covers: - The Diminishing MLOps Landscape: As the standalone MLOps space is rapidly consolidating, capabilities are being absorbed into broader platforms, signaling a shift toward unified, end-to-end AI systems. - LLM Accuracy, Monitoring & Performance is Blooming: Following 2024's shift toward LLM accuracy monitoring, ensuring the reliability of generative AI models has moved from "nice-to-have" to business-critical. - AWS Glue and Catalog Vendor Lock-in: While Snowflake just announced read/write support for federated Iceberg REST catalogs, finally loosening its catalog grip, AWS Glue is already offering full read/write federation, and is therefore the neutral catalog of choice for teams avoiding vendor lock-in. - Storage Providers Are Prioritizing Performance: in line with the growing demand for low-latency storage, we see a broader trend in which cloud providers are racing to meet the storage needs of AI and real-time analytics workloads. - BigQuery's Ascent in the Data Warehouse Wars: with 5x the number of customers of both Snowflake and Databricks combined, BigQuery is solidifying its role as a cornerstone of Google Cloud’s data and AI stack. These trends highlight how data engineering is evolving at an unprecedented pace to meet the demands of a rapidly changing technological landscape. Want to dive deeper into these critical insights and understand their implications for your data strategy? Read the full report here: https://lnkd.in/dPCYrgg6 #DataEngineering #AI #DataStrategy #TechTrends #DataInfrastructure #GenerativeAI #DataQuality #MLOps

The future of analytics is a metrics-first operating system. Let’s discuss three macro trends driving this inevitable evolution. Three Macro Trends: 1) Sophisticated and Standardized Data Modeling Data modeling is now widely accepted and implemented by data teams of all sizes. These models are increasingly capturing the nuances of varied business models. - From the early days of Kimball to today, powered by advanced data modeling and management tools, practitioners are coalescing around concepts like time grains, entities, dimensions, attributes and metrics modeled on top of a data platform. - Compared to even 7-8 years ago, we’ve made significant strides in tailoring these concepts for various business types—consumer, enterprise, and marketplace—across different usage and monetization models. - We’re now proficient in standardizing metrics and calculations for specific domains, such as sales funnels, lifetime value calculations for marketing, cohort tracking for finance, and usage and retention models for product teams. The architecture of data production is more robust than ever as data and analytics engineers refine their practices. Now, let’s look at the consumption side. 2) Repeatable Analytics Workflows Analytics workflows are becoming repeatable, and are centered around metrics: - Periodic business reviews and board meetings demand consistent metrics root-cause analysis, including variance analysis against budgets or plans. - Business initiatives, launches, and experiments require expedient analysis to extract actionable insights and drive further iterations. Experimentation is becoming a core workflow within organizations. - Organizations need to align on strategy, formulate hypotheses, and set metric targets to monitor progress effectively. 3) Limitations of Scaling Data Teams The cold reality is that data teams are never going to be big enough. This has become even more apparent as investment levels have waned over the past three years. Combining these insights: 1) The increasing standardization of data models across business models 2) The secularization and rise of repeatable workflows centered around metrics. 3) The need to maximize data team leverage It is clear that a metrics-first, low to no code operating system is the future. Such a system will provide immense leverage for data teams, while empowering executives and operators. This shift towards a metrics-first operating system represents the next evolution in analytics, driving both operational efficiency and strategic agility.

Data and analytics leaders, are you looking to keep up with the latest technology trends with D&A implications? Check out this new quarterly guidance led by Ramke Ramakrishnan and Akash Krishnan, Ph.D. that informs you on current adoption trends based on Gartner surveys and guides you to assess and prioritize technologies in 4 categories: *Adopt: Technologies are currently critical and demand a focus for up to one year. *Act: Technologies are gaining momentum and are expected to expand quickly within two to four years. *Prepare: Technologies are advancing rapidly and are anticipated to evolve in three to five years. *Aware: Early-stage technologies with slower adoption, potentially becoming mainstream in seven to 10 years. This edition focuses on: Adopt: AI trust, risk and security management (AI TRiSM) ensures the governance, trustworthiness, fairness, reliability, robustness, efficacy, security and data protection of AI models and applications. Act: Domain-specialized language models (DSLMs) are specialized, fit-for-purpose models that offer highly contextual and cost-effective GenAI solutions. They are characterized by a relatively limited number of parameters. Prepare: Agentic AI is an approach to building AI solutions based on the use of software entities that classify completely, or at least partially, as AI agents. These are autonomous or semiautonomous software entities that use AI techniques to perceive, make decisions, take actions and achieve goals in their digital or physical environments. Aware: Intelligent simulations provide accurate modeling and what-if scenarios of physical and digital process systems at unprecedented scale and accuracy, and at lower cost. To do so, they use digital technologies such as AI, digital twins, quantum computing and spatial computing. To access (subscription required): https://lnkd.in/eW59AsZX Not yet a client? Here are some great insights on data, analytics and AI https://lnkd.in/ek6RbnGM #GartnerDA #D&ATrends Juergen Weiss Sumayya Ulukan Christina Hertzler Lydia Ferguson Frank Buytendijk Carlie Idoine Mark O'Neill Alan D. Duncan Afraz Jaffri Ehtisham Zaidi Sally Parker Sumit Agarwal Lydia Ferguson David Pidsley Deepak Seth Avivah Litan

