In today’s data-driven landscape, organizations are increasingly recognizing the transformative power of analytics. A prime example is Netflix, whose meteoric rise is often attributed to its robust analytics strategy. In their seminal work, Competing on Analytics, Thomas Davenport and Jeanne Harris introduce the DELTA model—a framework that has become a cornerstone for assessing and advancing analytics maturity across industries. Over time, this model has evolved into the DELTA Plus model, incorporating additional elements to address the complexities of modern analytics.
What Is the DELTA Plus Model?
The DELTA Plus model provides a comprehensive blueprint for organizations aiming to enhance their analytics capabilities. It comprises seven critical elements that collectively drive an organization’s journey toward analytics maturity:
- Data
The foundation of any analytics initiative is high-quality, accessible, and integrated data. Organizations must transition from siloed, inconsistent data sources to a centralized, standardized approach, ensuring data governance and eliminating redundancies. - Enterprise
Analytics should be an enterprise-wide endeavor. This involves breaking down data silos and fostering a culture where analytics is embedded in decision-making processes across all departments. A unified strategy and roadmap are essential for aligning analytics efforts with organizational goals. - Leadership
Effective leadership is pivotal in cultivating an analytics-driven culture. Leaders must not only advocate for analytics but also demonstrate commitment through actions. Traits of analytics-savvy leaders include promoting data literacy, encouraging experimentation, and setting clear performance expectations. - Targets
Analytics initiatives should be aligned with strategic business objectives. Rather than attempting to analyze every facet of the organization, it’s crucial to focus on high-impact areas where analytics can drive significant value. - Analysts
A diverse team of analysts is essential. This includes not only data scientists and engineers but also domain experts who can interpret data within the context of business operations. Organizations should cultivate a range of analytical skills to address various challenges effectively. - Technology
The rapid evolution of analytics technologies necessitates continuous investment in infrastructure and tools. Organizations must adopt scalable solutions that support advanced analytics techniques, including artificial intelligence and machine learning, to stay competitive. - Analytics Techniques
Advancements in analytics techniques—from descriptive to predictive and prescriptive analytics—enable organizations to derive deeper insights and make proactive decisions. Leveraging these techniques requires a blend of technical expertise and business acumen.
Understanding Analytics Maturity Stages
Organizations progress through five distinct stages of analytics maturity:
- Analytically Impaired
At this stage, decisions are predominantly intuition-based, with little to no use of analytics. - Localized Analytics
Analytics efforts are isolated within specific departments, lacking coordination and integration across the organization. - Analytical Aspirations
There’s recognition of the value of analytics, but efforts are often fragmented and lack a cohesive strategy. - Analytical Companies
Analytics is more systematically integrated, with established processes and tools, though challenges in full integration may persist. - Analytical Competitors
Analytics is deeply embedded in the organization’s DNA, driving strategic decisions and providing a competitive edge.
Advancing through these stages requires deliberate effort across all seven DELTA Plus elements, ensuring that data and analytics become integral to the organization’s operations and culture.
Moving Forward with Analytics Maturity
Achieving analytics maturity is not a one-time effort but an ongoing journey. Organizations must continuously assess and refine their strategies, invest in talent development, and stay abreast of technological advancements. By embracing the DELTA Plus model, businesses can systematically enhance their analytics capabilities, leading to more informed decision-making and sustained competitive advantage.
For organizations looking to assess their current analytics maturity and chart a path forward, tools like the Data Maturity Scan offer valuable insights and actionable recommendations. Engaging with such assessments can provide a clear roadmap for leveraging analytics to its fullest potential.
Our previous post introduced you to the 7 elements of the DELTA Plus model for analytics. This model, introduced by Thomas Davenport and Jeanne Harris in their influential book Competing on Analytics, has become a guiding framework for organizations navigating their analytics journey. The book not only details these foundational elements but also outlines the five stages of analytics maturity—a structured path that helps organizations assess where they currently stand and what it takes to advance to the next level.
Now that you’re familiar with the core pillars of the DELTA Plus model, it’s time to explore the analytics maturity curve. This post will help you evaluate how well each of those DELTA elements is working within your organization—and identify the missing or underdeveloped links that may be holding you back.
What Are the 5 Stages of Analytics Maturity?
Analytics maturity refers to how effectively an organization leverages data for decision-making, strategy, and innovation. According to Davenport and Harris, organizations typically fall into one of the following five stages:
Stage 1: Analytically Impaired
Organizations at this stage operate with minimal, if any, use of analytics. They may still rely heavily on paper-based processes, lack ERP systems, or if they have one, fail to use the data it collects in a meaningful way. Data exists in silos—often unmanaged, inconsistent, or simply ignored.
