Data Maturity: How Advanced Analytics Drive 2.5x Better Business Outcomes and a 28% Revenue Lift

Groundbreaking findings from a new IDC white paper, sponsored by Heap Analytics, illuminate how data maturity and product analytics influence digital experiences and broad business outcomes. The research surveyed digital experience decision-makers to map current maturity levels, adoption, and practices surrounding digital product analytics technology. It reveals that organizations with higher data maturity consistently achieve better revenue growth, efficiency, customer satisfaction, and lifetime value than their less mature peers. The study also identifies concrete best practices and opportunities for improvement, highlighting both what leaders do well and where laggards struggle to leverage data effectively. This article distills those insights into a comprehensive, practical narrative aimed at helping enterprises advance their data maturity and elevate product analytics across the organization.

Data Maturity and Its Impact on Digital Product Analytics

Data maturity refers to how thoroughly a company collects, cleans, analyzes, and leverages data to inform decision-making across product and business functions. The findings show a pronounced correlation between maturity and business outcomes. Most mature digital product analytics teams exhibit approximately 2.5 times greater improvement in business outcomes across the board than teams that are least sophisticated in their use of digital analytics tools and processes. When focusing specifically on revenue improvement as a key business outcome, the most mature teams outperform the least sophisticated by a substantial margin—nearly 28 percent in revenue uplift.

The IDC white paper, exploring the nexus between data maturity and product analytics, underscores that maturity is not a fixed trait but an evolving capability. It encompasses technology adoption, data quality, governance practices, cultural readiness, and the ability to translate data insights into action. In this sense, maturity is both a technical and organizational phenomenon: it involves the right infrastructure, the right processes, and the right mindset to embed analytics into strategic decision-making. The research confirms that higher data maturity—meaning how well a company uses data and integrates it into everyday decisions—consistently yields higher revenues and profits, greater efficiency, improved Net Promoter Scores, and enhanced lifetime customer value. These outcomes are not isolated but interdependent: efficiency gains amplify revenue potential, and stronger customer insights feed into better product experiences, which in turn drive loyalty and long-term value.

The paper’s scope centers on how an organization’s data maturity shapes the digital experience it delivers. By examining the adoption and use of digital product analytics technologies, culture, and practices, the study reveals that maturity translates into concrete, measurable business results. Companies that reach higher maturity levels tend to be more data-driven in their decision-making, more disciplined about how they validate and govern data, and more capable of turning analytics into timely, actionable product and customer experience improvements. Conversely, lower maturity levels are often accompanied by inconsistent data use, slower reaction times to changing conditions, and missed opportunities to optimize the user journey. The overarching narrative is clear: data maturity is a powerful lever for enhancing both the quality of digital experiences and the financial performance tied to them.

To translate these insights into practice, organizations should view data maturity as a holistic capability that integrates people, processes, and technology. The most successful teams do not rely on a single tool or dashboard; they cultivate a data culture where data-informed experimentation, rapid learning cycles, and systematic measurement are embedded in product development and ongoing optimization. They align data governance with product objectives, invest in data instrumentation that captures the right signals, and design analytics that are accessible to the teams responsible for action. When these elements align, data maturity becomes a sustainable competitive advantage rather than a one-off project or trend.

In summary, the data maturity advantage is not merely about possessing robust analytics technology. It is about integrating data into the decision-making fabric of the organization, aligning analytics with product strategy, and continuously improving how data informs customer experiences. The upshot is a virtuous circle: greater maturity enables more precise insights, which support better decisions, which in turn drive improved business outcomes, and so on. This cycle underscores why many leading organizations obsess over maturity as a core strategic initiative rather than a side project.

Best Practices Among Leaders: Understanding Customer Journey Friction and Beyond

A central revelation of the report is the stark gap between leaders and laggards in understanding customer journey friction points. Among data maturity leaders, 98 percent report having a good to excellent understanding of where customers encounter friction along their journey. By contrast, only 29 percent of laggards reach this level of understanding. This disparity highlights how crucial a precise, data-driven view of the customer journey is to driving improvements in product experience and business outcomes.

