Imagine your company’s data as a very complicated dinner party. You’ve invited everyone—customer trends, sales figures, marketing results—but they’re all sitting at different tables, not talking to each other, with some guests so isolated you’ve forgotten they even exist. You’re the host (lucky you!), and you’re stuck trying to figure out how to get them all to mingle. It’s not just awkward; it’s a mess.
This is the reality for most SMBs when it comes to their data. Sure, data is hailed as the new oil or gold (pick your favorite cliché), but for many companies, it’s more like a hoarded treasure that no one can find. But things are changing, and data democratization is now entering the game. That is, the idea that data should be available to everyone, not just the tech wizards in the basement who speak SQL, communicate in workflows and consider Excel their second language.
Ultimately, data democratization should foster collaboration among different teams in departments – commercial and beyond -, breaking down silos and driving innovation within an organization.
There is one catch, though. While data democratization sounds lovely in theory, in practice, it’s been a bumpy ride. The tools that were supposed to democratize data have often made things worse or—at best—only half-delivered. Let’s take a look at the history books…
Remember Business Objects? Yeah, about that…
Let’s take a little trip down memory lane to the early 2000s, when Business Objects and Cognos were the cool kids on the block. These tools *promised* to let regular business users interact with data without needing a PhD in data science. The problem? They were anything but simple. You still needed IT to set things up, and once you had the dashboards, they were often out of date by the time anyone could use them. It was like showing up to a party after all the good appetizers were gone.
These tools were expensive and over-engineered for many businesses, especially SMBs that didn’t have a dedicated IT army to maintain them. Data democratization? Not quite. It was more like data aristocracy—a few people at the top knew how to use these tools, but the rest of us were left scratching our heads.
And, let’s not forget, their focus was on visualization, not as much on cleaning and interpreting.
The rise of the “drag and drop” era
Then came the “drag and drop” revolution with tools like Tableau and Power BI. The promise was, “You don’t need to know how to code. Just drag, drop, and boom! Insights!” (a trend forming here). And, to some extent, it worked. Suddenly, the sales team could build reports, and marketing could whip up dashboards without begging the IT department for help.
But—and it’s a big but—these tools assumed that your data was already neat and tidy. They expected that someone had cleaned it, structured it, and laid it out in a nice, orderly fashion. For SMBs, though, this was rarely the case. More often than not, data was scattered across CRM systems, Google Sheets, and random spreadsheets from different departments.
The only solution was – drumroll – to build the tools yourselves. Home-bred custom tech. Works for deep pockets, but the rest of the mortals had to still resort to pick and axe.
Take Monzo, the UK-based digital bank, for instance. As Monzo rapidly scaled its operations, the company found itself managing massive amounts of customer data from different systems: customer support logs, transaction data, and feedback loops. Data was fragmented across tools, making it hard to analyze quickly and effectively. To solve this, Monzo invested heavily in building an internal data platform that unified these sources, enabled seamless access, and drove more efficient customer service. By consolidating their data infrastructure, they managed to streamline decision-making and improved real-time reporting across teams.
Similarly, Airbnb struggled with fragmented data in its early years. The company dealt with data from customer feedback, booking systems, and property listings, all scattered across multiple platforms. This limited their ability to create a unified view of their users. In response, Airbnb built an in-house system, Airflow, that cleaned and structured their data, making it accessible to both engineers and non-technical teams like marketing and customer support. This democratized access allowed Airbnb to build an analytics-driven culture, which certainly helped to give them the massive edge they’ve achieved nowadays.
In truth data democratization has been more of an illusion than a reality for most businesses. Even the best-intentioned tools like Domo.AI or Alteryx still require clean, structured data to function at their best (and tech-savvy folks…). And therein lies the problem: for most companies, especially SMBs, data is messy.
The state of play leans towards AI
The big shift in data democratization today is artificial intelligence. Unlike previous tools that relied on pre-cleaned data, AI-driven platforms are helping businesses clean, structure, and even enrich their data without needing to rely on data experts. This is where the magic of AI lies—not just in answering questions or putting a nice ribbon around a chatGPT wrapper, but in making sure the data itself is usable and contextualized.
Take L’Oréal as an example, a ‘Digital First’ company, as they now call themselves. L’Oréal has massive datasets coming from global campaigns, customer transactions, and market research. To handle the complexity and fragmentation of their data, they adopted AI-powered solutions to manage those huge amounts of data, allowing commercial and finance teams to access insights in real time. This shift is now presenting a competitive edge, increasing conversions in key markets.
Similarly, Tinkery (yes, just a subtle mention here) is pushing the boundaries by using AI to contextualize and structure data from multiple sources, meaning you don’t have to wait for your data scientists to come to the rescue (what data scientists, you might be thinking?). Imagine walking into a room filled with jumbled papers and having someone instantly organize them into coherent stacks. That’s what AI is doing for your data.
Why does this all matter?
For SMBs, the promise of data democratization isn’t just a pipe dream—it’s a necessity. With AI stepping in to bridge the gap, businesses can finally start using their data in meaningful ways. It’s no longer about staring at pretty dashboards and wondering if the data behind them is even accurate. It’s about having a platform that actually makes sense of the data, automatically cleaning and structuring it, and offering real insights without the headache.
And the best part? You don’t need a team of engineers to make it happen.
So, where are we now? The future of data democratization is bright, but we’re still on the journey. AI is starting to remove the barriers, but it’s up to businesses to adopt these new tools and approaches. Gone are the days of waiting for IT to send you a report; the power to make sense of your data should be in the hands of every team in your organization—whether it’s sales, marketing, or operations.
And maybe—just maybe—you can finally invite everyone to the same dinner party and have them actually talk to each other.