Decisions in analytically impaired organizations are often based on gut instinct rather than data. There’s little leadership support for data-driven culture, and employees aren’t encouraged—or even enabled—to leverage data in day-to-day decision-making. Progress in this stage is sporadic and often fueled by chance rather than insight.
Stage 2: Localized Analytics
Here, we start to see the use of basic analytics—but often only within isolated departments. These pockets of analytics activity are typically limited to standard reports or spreadsheet-based insights.
However, data remains fragmented. Each department may define and interpret metrics differently, leading to what’s often called “multiple versions of the truth.” This lack of coordination can result in conflicting goals across the organization and missed opportunities to derive strategic value from data.
Moreover, decision-making is still primarily intuition-driven, with analytics being used more to validate choices than to guide them.
Stage 3: Analytical Aspirations
Organizations in this stage are no longer dabbling—they recognize the power of analytics and are actively working to build capabilities.
Data silos begin to break down, and unified repositories start taking shape. There’s increasing leadership involvement, and analytics begins to play a more central role in decision-making—especially in select areas like marketing, operations, or finance.
These organizations often experiment with predictive analytics and start investing in analytics talent, infrastructure, and tools. However, they may still lack an enterprise-wide vision and are typically focused on departmental or short-term goals.
Their ambition? To discover and leverage analytics as a competitive differentiator.
Stage 4: Analytical Companies
These are organizations where analytics is deeply embedded in operations and decision-making. Data strategies are well-aligned with broader business goals, and leadership consistently champions a data-driven culture.
There is a centralized (or hybrid) data infrastructure, offering a “single source of truth” across departments. Advanced tools—such as machine learning platforms and IoT-driven data pipelines—are in place, supporting more refined analytics practices like prescriptive analytics and scenario modeling.
Teams are cross-functional, and there’s a strong emphasis on collaboration between domain experts and data professionals. Still, some organizations in this stage may struggle with innovation fatigue or a narrow focus on a limited set of analytics goals.
Stage 5: Analytical Competitors
This is the pinnacle of analytics maturity. Organizations in this stage use analytics not just to support the business—but to lead it.
Analytics is a strategic asset, built into every function—from supply chain optimization to customer experience, product development, and beyond. These companies rely on real-time, autonomous analytics powered by AI and machine learning, often designing their own proprietary algorithms.
They seamlessly analyze structured and unstructured data, including text, images, videos, and social media streams. Analytics is no longer a support function—it’s a competitive advantage.
Teams here are composed of data scientists, engineers, domain specialists, and citizen data professionals, all working in sync under a governance framework that ensures quality, compliance, and innovation.
Are Analytics Maturity and the DELTA Plus Model Applicable Across Industries?
Absolutely. The DELTA Plus model and the five stages of analytics maturity apply to any industry where data can drive value—which today means almost every industry.
As highlighted in Competing on Analytics, organizations like Netflix (streaming), Caesars Entertainment (gaming), Nike (consumer goods), and Visa (financial services) exemplify high analytics maturity.
Even traditional sectors like manufacturing and construction are seeing transformation. CEMEX, a global cement manufacturer, uses analytics to optimize supply chain operations and delivery routes.
And while the authors didn’t initially anticipate the fashion industry to be data-forward, it too has adopted predictive analytics to influence design, inventory, and marketing strategies. The model’s versatility is clear: whether it’s healthcare, finance, logistics, or entertainment, the DELTA framework applies.
Where Does Your Organization Stand?
If analytics hasn’t yet become a core strength—or better yet, a profit center—it’s time to reflect.
Developing analytics maturity isn’t just about deploying new tools. It’s about building a culture that embraces data, democratizing access, and nurturing skills across roles.
Whether you’re laying the foundation or preparing for that final leap into advanced AI-driven analytics, understanding your current stage is key. From there, you can identify what needs to evolve—whether it’s leadership commitment, tech investments, or cross-team collaboration.
Need a Partner on Your Analytics Journey?
At Logesys, we’ve spent over a decade working with organizations across industries to strengthen their analytics frameworks—from foundational strategies to high-end implementations.
If you’re ready to explore your maturity stage and discover the next step in your analytics evolution, let’s talk. We’re here to support your journey.
Note: Examples referenced in this blog are from the book Competing on Analytics by Thomas H. Davenport and Jeanne G. Harris.