The Future of Innovation in a Data-Driven World

A quiet shift is happening in the world of data. As AI becomes more embedded in real products, data is stepping into the spotlight. For years, data teams have lived under G&A or “cost center” budgets. But that’s starting to shift. But now I’m hearing things like: “We’re funding our data platform like product R&D.” “Data isn’t just analytics anymore - it’s our AI foundation.” We’re now seeing: → Data infrastructure classified as CapEx, not just OpEx → Data initiatives moving into R&D and product orgs This isn’t just a re-org. It’s a revaluation. Data is becoming an innovation asset. My prediction? In a few years, the best companies will treat data like software: A core R&D investment - not an internal service. If you’re a data leader today, your org chart - and your budget - might look very different in 12 months. What shifts are you seeing in your organization?

Last year, I watched a client's CFO stare at a screen filled with perfectly accurate data—revenue trends, customer metrics, operational KPIs—yet struggle to make a critical decision. The data was pristine, the analytics sophisticated, but wisdom remained elusive. That moment crystallized a truth I've carried through fifteen years of building Devsinc: we've mastered collecting data, but we're still learning to cultivate wisdom. The numbers are staggering. The BI software market is growing at 10% annually, reaching unprecedented scale, while 94% of data and AI leaders report that GenAI is driving greater focus on data quality and governance. Yet for all this investment, 49% of small businesses only increased their analytics usage post-COVID—a reactive, not proactive approach. The evolutionary path is clear: we've moved from data scarcity to data abundance, from business intelligence to augmented analytics. By 2025, AI-powered augmented analytics will drive 40% of new BI deployments, but the real transformation isn't technological—it's philosophical. I've seen teams drowning in dashboards while thirsting for insights. The missing link isn't more sophisticated algorithms; it's the human capacity to transform information into understanding, understanding into judgment, and judgment into wisdom. To the brilliant graduates entering our field: your generation inherits tools that can process petabytes in milliseconds, but your true value lies in asking the right questions. Self-service analytics is democratizing data exploration, but democratization without education breeds confusion. To my fellow CTOs and CIOs: we must architect systems that don't just serve data, but nurture decision-making capabilities. The future belongs to organizations that can traverse the entire spectrum—from raw data to actionable wisdom. At Devsinc, we've learned that the ultimate competitive advantage isn't having the most data or the fastest analytics. It's building the organizational wisdom to know when to trust the data, when to question it, and when to look beyond it entirely.

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.

Most businesses today are running on Simple Data Analytics (SDA). -Summing -Averaging -Multiplying -Basic reports It’s enough to track what’s happening. But is it enough to stay competitive? Maybe not. Because while SDA gives you a snapshot of the past, it doesn’t prepare you for the future. Enter Intelligent Data Analytics (IDA). IDA goes beyond basic number crunching. It transforms, standardizes, and enriches data with AI before analysis. That means: ✔ Extracting meaning from unstructured sources (like social media, emails, or customer reviews). ✔ Identifying hidden patterns using natural language processing and machine learning. ✔ Automating complex data processing to surface real insights. Why does this matter? Let’s say your company sees a 10% drop in customer retention. SDA tells you the retention rate is down. But why? With IDA, you can analyze customer call center transcripts, recent product reviews, customer satisfaction surveys, and buying behavior to tell you: → Are customers leaving due to price sensitivity? → Is a competitor offering better service? → Are product reviews highlighting recurring issues? SDA can tell you what happened, but IDA can tell you what actually transpired and provide insights into what to do next. Businesses that stop at simple data analytics are leaving valuable insights on the table. In our AI-driven world, data isn’t just about reporting—it’s the key to smarter, more strategic decision-making. Are you still relying on basic reports, or have you made the shift to intelligent data analytics?