Understanding friction points is not merely about identifying where problems occur; it is about translating those insights into concrete optimization actions that improve conversion, retention, and satisfaction. Leaders typically employ end-to-end journey mapping, event-level telemetry, and user session analysis to uncover friction hotspots, such as bottlenecks in onboarding, friction in checkout, or friction in product discovery and discovery-to-purchase transitions. They combine quantitative data with qualitative feedback to validate findings, test hypotheses, and iterate rapidly. This approach enables teams to prioritize remediation efforts based on impact, feasibility, and the likelihood of measurable improvement.

In practical terms, best-in-class data maturity leaders invest in a structured approach to journey friction analysis. They establish a common taxonomy for measuring friction across stages, from discovery and activation to retention and expansion. They implement instrumentation plans that capture reliable signals for each friction point, ensuring data quality and consistency across platforms and devices. They create dashboards and performance signals that stakeholders can interpret quickly, supporting cross-functional collaboration between product, design, engineering, marketing, and customer success. They also standardize the process for running experiments to validate friction-reduction hypotheses, enabling learning to scale across teams and product lines.

Beyond friction comprehension, leaders demonstrate several other best practices that consistently correlate with higher maturity and stronger business outcomes. These include:

• Establishing a clear data strategy aligned with product and customer experience goals.
• Automating data validation, access policies, and dataset management to ensure data integrity and governance.
• Enabling fast, trustworthy decision-making by delivering near-real-time or near-time insights to the teams closest to action.
• Cultivating a culture that values experimentation, learning from failures, and evidence-based decision-making.

The evidence from the IDC study indicates that maturity leaders embrace these practices more comprehensively and systematically than lagging organizations, resulting in stronger, data-driven product decisions and superior business results. The implication for enterprises is straightforward: elevate the organization’s understanding of the customer journey, invest in disciplined analytics practices, and embed data-driven decision-making into product development workflows. When teams act on reliable insights about friction points and customer behavior, they create better digital experiences and realize meaningful gains in revenue, efficiency, and customer satisfaction.

AI Scaling and the Limits of Enterprise AI

The discourse around artificial intelligence in product analytics continues to evolve, with a growing recognition that AI scaling has tangible limits in the enterprise. The report highlights several practical constraints shaping how organizations deploy and scale AI technologies. Power caps, rising token costs, and inference delays are influencing how teams design and operate AI systems for product analytics. These constraints can slow the pace of experimentation, raise the cost of advanced analytics, and necessitate more thoughtful architectural choices that balance speed, cost, and accuracy.

To address these challenges, industry leaders are focusing on strategies to architect efficient AI systems that deliver real throughput gains while controlling resource usage. This includes optimizing model selection for specific tasks, using model compression techniques, caching, and retrieval-augmented generation where appropriate. It also involves streamlining data pipelines so that the inputs to AI systems are of high quality and low latency. By investing in scalable, efficient AI infrastructures, leaders can sustain experimentation and derive ROI from AI investments without being overwhelmed by resource constraints.

In addition to technical strategies, the human and process dimensions of AI adoption remain essential. Leaders are increasingly treating AI as a collaborative partner that augments human judgment rather than a substitute for it. They establish governance around AI usage, define accountability for AI-driven decisions, and ensure that AI outputs are explainable and interpretable for product teams. They also emphasize responsible AI practices, including bias monitoring, fairness checks, and robust validation to ensure that AI-assisted analytics improve the user experience without introducing unintended risks.

The industry still contends with the tension between rapid AI experimentation and sustainable, cost-aware deployment. The takeaway for enterprises is practical: pursue AI-enabled product analytics with a clear plan for managing costs, latency, and reliability. Invest in efficient inference pipelines, optimize data workflows, and maintain strong governance so that AI advances translate into measurable improvements in digital experiences and business outcomes, even in the face of token cost increases and power considerations. By combining technical efficiency with disciplined governance and a culture of responsible experimentation, organizations can continue to benefit from AI in product analytics without overextending their resources.

