Innovation with data: how to use analytics to create products and experiences.

From raw data to concrete opportunity.

Almost every company claims to be data-driven. Few, however, manage to transform analytics into innovation with real data: new products, services, experiences, and revenue streams. The turning point lies less in the technology and more in how the team organizes the journey from data to the production experiment.

A good starting point is to structure data-driven innovation in three layers: discovery, design, and decision. In the discovery layer, the focus is on mapping sources (product, CRM, support, digital channels) and consolidating everything in an analytical environment, such as BigQuery, Snowflake, or an enterprise Lakehouse. In the design layer, innovation frameworks that connect insights to business value hypotheses come into play. In the decision layer, product and business teams prioritize what becomes a test, with clear governance of metrics, risks, and expected return.

This chain of events creates a continuous process: data becomes insight, insight becomes hypothesis, hypothesis becomes experiment, and if it works, it becomes a feature, service, or new customer journey.

Frameworks, case studies, and translating analytics into a roadmap.

To bring analytics to life, several frameworks help organize the chaos. The first is the double diamond applied to data: explore what the data reveals (diverge), focus on relevant problems (converge), generate possible solutions (diverge again), and finally, select feasible tests (converge). At each stage, dashboards and predictive models support the choices, avoiding decisions guided solely by opinion.

Another approach is to combine jobs to be done with analytics. Instead of just looking at demographic segments, the team cross-references behavioral data with the ‘job’ the client is trying to solve. For example: a mobility app discovers, through analysis of journeys and schedules, a cluster of users who only need predictability to get to work. From there, a subscription product with pre-scheduled routes is created, priced based on historical demand and cancellation data.

Furthermore, propensity and recommendation models are used to create personalized experiences. In e-commerce, recommendation algorithms powered by real-time analytics already support dynamic storefronts and individualized promotions, increasing average order value and frequency. In financial services, risk and affinity scores help design tailored credit and insurance packages, balancing risk appetite and revenue opportunity.

Trends in analytics that unlock innovation with data.

Three market trends are accelerating data-driven innovation. The first is the integration of generative AI into analytics environments: business teams can ask questions in natural language and automatically receive insights, simulations, and summaries, democratizing access to analytical intelligence. This reduces dependence on highly technical teams and opens up space for more people to propose evidence-based ideas.

The second trend is the advancement of low-code and no-code platforms in BI. These allow PMs, marketing analysts, and teams to create their own dashboards, test data slicing, and validate hypotheses in days, not months. In practice, this creates micro-innovation labs spread throughout the company, all connected to the same source of truth.

Finally, the orchestration of real-time data and predictive models embedded in products enable continuous innovation. In retail, algorithms dynamically adjust assortment and prices; in industry, failure prediction guides advanced maintenance services; in digital services, continuous A/B experimentation feeds back into the product backlog. Those who can combine a clear strategy, data governance, and a culture of experimentation will have, in data-driven innovation, the main competitive advantage in the coming years.