Big Data is Out, Small Data is In 

Remember when “big data” was the tech world’s version of avocado toast? Everyone wanted it, everyone claimed to have it, and everyone swore it was the future. It was supposed to solve all our problems—from curing diseases to predicting which flavor of potato chip would dominate the snack aisle next. For a while, it felt like we were all just a Hadoop cluster away from omniscience. 

But here’s the thing about big data: it’s not that big of a deal anymore. And it turns out, size does matter, just not the way we thought. 

The rise and stall of big data. 

For years, big data was the shiny new toy. Entire industries threw money at massive data lakes (which, let’s be honest, often turned into data swamps) with the hope that insights would magically bubble up. Vendors promised that if we could just capture and analyze all the data, the answers to life’s mysteries would reveal themselves—like an oracle, but powered by servers and caffeine instead of mythology.

And to be fair, it did deliver in some areas. Netflix recommends what to watch next, and Google Maps predicts traffic better than your dad ever could. In the right hands, it helped businesses uncover trends, optimize operations, and personalize customer experiences at a scale previously unimaginable.

But for most organizations, the reality has been a bit less dazzling. Big data is expensive, unwieldy, and often delivers diminishing returns. Many companies have found themselves overwhelmed by sheer volume, struggling to separate the meaningful from the meaningless. Collecting more doesn’t necessarily mean knowing more, especially if you’re drowning in noise while searching for a signal. In fact, the chase for “more data” often diverts focus and resources away from the insights that could actually drive impactful decisions.

It’s a classic case of overpromising and underdelivering—not because the technology isn’t impressive, but because the approach often prioritizes quantity over clarity. 

Enter small and wide data: less is more. 

Cue the shift: Gartner’s latest prophecy predicts that in 2025, a whopping 70% of organizations will pivot away from big data toward “small and wide data.” Because small and wide data is about working smarter, not harder. These approaches recognize that value doesn’t come from sheer volume but from precision, context, and actionable insights.

• Small Data: Think of this as the “retro” of data. It’s focused, digestible, and designed for human-scale decision-making. Instead of hoarding terabytes of clickstream data, small data asks: what’s the one metric that actually drives value here? This approach allows teams to focus on what truly matters without getting bogged down in irrelevant noise.
• Wide Data: If small data is retro, wide data is eclectic. It’s about blending diverse sources of information—structured, unstructured, numerical, textual—to get a richer, more nuanced picture. Wide data says, “Hey, maybe the answer isn’t in this one dataset. Let’s zoom out.” By considering external factors, industry trends, or even customer sentiment, wide data creates a more comprehensive understanding of the problem at hand.

Together, small and wide data address big data’s fatal flaw: the idea that more is always better. They emphasize quality over quantity and seek to make insights more accessible, not more overwhelming. For businesses, this shift means less time wrangling data and more time acting on it. It’s a smarter, leaner approach that aligns perfectly with today’s demand for agility and precision.

Why the industry is coming full circle. 

In a way, this shift isn’t new at all. It’s a return to the fundamentals. Before “big data” became a thing, analysts were already solving complex problems with smaller, targeted datasets. The tools were just less shiny, and the buzzwords hadn’t been invented yet.

But now, with AI and advanced analytics in the mix, small and wide data are getting their glow-up. They’re not just old-school; they’re optimized for today’s realities. Think edge computing, real-time decision-making, and the rise of tools that can extract meaning from messy, unstructured data. It’s less about building Mount Everest and more about assembling a Swiss Army knife—versatile, efficient, and ready for anything.

This return to smaller, more focused datasets doesn’t just benefit data analysts, it benefits everyone in an organization. By simplifying datasets and focusing on what matters, businesses can cut costs and increase the speed of decision-making. In today’s fast-paced world, where agility is a competitive edge, this shift is more than just a trend; it’s a necessity. Wide data’s ability to blend sources also provides a broader context, which is crucial in industries like healthcare, where diverse inputs can mean the difference between a good and a life-saving decision.

How to ride the small data wave. 

If you’re still clinging to the big data train, don’t panic. You don’t need to abandon ship entirely; you just need to pivot. Organizations that have successfully made the shift to small and wide data often start by asking the right questions: What insights are we really missing? Where are our biggest data gaps, and can they be filled with smaller, more focused datasets or by integrating new types of information?

The transition isn’t about throwing everything out and starting from scratch, it’s about layering new strategies onto what already works. Small data approaches can complement existing data infrastructure, providing clarity and direction where overwhelming volume once obscured actionable insights. Wide data strategies, on the other hand, can pull together silos across departments, giving decision-makers a 360-degree view of operations, customers, or emerging trends.

Here’s how to get started: 

1. Focus on Outcomes, Not Outputs: Stop measuring success by how much data you collect. Instead, ask what decisions your data is helping you make. 
2. Leverage Unstructured and Diverse Sources: Texts, images, social media chatter—these “messy” data types are goldmines when analyzed alongside traditional datasets. 
3. Invest in Human-Centric Analytics: Remember, most decisions are still made by humans. Small and wide data make it easier to provide insights that real people can actually use. 
4. Rethink Infrastructure: You don’t need a data lake the size of the Pacific. Scalable, targeted solutions often deliver better ROI. 
5. Experiment with AI: Tools like natural language processing and machine learning thrive in wide data environments. They’re great at finding patterns in chaos. 
   
   Article Continues Below:

The takeaway: The circle completes. 

The analytics industry has a habit of chasing shiny objects, but the pendulum always swings back. Big data had its moment, and while it’s not going away entirely, it’s no longer the end-all, be-all. Small and wide data are here to remind us that sometimes, less really is more—and that the best insights often come from looking at the data we already have in smarter ways.

This shift also signals a deeper change in how we approach data. It’s no longer about being overwhelmed by the size or scale of our datasets, but about being strategic in how we use them. Organizations are realizing that the right data, combined with the right context, can make even the smallest dataset a powerhouse of actionable insights.

Moreover, this isn’t just a technical adjustment—it’s a mindset shift. It’s about prioritizing clarity over clutter, and impact over volume. Teams that once chased the allure of more data are now focusing on finding the right data and making it accessible to decision-makers at all levels. It’s less about building a sprawling data empire and more about empowering teams with tools and insights that drive meaningful action.

As we complete this full-circle moment, there’s also a certain elegance in seeing technology mature. From the wild, uncharted waters of big data to the focused, value-driven approaches of small and wide data, we’re entering an era where analytics truly serves people, not the other way around. It’s a future where organizations of all sizes can harness the power of their data without getting buried under it.

So, let’s not mourn big data. Let’s thank it for its service and move on. Because the future of analytics? It’s smaller, wider, and infinitely more interesting.

Ready to rethink your big data strategy? 

If you’ve been nodding along and thinking, “Yeah, this all makes sense,” then it might be time to take the next step. Crafting a data or AI strategy doesn’t have to be a daunting, overly technical exercise. It’s about real talk, understanding where you are today, and figuring out where you want to go.

We’re here to help you strip away the noise and focus on what matters: data that actually drives decisions, systems that scale to your needs (not the other way around), and strategies that feel tailored, not templated. Whether it’s breaking down silos, rethinking your infrastructure, or finally making sense of that “unstructured data” everyone keeps talking about, we’ve got your back.

So, if you’re ready to turn the chaos into clarity, let’s chat. No pushy pitches, no pretense—just practical ways to make your data work smarter, not harder. The future’s calling, and it’s smaller, wider, and way more interesting than ever before.