The global data and analytics market is positioned for unprecedented growth, projected to reach $17.7 trillion, with an additional $2.6 to $4.4 trillion driven by generative AI applications. However, this opportunity comes with significant hurdles. As 75% of companies race to integrate generative AI, many are accumulating technical debt, data clean-ups and grappling with regulatory compliance challenges across the globe. According to McKinsey, 2025 will see a surge in investments toward advanced data protection technologies, including encryption, secure multi-party computation, and privacy-preserving machine learning. Meanwhile, IDC forecasts that by 2025, nearly 30% of the workforce will regularly leverage self-service analytics tools, fostering a more data-literate corporate environment. Not long ago, “data democratization” dominated industry conversations. In the last few years, the focus was on making data universally accessible. But raw data alone doesn’t provide meaningful insights , drive decisions, or create competitive advantage. The real transformation lies in insight democratization—a shift from simply providing access to data to delivering actionable intelligence where and when it matters most. That is where most of the data & analytics leaders are now focusing. The future of transformative or strategic inititaitves, business & finance operations, and revenue growth will not be defined by dashboards and static reports. Instead, success will hinge on the ability to extract, contextualize, and act on insights in real time. Organizations that embrace this shift will lead the next era of data-driven decision-making, where knowledge is not just available, but empowers action. #datainsights, #datacleanroom, #predictiveanalytics

𝐇𝐚𝐫𝐧𝐞𝐬𝐬𝐢𝐧𝐠 𝐭𝐡𝐞 𝐏𝐨𝐰𝐞𝐫 𝐨𝐟 𝐀𝐈 & 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐭𝐨 𝐃𝐫𝐢𝐯𝐞 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧-𝐌𝐚𝐤𝐢𝐧𝐠 In today’s rapidly evolving business environment, leveraging AI and data analytics has become critical to drive strategic decision-making. But true value comes not just from implementing these technologies but from how effectively they are integrated into business processes and culture. Here’s a deeper dive into maximizing their impact: 𝟏. 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 𝐟𝐨𝐫 𝐅𝐮𝐭𝐮𝐫𝐞-𝐑𝐞𝐚𝐝𝐲 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: AI-powered predictive models go beyond historical analysis to forecast future trends, risks, and opportunities. Companies leveraging predictive analytics can anticipate shifts in market demands, customer behavior, and emerging industry patterns. For example, by analyzing millions of data points, AI algorithms can predict product demand, reducing inventory costs and minimizing waste. 𝟐. 𝐏𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐇𝐲𝐩𝐞𝐫-𝐒𝐞𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: AI-driven analytics enable organizations to segment their customer base with pinpoint accuracy and deliver hyper-personalized experiences. Consumer goods companies, for instance, have used AI to create tailored marketing campaigns and product offerings, resulting in a 20-30% increase in customer retention rates. This capability turns data into a competitive advantage by fostering deep customer loyalty. 𝟑. 𝐃𝐚𝐭𝐚-𝐁𝐚𝐜𝐤𝐞𝐝 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞: Operational inefficiencies often drain resources and hinder growth. AI systems analyze complex datasets to uncover inefficiencies in supply chains, manufacturing processes, and service delivery. For example, machine learning models can identify patterns of equipment failure before they occur, enabling predictive maintenance that reduces downtime by up to 50%. This optimization ultimately leads to increased productivity and lower costs. 𝟒. 𝐀 𝐃𝐚𝐭𝐚-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 Data-driven decision-making extends beyond technology; it demands a cultural shift. Companies must foster a mindset where data insights are valued and applied at every organizational level. This requires training teams, promoting data literacy, and breaking down silos. When data informs every decision, from boardroom strategy to daily operations, organizations are equipped to innovate faster and adapt to change. To drive meaningful outcomes with AI and analytics, leaders must focus not just on adoption but on embedding these tools into the organization's DNA. The real power lies in cultivating an environment where data-driven insights guide every move. 💡 How is your organization embedding AI and data-driven practices into its strategy? #DataDrivenLeadership #AIandAnalytics #StrategicPartnerships #DigitalInnovation #BusinessTransformation #TechLeadership #OperationalExcellence #ConsumerGoodsInnovation

