Trends in Data Analytics Impacting Innovation

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?

🚀 Unlocking Public Value with Non-Traditional Data: New Use Cases, Emerging Trends 🤔From mobile phone records to social media posts, satellite imagery to grocery shopping data—Non-Traditional Data (NTD) is rapidly expanding how we understand and respond to today’s public challenges. 👉 In our latest curation, we spotlight how these often privately held, passively generated datasets are driving impact across domains like: 💳 Financial Inclusion 🏥 Public Health & Well-being 🏙️ Urban Mobility & Planning 📉 Economic & Labor Dynamics 🌐 Digital Behavior & Communication 🧭 Socioeconomic Inequality 📲 Data Systems & Governance 🔍 What’s new? We’re seeing more interdisciplinary research, hybrid use with traditional data, and stronger attention to ethics and impact. 👇A few standout examples: ➡️ In South Africa, grocery shopping data helped assess creditworthiness for 8M individuals without formal credit history. ➡️ In NYC, researchers used Google Street View + AI to challenge assumptions about urban health interventions. ➡️ In Chile, mobile phone data revealed stark inequalities in wildfire evacuation patterns. ➡️ A team in the US used Reddit and NLP to track how insomnia treatments are perceived over time. ➡️ Global wastewater surveillance via aircraft is proving a scalable early-warning system for pandemics. 📚 Check out the full set of curated cases and reflections here (with ✍️ Adam Zable) 👉 https://lnkd.in/eUDkqyQi #DataForGood #NonTraditionalData #PublicInterestTech #DataGovernance #DigitalInnovation #SocialLicense #DataStewardship #AIForPublicValue

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.

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

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

4 Game-Changing Trends in Data & AI for 2025 The next year will be a turning point for organizations leveraging AI. The difference between leaders and laggards will be defined by their ability to navigate these emerging realities: 1. GenAI will shift from broad applications to targeted impact. The days of “GenAI for everything” are numbered. The real value lies in contextual use cases—AI solutions designed to address specific business problems with precision. Broad, one-size-fits-all approaches may dazzle, but they rarely deliver sustained value. 2. The AI talent shortage will intensify. The market will continue to be flooded with resumes, but identifying individuals who can genuinely drive impact will be harder than ever. Organizations that succeed will prioritize strategic hiring frameworks that distinguish technical skill from real-world execution. 3. Organizational design will take center stage. Great data alone isn’t enough. Companies will likely begin to focus on restructuring workflows, eliminating silos, and fostering collaboration to unlock the full potential of AI initiatives. Alignment between teams will be as critical as alignment between datasets. 4. Businesses will invest in AI with greater precision. The exuberance surrounding AI isn’t fading, but it’s becoming more intentional. Leaders will evaluate initiatives based on their ability to generate measurable ROI. AI investments will shift from exploratory to outcome-driven. 💡 Organizations that embrace these trends will position themselves for sustainable growth. Those that don’t risk being left behind in an increasingly competitive landscape. Which of these trends resonates most with your current challenges? I’d love to hear your thoughts. #AI #DataAnalytics #Leadership #GenerativeAI #BusinessInnovation