Over the last decade, companies have increasingly discovered that the most valuable assets they possess aren’t necessarily their products, their brands or even their intellectual property. It’s their data. Data about customers, markets, operations and competitors has become the raw material from which modern companies derive insights, predictions and, ultimately, competitive advantage.
However, collecting and using data is rarely straightforward. Most organizations struggle with fragmented data sources, manual reporting processes and analytics that arrive too late to influence decisions. As a result, a new generation of companies is emerging that focuses not on creating more data, but on helping to collect, structure and exploit data more effectively. Two companies that I’m involved in and that illustrate this shift particularly well are Burt Intelligence and Forloop.ai.
Burt Intelligence operates in the digital publishing and advertising industry. Companies in this space generate enormous amounts of data. Every campaign produces signals: impressions, clicks, conversions, delivery pacing, pricing, inventory utilization and much more. In theory, this data should allow publishers to optimize their advertising operations continuously. In practice, the opposite often happens. Data is scattered across advertising platforms, sales systems and analytics tools. Analysts spend hours manually reconciling numbers before they can even begin to understand what’s happening.
This is the problem addressed by Burt Intelligence. It provides an analytics and decision-intelligence platform specifically designed for digital publishers and media companies. The platform consolidates data from multiple advertising systems, harmonizes it and provides reporting, forecasting and operational insights that help optimize revenue and advertising operations. Rather than simply visualizing historical data, Burt’s approach is to provide a single source of truth for advertising performance and use AI-driven analytics to identify revenue opportunities and operational improvements.
This shift from fragmented reporting to integrated decision intelligence has profound implications. When publishers can immediately see campaign performance, delivery risks or revenue discrepancies, they can act faster. Instead of reacting to problems weeks later, they can continuously optimize pricing, inventory allocation and campaign execution. In other words, Burt transforms data from something that needs to be reconciled into something that can directly drive decisions.
Whereas Burt focuses on making sense of internal operational data, Forloop.ai targets a different but equally important problem: collecting external data. Modern companies increasingly depend on information that lies outside their own systems. Prices on competitor websites, product positioning on e-commerce platforms, online shelf visibility and promotional campaigns across markets all contain valuable signals. Obtaining this data, however, is surprisingly difficult.
Web scraping pipelines break. Data formats change. Infrastructure needs constant maintenance. As a result, data teams spend enormous effort simply collecting and cleaning external data before they can analyze it. Forloop.ai tackles this challenge by providing a platform that allows companies to build automated pipelines that collect, prepare and integrate external data from websites, APIs and other sources. The platform combines web extraction, data preparation and pipeline orchestration in a single environment. It allows users to turn websites into APIs, schedule continuous data collection and automatically repair broken data pipelines when websites change.
One particularly compelling use case is in the fast-moving consumer goods (FMCG) industry. Brands need to monitor product prices, promotions and visibility across hundreds of online retailers. With automated data collection and AI-based product matching, companies can track competitor strategies, detect pricing changes and optimize promotions in real-time.
What makes this especially interesting is the direction in which these platforms are evolving. Tools like Forloop are moving toward generative-AI-driven “vibe coding,” where users describe the data pipeline they want and the system generates the workflow automatically. This dramatically lowers the barrier to building sophisticated data pipelines. In effect, data engineering is becoming conversational.
Taken together, these two companies illustrate an important pattern in modern organizations: from simply collecting data to creating an advantage based on the data. Here, the first challenge is making sense of internal data. Companies like Burt help organizations unify fragmented operational data and turn it into insights that support daily decision-making. The second challenge is accessing external data. Platforms like Forloop allow organizations to collect and analyze market signals that previously required large data engineering teams.
When these two capabilities are combined, something powerful happens. Companies move from occasional, retrospective analysis to continuous situational awareness. They can monitor their own performance while simultaneously observing the market around them. Decisions become faster, more informed and increasingly automated. In many industries, this is becoming the real source of competitive advantage.
It’s not the company with the most data that wins. It will be the company that can collect the right internal and external data, process it quickly and act on it continuously. And increasingly, that capability is becoming the defining characteristic of modern digital organizations. To end with Geoffrey Moore, “Without big data, you’re blind and deaf in the middle of a freeway.”
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