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

We are on the cusp of a monumental shift in how we work and live, driven by #AI, and it's happening faster than any prior innovation. ✅ Faster than the mainframe. ✅ Faster than the PC. ✅ Faster than the web. ✅ Faster than the smartphone. There has been a massive acceleration in AI innovation in the last 24 months. You can see it in the chart below, produced by EpochAI. Compute growth is now increasing by a factor of 4-5x/year. The AI revolution is here, poised to deliver more profound and far-reaching changes than any previous wave of technology. The downside? AI innovation is outpacing your enterprise data capabilities by a wide margin. It’s not even close. Legacy apps and their hyped AI agents are making your enterprise data problem worse, not better. Why is that? The data is buried inside apps and used for just one specific business area. AI promises to transform businesses, but legacy applications treat data like a VIP-only event, locking out most of the enterprise and squandering its most valuable asset: trusted data. Why would you let one application control your customer data? Are you going to use that one vendor across the entire enterprise? I don’t think so. Imagine a world where data across the organization is free-flowing and unified across your organization, hydrating AI that predicts needs, automates tasks, and instantly connects the dots between every customer interaction, supply chain event, product and operational decision. Organizations that move quickly to build foundational data capabilities will have a significant and lasting competitive advantage as they prepare to move from isolated AI use cases to fully integrated AI strategies that transform the enterprise. The time to act is now—lean in, hit the accelerator on your data unification efforts, and position your business to lead in the age of AI. This is not a “nice to have.” This is a must-have. Because the AI-powered enterprise is closer than you think—maybe ten years, more likely five. Thinking AI will eventually solve your data problems? Don’t wait. The future of your business depends on fixing them now!

𝐌𝐢𝐝-𝟐𝟎𝟐𝟓 𝐌𝐢𝐥𝐞𝐬𝐭𝐨𝐧𝐞: 𝐀𝐈 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐌𝐨𝐯𝐞𝐬 𝐟𝐫𝐨𝐦 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐭𝐨 𝐄𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 According to #IBM’s “5 Trends for 2025” report, leaders are now scaling innovation and empowering teams to unlock AI’s full potential. 🔹𝐊𝐞𝐲 𝐒𝐡𝐢𝐟𝐭𝐬 𝐢𝐧 𝐀𝐈 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧  👉AI is moving from experimentation to execution ▪46% of executives say their organizations are scaling AI this year, focusing on optimizing existing processes and systems. ▪44% are using AI for innovation, driving new opportunities and business models. ▪Only 6% of organizations are still in the experimentation phase, down sharply from 30% just a year ago.  👉AI is now a core driver of business transformation ▪85% of executives believe AI is enabling business model innovation. ▪89% say AI is driving product and service innovation. 🔹𝐇𝐨𝐰 𝐋𝐞𝐚𝐝𝐞𝐫𝐬 𝐀𝐫𝐞 𝐏𝐮𝐬𝐡𝐢𝐧𝐠 𝐓𝐞𝐚𝐦𝐬 𝐅𝐨𝐫𝐰𝐚𝐫𝐝  👉Empowering people at every level ▪Democratizing decision-making so teams can act quickly and effectively. ▪Providing robust tools, training, and support for employees to succeed with AI.  👉Fostering a culture of innovation ▪Leaders are redefining leadership by delegating more decisions as AI augments roles across the organization. ▪Teams are encouraged to rethink workflows and deploy AI agents in new ways to boost performance.  👉Strategic support for teams ▪Implementing strong security and governance as AI becomes more embedded in operations. ▪Leveraging data-driven decision support for smarter, faster choices. 🔹𝐓𝐡𝐞 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐂𝐚𝐬𝐞 𝐟𝐨𝐫 𝐀𝐈 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧  👉AI is now a business imperative ▪68% of CEOs say AI is changing core aspects of their business. ▪61% believe competitive advantage depends on having the most advanced generative AI. ▪64% of leaders see automation’s productivity gains as essential to staying competitive.   👉Bold investment and risk-taking ▪62% of leaders invest in new technologies before fully understanding their value, determined not to fall behind. ▪The winners are balancing experimentation with strategic, incremental innovation. 🔹𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐚𝐥 𝐒𝐭𝐞𝐩𝐬 𝐋𝐞𝐚𝐝𝐞𝐫𝐬 𝐀𝐫𝐞 𝐓𝐚𝐤𝐢𝐧𝐠  👉Talent and skills ▪Rethinking talent strategies—people are the most important tech investment. ▪Focusing on targeted training, upskilling, and making AI proficiency a must-have.  👉Technology and data ▪Building integrated, enterprise-wide data architectures for cross-functional collaboration. ▪Using proprietary data to unlock the full value of generative AI. The organizations that will win are those where leaders empower their people, invest in skills, and foster a culture where AI-driven innovation thrives. 𝐒𝐨𝐮𝐫𝐜𝐞: https://lnkd.in/gRNGWqNQ #AI #DigitalTransformation #GenerativeAI #GenAI #Innovation  #ArtificialIntelligence #ML #ThoughtLeadership #NiteshRastogiInsights 

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