Note: While industry events and salons frequently surface discussions about AI architecture, optimization, and ROI, the core message remains focused on turning technical efficiency into real, on-the-ground gains in product performance. The intention is to help teams translate AI capabilities into tangible improvements in customer experience, conversion, and long-term value, rather than pursuing AI for its own sake.

Automation, Data Validation, and Dataset Management

Automation plays a pivotal role in advancing data maturity, particularly in the areas of data validation, data access policies, and dataset management. The IDC findings show a pronounced gap between leaders and laggards in this domain. Specifically, 80.1 percent of data-maturity leaders fully automate their data validation, data access policies, and dataset management processes. In contrast, only 3.2 percent of lagging organizations achieve full automation in these areas. Meanwhile, 72.1 percent of lagging organizations rely on manual processes or only basic automation for these essential activities. This stark difference underscores how automation capabilities drive reliability, speed, and governance across analytics initiatives.

Full automation in validation and governance yields several downstream benefits. It reduces the risk of inconsistent or erroneous data entering analytics workflows, accelerates the provisioning of data to product teams, and strengthens compliance with internal policies and external regulations. With automation, teams can enforce standardized data quality checks, ensure access controls are consistently applied, and maintain up-to-date datasets that reflect the latest customer interactions and product usage. These practices enable faster, more confident decision-making and reduce the overhead associated with manual data curation.

Beyond automation, the findings reveal noticeable differences in how quickly leaders receive answers and how experimentation is valued within their organizations. Approximately 84 percent of leading teams report that they receive answers to critical questions in minutes or hours, compared with only about 3 percent of laggards who experience similar turnaround times. This speed of insight is a direct outcome of mature data pipelines, automated governance, and streamlined analytics operations. It enables teams to iteratively test product changes, verify results, and scale learnings across a portfolio of products.

Perhaps more telling is the cultural contrast around experimentation. Ninety-one percent or more of leaders agree that their organization celebrates learning from experimentation, while only 77 percent of lagging teams feel the same. This difference reflects a broader cultural alignment with data-driven decision-making and a willingness to explore, fail, and iterate. A data-driven, experiment-friendly culture is a core ingredient of data maturity, reinforcing the technical capabilities with an organizational attitude that values evidence over opinion.

The study also highlights persistent needs for improvement that affect both leaders and laggards, reminding organizations that maturity is an ongoing journey. One striking finding is that 69 percent of companies report that decisions are still driven by the HIPPO—Highest Paid Person’s Opinion—without regard to available data. This tendency indicates that even in mature analytics environments, organizational dynamics can undermine data-driven decision-making. Another notable finding is that 81 percent of leading companies believe they could do more with the data they have access to, suggesting untapped potential within even top-tier teams. The implication is clear: while automation and governance are essential, unlocking the full value of data also requires empowering teams with broader access to relevant data, tools, and analytical capabilities.

In lagging organizations, a significant portion lacks access to advanced analytics tools such as session replay or specialized friction-identification tools. More than 65 percent report lacking access to these tools, and only 31 percent have formal training processes in place, compared with 71 percent among leaders. This disparity signals that expanding tool access and formal training can drive substantial performance gains for lagging teams, narrowing the gap with leaders and accelerating overall data maturity within the organization.

To operationalize these insights, enterprises should pursue a dual focus: (1) scale automation to ensure consistent data quality and governance, and (2) broaden access to analytical capabilities and training, enabling more teams to participate in data-driven decision-making. By doing so, organizations can realize faster insights, better product outcomes, and more sophisticated analytics practices that support sustained growth.

Data-Driven Decision-Making, Experimentation, and the HIPPO Challenge

A recurring theme across the findings is the tension between data-driven decision-making and organizational inertia. The report notes that 69 percent of all companies indicate decisions are often driven by the HIPPO, rather than by data. This statistic underscores a pervasive challenge: even with mature analytics capabilities, human factors and governance structures can override data insights. The persistence of HIPPO-driven decisions suggests that organizations must implement stronger decision governance, data literacy programs, and accountability mechanisms to ensure evidence-based choices prevail over individual authority.

