Beyond Big Data - AI Hype to ROI Reality
Ocient’s annual Beyond Big Data: From Roadmap to Reality report
Note: This non-sponsored post is featured content based on Ocient’s annual “Beyond Big Data: From Roadmap to Reality” report, which surveyed 531 data and IT leaders. If you are a company, start up, agency or entrepreneur interested in being featured on The Full Data Stack, please contact me at: hoyt@thefulldatastack.com
I was reached out to by a PR firm with Ocient’s latest survey results. Below are the top 10 points from it I think are interesting.
1. AI ROI is Real
The shift from “AI experimentation” to “AI ROI” is the story here. Ocient’s survey shows 90% of leaders reporting measurable returns. Whether comprehensive or not, I think we’ve moved beyond the proof-of-concept phase into operational reality for AI. Data platforms, orchestration tools, and infrastructure providers need to demonstrate clear paths to business value, not just technical superiority. The 5% reporting no ROI are likely organizations with immature data foundations or misaligned expectations. For data teams, this means the conversation shifts from “Can we do AI?” to “How do we scale what’s working?” The data stack of 2025 must be built for production AI workloads, not just analytics.
2. AI is Crushing Infrastructure
The statistic that shouldn’t surprise anyone is that 97% of organizations are experiencing notable processing increases with 55% calling them “significant”. Given a huge amount of Gen AI compute is subsidized by VC money means it is hard understand the TOC of full AI adoption. Legacy architectures designed for batch ETL and BI dashboards don’t look suited for AI’s realtime, high volume, multi modal demands. Ocient notes that one third of organizations weren’t ready. Maybe that means a wave of emergency migrations and infrastructure overhauls. This could lead to new competitors in data warehouse and lakehouse markets. The question will be, “What solution best fits the new AI data model?”.
3. Security is Still the #1 Pain Point
Four consecutive years of security topping the pain point list on this report tells us the data industry has a security problem. As data volumes explode and AI introduces new attack vectors (model poisoning, prompt injection, data exfiltration through embeddings), security doesn’t seem like it is keeping up with innovation. The fact that 75% are investing in security equaling their investment in core infrastructure tells you where we are with these early stage AI initiatives. We’ve been throwing our data and questions to third party servers. Even if you are on an enterprise account, is your data really private and secure? Highly regulated industries like healthcare and finance have a huge liability when it comes to incorporating AI into their processes. Having the right security means you need to rethink your data stack. Hosting your own inference and compute is very expensive, so companies will have to think long and hard about how to approach using AI with their data.
4. The Revenue Gap
The report raised a really interesting finding that I had always felt, but couldn’t prove. AI is delivering ROI through operational efficiency but failing to unlock new revenue streams for 40% of organizations. This means we’re still in the “AI as cost-saver” phase rather than “AI as business model transformer.” But the companies that crack the code to create more revenue generating AI use cases (dynamic pricing optimization, personalized product recommendations, predictive customer acquisition) will have a huge opportunity for greater market penetration. Right now the gap between cost and monetization is really wide. We have great infrastructure for training models and running queries but weak infrastructure for serving AI driven products at scale. Whoever bridges this gap wins the next decade.
5. Knowledge Gap at the Top
When 58% of technical leaders admit they don’t fully understand the technologies they’re investing in, we have an education crisis masquerading as an adoption problem. This knowledge gap creates risk. Poor architectural decisions, misallocated budgets, and unrealistic expectations are common results. It also creates opportunity for consultancies, training platforms, and vendors who can translate complexity into clarity. The data industry has historically been terrible at explaining itself to non-technical stakeholders. The AI era demands we get better. It also creates the rise of “AI product managers” who can bridge technical and business domains. For the industry, it means simplification is a competitive advantage. The platforms that can deliver sophisticated AI capabilities through intuitive interfaces will capture market share from other technically superior but operationally complex alternatives.
6. Data Warehouse Modernization Surge
The 17 point jump in warehouse modernization investment (58% to 75%) tells me that organizations are starting to understand that you can’t bolt AI onto legacy systems. AI is unique in that it doesn’t play by the same data rules that historical data workloads do. For data warehousing vendors like Snowflake, Databricks, BigQuery and MotherDuck this is a make or break moment to grab market share. The bar will be much higher this time around though. Old school marketing jargon like “cloud native” and “scalable” aren’t interesting anymore. The winners will be platforms that handle modern data problems like realtime streaming, support for ML workloads, offer predictable costs at scale, and have flexible deployment models (probably the biggest differentiator).
7. Explosive Data Growth
Ocient reports that fifty percent of organizations are expecting 75% annual data growth. That’s not just a storage problem. AI data is multi modal, meaning structured and unstructured. That type of data at that growth rate could break today’s architectures, cost models, and governance frameworks within a couple years. This drives several potential trends. The rise of data lakehouses that can handle structured and unstructured data economically, increased adoption of tiered storage with hot/warm/cold policies, and renewed focus on data quality over data quantity (because storing garbage at scale is expensive). But organizations need predictable costs even as volumes explode. We might start seeing more flat rate pricing as competition increases and commodity hardware continues to drop in price. For the industry broadly, it’s a reminder that the “store everything forever” mindset of the big data era needs to evolve into something more like “just keep what you need”.
8. Cloud-Only is Losing Ground
I’m a bit skeptical about this data point. The report indicates a 13-point drop in cloud-only priorities and a significant shift toward hybrid deployments (45% current, 90% planned for AI workloads). But let’s accept these figures for now. If accurate, they represent a maturation of cloud strategy. I’ve noticed a counterrevolution emerging with single-node compute (DuckDB, Polars). I feel like we keep forgetting that cloud services really just provide commodity hardware for workloads at a premium that your local laptop could handle with 10x more power. Cloud-only solutions aren’t always cheaper, faster, or more secure even for massive datasets and real-time workloads. Sometimes you want that on prem speed and reliability. Especially if you have consistent workloads that don’t expect to change overnight.
9. Real-Time Analytics Momentum
Historically, “real time” in data has meant a misalignment of definitions. Leadership said they wanted real time, but what they really meant was just daily. The report shows a jump from 47% to 67% investment in real-time analytics which reflects a shift in that definition problem. Modern businesses want to react to customer behavior, market conditions, and operational events in seconds, not hours. This has cascading effects across the data stack. Streaming platforms (Kafka, Pulsar) and change data capture (CDC) become table stakes and databases need to support both OLAP (for analytics) and operational workloads (for immediate use). For vendors, streamlining real time data tools will be really important (kakfa implementation is a pain). For data teams, real time architecture isn’t really optional anymore and I expect to see the industry take it much more seriously.
10. Predictive AI Over Generative
This last insight is my favorite. Despite generative AI dominating headlines, a striking 66% of organizations are prioritizing predictive AI applications. This is where I believe true business value resides. Predictive models that forecast demand, identify churn risk, and optimize inventory directly impact bottom line performance in measurable ways that chatbots and content generation simply cannot match. However, this comes with a prerequisite. Data quality is non-negotiable. Predictive AI requires clean historical data, feature engineering pipelines, model monitoring infrastructure, and integration with operational systems. The generative AI stack (vector databases, embedding models, LLM APIs) is largely orthogonal. Many companies have and will invest in the initial wave of Gen AI technology, but Predictive AI is the blue sky frontier. This suggests a return to traditional ML but integrating with agents and workflows. Investing in MLOps platforms, feature stores, and automated retraining pipelines will deliver more ROI than chasing every new LLM release.
Source: Ocient “Beyond Big Data: From Roadmap to Reality” 2025 report, surveying 531 data and IT leaders across 15+ industries