Our expectations of AI are about to go up a level. The next wave of AI workplace products will enter the market, and they’re going to make use cases that wowed us a year ago look…kinda old. The thing that’s notable about the next wave of applications is that they’re taking the power of an LLM and connecting it with live enterprise data. Together, this connection creates the conditions for new use cases that will transform the way we approach so many day-to-day tasks. As an example, think of the task of writing a sales email. Over the last year or so, you might have used an LLM to help you create a first draft of a standard sales email. Helpful? Yes. But it’s just the beginning. Now imagine writing a sales email with an AI application that has access to live data in your CRM, email tool, and other apps. It knows the company, role, industry, and country of the person you want to email. It knows what has worked well in prior emails to similar people. It even knows what competitor product they may be using. And it generates an email draft that’s tailored to that specific person using all of that information. That’s just one example of course. Imagine daily workflows across every role at a company, all potentially benefiting from applications that combine LLMs with enterprise data. An interesting second-order effect of this shift is that it’ll lead to many companies having a reckoning with the quality and cleanliness of their data. Because as the potential of AI in the workplace increases, the importance of having rich, accurate, organized data will increase too. We’re approaching the “end of the beginning” of the chapter that began with the launch of ChatGPT in November 2022. And things are about to get really interesting! What AI workplace use cases do you think will improve the most in 2024? #ai #data #futureofwork

Proud to have published our latest projection of the major shifts we expect in Data and AI over the next 5 years entitled 'Charting a path to the data- and AI-driven enterprise of 2030'. I authored this with my colleagues Dr. Asin Tavakoli, Holger Harreis and Michael Bogobowicz. The shifts we see include: 1) Everything, everywhere, all at once: Data and AI will become pervasively adopted to solve business problems large and small, GenAI will be embedded in a vast range of apps and systems, often beyond our awareness 2) Unlocking ‘alpha’: With such a mass adoption of vendor provided AI and GenAI, hereby normalizing many capabilities, firms will need to consider where they can create competitive edge in the digital world 3) Capability pathways - From reacting to scaling: Firms will become more disciplined about rolling out capabilities, rather than scattered modules of architecture, data and talent, they will prioritize capability pathways that create a flywheel of impact off of a common stack of capabilities 4) Living in an unstructured world: Firms have barely dealt with cleansing and curating their structured data for impact, arguably this is ~10% of data that firms have at their disposal, the next few years will see firms tackling the mountain of messy unstructured data to feed their GenAI models 5) Data leadership - It takes a village: Firms will need to figure out how to overcome historic challenges of combining the disciplines of sector specific value creation, engineering and governance at all levels of the organization in order to safely drive scalable impact from Data and AI 6) The new talent life cycle: The war for talent will enter a new phase, with new skills being required, baby boomers retiring, global politics changing talent flows and firms writing bigger and bigger checks 7) Guardians of digital trust: With new and even greater digital risks emerging, the arms race will heat up, regulators will lean in and ethics will be at the forefront of many decisions. In this context, competitive edge will be created by firms that can change speed bumps to jump ramps. Enjoy the read! #data #genAI #generativeAI #digitaltrust #quantumblack #mckinsey #mckinseytechnology https://lnkd.in/eyHMt4BK

Back to my roots :) A fast growing, cool data startup Coalesce.io asked me for my predictions for the data space in 2025. Here are my top 4: 1.) AI DECISIONING - We’ll see data, marketing, and digital product teams adopting AI Decisioning platforms on top of their data warehouses to drive 1:1 personalization with their customers across all channels. Instead of building manual rules, audiences, and journeys, AI will look at each customer and decide the best actions to drive your company’s goals, and continuously learn and get smarter. 2.) DATA ACTIVATION - Data warehouses are going to continue to grow as the center of data gravity and more business teams will want to get data out of these warehouses into the tools they use every day. 3.) WAREHOUSE 3.0 - Open format tables like Iceberg will continue to grow in adoption by companies and the warehouses. Separately, batch and streaming workflows are going to continue to converge as data warehouses support low-latency use-cases. 4.) DATA PRODUCTS - We’re entering the era of data products, where companies don’t just build one off reports or analyses but really think about what artifacts (eg “marketing user data” or “customer service ticket insights”) they should be exposing to other data teams and the business for ongoing consumption. You should check out the full report here: https://lnkd.in/ew9C2Yxs I’d love your reactions— do these trends seem right for the next year? What else am I missing?