Conversely, among leading organizations, there is a strong sense that more could be done with the data currently available. Approximately 81 percent of leaders believe there is untapped value in the data accessible to them, signaling a widely recognized potential for expansion. This sentiment reflects a culture oriented toward data-driven optimization, where teams consistently ask, “What more can we learn, build, and improve with the data at hand?” It also implies that leaders view data maturity as an evolving capability rather than a fixed level, with opportunities to extend analytics coverage, deepen models, and enhance decision support.

Experimentation emerges as a unifying thread across the leadership cohort. A large majority, about 89 percent, agree that their organization celebrates learning from experimentation. This cultural emphasis on testing hypotheses, embracing rapid iteration, and leveraging data to guide product decisions is a hallmark of advanced data maturity. In contrast, 77 percent of lagging teams report a culture that does not celebrate experimentation as consistently, which can dampen exploratory initiatives and slow the cycle of improvement.

From a strategic standpoint, addressing the HIPPO challenge requires a multifaceted approach:

• Establish clear data governance and decision-rights frameworks that elevate data as a central reference point for decisions.
• Build data literacy programs that empower a broad spectrum of employees to interpret analytics, understand data limitations, and participate in data-driven discussions.
• Create standardized experimentation playbooks that define hypotheses, success metrics, and decision criteria, reducing the reliance on subjective opinions.
• Invest in dashboards and decision-support tools that provide accessible, context-rich insights to product teams, enabling quicker, more confident decisions.

The convergence of higher data maturity, a culture that values experimentation, and governance that anchors decisions in data creates a powerful environment for sustainable growth. Enterprises that align people, processes, and technology around data-driven decision-making are better positioned to translate analytics insights into improved product outcomes, stronger customer experiences, and measurable business value. The HIPPO challenge is not insurmountable; with deliberate governance, education, and standardized experimentation, organizations can shift toward evidence-based decision-making as a core operating principle.

Access, Tools, and Training: Bridging the Gap for Lagging Teams

The IDC findings draw a clear line between leading organizations and lagging ones regarding access to tools, training, and formal processes. A substantial portion of lagging companies struggle with these foundational elements, which impedes their ability to realize the full benefits of data maturity. More than 65 percent of lagging teams report lacking access to essential analytics tools, such as session replay or friction-identification tools, that are commonly used to diagnose and optimize user experiences. This tools gap impedes their ability to observe actual user behavior, identify pain points, and validate hypotheses with concrete evidence.

Formal training also emerges as a critical differentiator. Only 31 percent of lagging organizations have formal training processes in place for data analytics, compared with 71 percent of leaders. Training plays a vital role in elevating data literacy, ensuring consistency in how data is interpreted, and enabling employees to apply analytics insights effectively within their roles. The absence of structured training can perpetuate misinterpretations, inconsistent data practices, and slower analytical progress.

Access to tools and robust training are not merely enabling capabilities; they are catalysts for cultural change toward broader data democratization. When more teams have the tools they need and the training to use them effectively, analytics capabilities extend beyond a small group of specialists to become embedded in product development, marketing, customer support, and executive decision-making. This democratization accelerates learning, reduces bottlenecks, and enhances the organization’s ability to react to evolving customer needs with speed and confidence.

The implications for lagging organizations are clear. To close the gap with leaders, these organizations should take a two-pronged approach:

• Expand access to core analytics tools, ensuring that product teams, designers, data engineers, and business stakeholders can observe user behavior and extract actionable insights. Prioritize tools that enable session replay, user journey analysis, and friction detection, while maintaining governance and data privacy controls.
• Institutionalize formal training programs that cover data literacy, analytics fundamentals, data governance, and the interpretation of key metrics. Training should be ongoing, include hands-on exercises, and connect directly to real product decisions to demonstrate tangible value.

