Chloe Maraina has spent her career proving that data is more than just a collection of numbers—it is a narrative waiting to be told. As a leading expert in Business Intelligence and data science, she has helped organizations navigate the complex transition from simply collecting information to extracting real economic value. Her vision for the future of data management emphasizes the human element, focusing on how visual storytelling and integrated analytics can transform raw technical assets into actionable business strategies.
The following discussion explores the evolving landscape of data monetization, examining the critical shift from selling raw datasets to providing sophisticated, embedded analytics. We delve into how this transition affects organizational infrastructure, the expanding responsibilities regarding data accuracy, and the ways in which predictive insights are broadening the market for data-driven decision-making. By moving beyond the delivery of “ingredients” to providing the “answer,” businesses are redefining their relationship with their customers and the very value of the information they hold.
Organizations often face a choice between licensing structured datasets via APIs and delivering embedded dashboards. What specific infrastructure shifts are required when moving from selling raw “ingredients” to providing the “answer,” and how does this transition affect your master data management strategy?
The shift from providing raw ingredients to serving the final answer requires a fundamental rebuilding of the internal pipeline. When you sell raw data, your primary job is essentially logistics—ensuring that the data is delivered securely and meets privacy standards like GDPR or CCPA. However, once you move into delivering embedded dashboards, the heavy lifting of cleaning, normalizing, and interpreting that data moves upstream into your own environment. This makes Master Data Management (MDM) absolutely paramount because you are no longer just a pass-through; you are the source of truth. If a customer-facing dashboard displays a conflicting metric or a broken trend line, it doesn’t just look like a data error—it looks like a failure of your product’s core promise. You have to invest heavily in robust data quality frameworks that can handle the “sensory” pressure of real-time reporting where every discrepancy is visible to the end user.
Selling raw data shifts the burden of cleaning and interpretation to the buyer, whereas analytics monetization handles that work upstream. How does this change in responsibility impact your brand’s liability regarding accuracy, and what steps must you take to ensure data lineage remains transparent to the end user?
When you monetize analytics, you are essentially attaching your brand’s reputation to every insight generated by your platform. In the traditional model, the buyer’s analysts were responsible for any misinterpretations, but now, if a predictive model forecasts demand incorrectly, the liability sits squarely on your shoulders. This creates an emotional weight for data teams because the stakes are no longer just technical; they are reputational. To mitigate this, we have to move toward extreme transparency in data lineage, showing the “receipts” for how a specific insight was derived. It involves building features that allow users to drill down into the logic of the calculation, ensuring they feel the same confidence in the result as they would if they had performed the analysis themselves. This shift requires a rigorous focus on quality control that far exceeds what is necessary for simply licensing a dataset through a marketplace.
When multiple providers offer similar datasets, the competitive advantage often moves to those performing the analysis. In what ways does delivering predictive models or operational insights allow for greater product differentiation, and how do you measure the resulting increase in economic value captured by your organization?
In a crowded market, raw data quickly becomes a commodity, and when everyone is selling the same geolocation or transaction data, the race to the bottom on price is inevitable. By delivering predictive models or operational insights, you transform that commodity into a unique product capability that is much harder to replicate. This differentiation allows you to capture a larger share of the economic value because you are solving a specific business problem rather than just providing a resource. We measure this value through increased customer stickiness and the ability to command premium pricing for “insight-driven” features that save users hours of manual labor. Instead of just seeing raw numbers, a logistics manager sees a forecast of delivery performance, and that immediate utility creates a much stronger bond between the provider and the client. It’s the difference between selling someone a bag of flour and selling them a perfectly baked loaf of bread; the latter always commands a higher margin and builds more loyalty.
Raw data monetization assumes the customer possesses high data literacy and specialized infrastructure. How does shifting to an analytics-driven model expand your addressable market to smaller organizations, and what specific decision-support features are most effective at making complex data accessible to non-technical users?
The most exciting part of this shift is the democratization of data, as it removes the requirement for a client to have a dedicated team of data scientists. Small to mid-sized organizations often have the same problems as global giants but lack the infrastructure to clean and analyze massive datasets on their own. By delivering analytics through embedded tools, we effectively become their outsourced data department, making complex trends understandable through visual storytelling. Features like automated forecasting, natural language explanations of data shifts, and performance “health scores” are incredibly effective at guiding non-technical users toward the right actions. We are seeing a major trend here, with 76% of organizations already utilizing embedded analytics internally to bridge this literacy gap. By packaging the “answer,” we open the door to a massive segment of the market that was previously excluded because they didn’t have the tools to process the “ingredients.”
With a significant majority of organizations now prioritizing embedded analytics and business intelligence, the focus is shifting toward real-time operational tools. What are the primary governance trade-offs between maintaining strict privacy compliance for raw data and ensuring high-speed data quality for customer-facing dashboards?
The tension between speed and security is the defining challenge for modern data leaders. When you are licensing raw datasets, your governance focus is almost entirely on privacy and licensing—making sure the “gates” are secure. But with real-time operational dashboards, the focus must expand to include high-speed quality and lineage, ensuring that the data moving at lightning speed is still accurate and compliant. You often have to make hard choices about how much data to aggregate to protect privacy versus how much granular detail is needed to provide a useful insight. The goal is to create a seamless experience where the user feels the “pulse” of their business in real-time without ever compromising the underlying security of the data assets. With 84% of organizations expecting their focus on business intelligence to increase by 2026, the pressure to master this balance is only going to intensify.
What is your forecast for the future of data monetization through 2026 and beyond?
By 2026, I expect the “raw data” market to become almost entirely a back-end utility, while the real economic battlefield will be won in the user interface. We will see a massive surge in “prescriptive analytics,” where platforms don’t just show you what happened or what will happen, but tell you exactly what button to press to fix a problem. The organizations that thrive will be those that treat data not as a digital exhaust but as a core product feature that enhances the user experience. We will likely see more industries following the lead of SaaS and fintech, where data insights are so deeply integrated that the user doesn’t even realize they are performing “data analysis.” As we head toward 2026 and beyond, the most successful companies will be the ones that can turn the cold, hard reality of big data into a warm, intuitive guide for human decision-making.