By prioritizing tool access and training, lagging teams can accelerate their journey toward higher data maturity and begin translating analytics into improved product outcomes, better user experiences, and stronger business performance. The path to maturity involves both technology enablement and organizational development, and both dimensions are essential for sustainable progress.

Maturity Classifications: The Four Stages of Data Mores and Practices

IDC’s survey of more than 600 digital product builders identified four distinct maturity groups that span from lagging to leading: lagging, progressing, advancing, and leaders. This four-stage framework provides a structured view of how organizations evolve their data maturity across technology, culture, and practices. Each stage represents a different constellation of capabilities, processes, and outcomes, offering a roadmap for organizations aiming to move from less mature to more mature analytics ecosystems.

• Lagging: At this initial stage, organizations typically struggle with automation, data quality, and governance. They rely heavily on manual processes, lack access to advanced tools, and have limited formal training in data analytics. Decision-making is frequently influenced by opinion rather than data, and the organization may experience longer cycles to draw insights and implement changes in products and experiences.
• Progressing: In the progressing stage, companies begin to formalize analytics practices, adopt more automated processes, and invest in data infrastructure. They start to address gaps in data quality and governance, enabling more reliable decision-making. While improvements are evident, the integration of data insights into product decision processes is uneven, and there may be variability across teams.
• Advancing: Organizations at the advancing level demonstrate more mature data practices, broader access to analytics tools, and more consistent governance. Data-driven decision-making becomes the norm in several product teams, and experimentation is more systematically embedded in the development lifecycle. The culture around data is more robust, and leaders actively seek to maximize the value of data across the organization.
• Leaders: The leaders represent the highest level of data maturity observed in the study. They exhibit near-systematic automation of validation, access controls, and dataset management; comprehensive data democratization; and an established culture of experimentation and data-driven decision-making. They understand the customer journey deeply and leverage analytics to optimize digital experiences and business outcomes extensively. They also identify opportunities to do more with the data they have, signaling a continuous path of improvement rather than a fixed plateau.

The four-stage framework provides a practical lens for organizations to assess current capabilities, identify gaps, and chart a credible path forward. It reinforces the notion that data maturity is not a binary state but a continuum that encompasses people, processes, and technology. As teams progress from lagging toward leaders, they unlock more advanced analytics capabilities, elevate their culture around data, and accelerate improvements in digital experiences and business results.

In addition to the maturity framework, the IDC analysis emphasizes that improving data maturity requires a deliberate, cross-functional effort. It involves aligning product strategy with data strategy, investing in instrumentation and data quality, enabling broad participation in analytics, and maintaining a governance model that sustains quality and trust. A well-planned journey from lagging to leaders often includes targeted investments in automation, training, and tool access, as well as changes in organizational behavior that foster experimentation and evidence-based decision-making.

Overall, the four-maturity framework offers a concrete map for organizations seeking to advance their data maturity and product analytics maturity. It clarifies where they stand, what capabilities are most critical to develop next, and how to sequence improvements to maximize impact on digital experiences and business outcomes.

Practical Implications for Enterprises: A Roadmap to Elevate Data Maturity

The convergence of the study’s findings yields a practical, enterprise-focused roadmap for elevating data maturity and improving product analytics outcomes. The following strategic pillars synthesize the report’s insights into actionable steps that organizations can implement to accelerate their maturity journey and maximize business impact.

• Build a comprehensive data strategy aligned with product goals. A clear data strategy defines what data to collect, how to govern it, and how analytics will inform product decisions. It should specify data quality standards, data access policies, and the roles responsible for data stewardship. A well-articulated strategy ensures consistency across teams and channels and provides a north star for analytics investments.
• Invest in robust instrumentation and data quality governance. Reliable data is the foundation of any analytics program. Investments should prioritize data collection fidelity, accurate event tracking, consistent identifiers, and data quality validation mechanisms. Automated data validation and governance processes help maintain confidence in analytics outputs, enabling teams to rely on data for decision-making.
• Democratize access to analytics with governance. Broader access to data and analytics tools accelerates insight generation and product improvement. However, governance remains essential to preserve privacy, security, and data integrity. A balanced approach provides teams with the data and tools they need while enforcing standards that protect data and ensure accountability.
• Embed analytics into product development and decision-making cycles. Integrate analytics into the product lifecycle—from discovery and design through development, testing, and optimization. Design dashboards and reporting that deliver decision-ready insights to product managers, designers, engineers, and executives. Prioritize speed and clarity so insights can drive timely actions.
• Accelerate automation for speed, reliability, and scalability. Automation across data validation, access controls, and dataset management reduces manual overhead and error risk. It speeds up data provisioning to teams, enabling faster experimentation and shorter iteration cycles. Automation also strengthens governance by ensuring consistent application of policies and standards.
• Cultivate a culture of experimentation and evidence-based decision-making. This culture is central to data maturity. Leaders create an environment that celebrates learning from experimentation, treats data as a shared resource, and supports risk-taking when it’s informed by evidence. Encouraging cross-functional collaboration around experiments helps scale learnings across product lines.
• Address HIPPO dynamics with governance and education. To reduce the influence of opinions over data, implement decision governance that makes decisions transparent and data-driven. Invest in training programs to raise data literacy and enable more teams to participate in evidence-based decision-making. Clear roles, documented decision criteria, and accessible data can shift organizational behavior toward greater objectivity.
• Focus on AI scalability with practical efficiency. As AI plays a larger role in analytics and product experiences, prioritize efficient architectures, model selection, and cost controls. Implement caching, model optimization, and retrieval strategies to balance performance with resource constraints. Maintain governance and explainability to ensure AI-generated insights remain trustworthy and actionable.
• Close the tools and training gaps for lagging teams. Expand access to essential analytics tools, including session replay and friction-identification capabilities, and provide formal training. Align tooling investments with product analytics needs and ensure that teams can use these resources to drive improvements in the user journey and business outcomes.

This roadmap presents a practical path for enterprises seeking to raise their data maturity and product analytics capabilities. It emphasizes a composite approach that combines technology investments, governance, culture, and leadership commitment. Organizations that implement these steps thoughtfully can accelerate the translation of data into meaningful product improvements, better customer experiences, and stronger financial performance.

Conclusion

The IDC white paper, conducted with support from Heap Analytics, offers a compelling view of how data maturity and product analytics drive digital experiences and business outcomes. The central message is that maturity is a holistic capability encompassing technology, governance, culture, and organizational practices. Leaders—those who achieve higher data maturity—demonstrate substantially better business results, including pronounced revenue improvements, greater efficiency, higher NPS scores, and enhanced lifetime value. They also exhibit a more nuanced understanding of the customer journey, a stronger emphasis on automation and governance, faster access to insights, and a culture that celebrates learning from experimentation.

However, the findings also reveal persistent gaps that can limit progress. Many organizations still rely on manual processes for data validation and governance, lack access to critical analytics tools, and struggle with decision-making processes that favor opinions over data. The HIPPO dynamic remains a barrier to data-driven decisions, and lagging organizations frequently report training and tooling deficiencies that hinder progress toward data maturity. Nonetheless, these insights point to clear levers for improvement: invest in instrumentation and data quality, expand tool access and formal training, foster a data-driven culture, and implement governance that anchors decisions in evidence.

For enterprises, the takeaway is actionable and strategy-driven. Elevating data maturity requires a deliberate, cross-functional effort that aligns product strategy with data capabilities, accelerates automation, and nurtures a culture of experimentation. By following the four-maturity framework—lagging, progressing, advancing, and leaders—organizations can chart a credible path toward deeper analytics maturity and stronger outcomes. As teams increasingly treat data as a strategic asset, they can deliver more delightful digital experiences, achieve better business performance, and sustain competitive advantage in a data-driven era. The journey from lagging to leaders is continuous, but with intentional steps and disciplined execution, enterprises can realize the full value of data maturity and product analytics in the modern marketplace.