The Analytical Maturity Curve: Navigating the Path to Increasing Business Value

Organizations striving for data-driven decision-making often follow a journey along an analytical maturity curve. This maturity curve represents the stages through which businesses progress as they leverage data to generate increasing levels of value. Each stage reflects a greater degree of sophistication, moving from basic descriptive analysis to advanced predictive and prescriptive analytics.

Understanding each stage of the curve allows organizations to assess their current capabilities, prioritize investments, and strategically advance toward more impactful insights. Let’s explore the stages of the analytical maturity curve, what they entail, and the value they provide.

Descriptive Analytics: Understanding What Happened

At the descriptive stage, organizations focus on analyzing historical data to understand past events. All businesses begin in this phase. Businesses first start with raw data. Next they typically spend a great deal of time endeavoring to clean this data. Finally, businesses typically build reporting systems to summarize this data . The descriptive analytics stage of the maturity journey serve as the foundation of all later analytics. These steps seek to answer questions such as:

• What happened? What’s happening?
• How many customers purchased a product?
• What was the revenue last quarter?

Characteristics:

• Data Sources: Structured data from spreadsheets, databases, or basic reporting systems.
• Tools: BI dashboards, Excel, or SQL-based reports.
• Key Activities: Data aggregation, summarization, and visualization.
• Output: Static reports, charts, and dashboards that provide clarity on past performance.

Business Value:

Descriptive analytics delivers insights to monitor performance, identify trends, and measure outcomes. However, its value is limited to retrospective analysis—it explains what happened but not why.

Diagnostic Analytics: Explaining Why Something Happened

Building on descriptive analytics, diagnostic analytics dives deeper to uncover the root connections and relationships between variables associated with past events. It answers questions such as:

• Why did sales drop last quarter?
• Which factors contributed to an increase in customer churn?

Characteristics:

• Data Sources: Structured and semi-structured data; often involves drilling into more granular data points.
• Tools: Statistical and machine learning analysis software.
• Key Activities: Correlation analysis, segmentation, and regression analysis.
• Output: Insights explaining relationships between variables and identifying underlying relationships.

Business Value:

Diagnostic analytics empowers organizations to understand the reasons behind key trends, enabling informed decision-making. By explaining why events occurred, businesses can begin to identify opportunities for improvement.

Predictive Analytics: Forecasting What Will Happen

In the predictive stage, organizations leverage statistical models and machine learning techniques to forecast future outcomes based on historical patterns. Predictive analytics answers questions such as:

• What is the likelihood of a customer churning?
• How will sales trend next quarter?
• Which customers are most likely to purchase a new product?

Characteristics:

• Data Sources: Structured data is used for this analysis, which can itself be the end product of the analyses of semi-structured and unstructured data.
• Tools: Python, R, machine learning libraries (e.g., Scikit-learn, TensorFlow), and specialized software.
• Key Activities: Data Prep and Sampling, Model Building, Testing, and Validation.
• Output: Forecasts, probabilities, and risk scores.

Business Value:

Predictive analytics enables organizations to anticipate trends, risks, and opportunities. By leveraging historical data to predict outcomes, businesses can proactively address issues, optimize strategies, and make forward-looking decisions.

Prescriptive Analytics: Determining How To Optimize What Happens

At the pinnacle of the maturity curve is prescriptive analytics. This stage focuses on providing actionable recommendations and optimizing decision-making. It answers questions such as:

• What is the best course of action?
• How can we allocate resources to maximize efficiency?
• Which strategy will yield the highest ROI?

Characteristics:

• Data Sources: All available data types, often in real-time.
• Tools: Optimization algorithms, AI models, simulation tools, and decision support systems.
• Key Activities: Scenario analysis, optimization modeling, and AI-driven decision-making.
• Output: Actionable insights, recommended strategies, and automated decision workflows.

Business Value:

Prescriptive analytics delivers the highest level of value by not only predicting future outcomes but also recommending actions to achieve desired results. This empowers businesses to optimize resources, improve efficiency, and drive strategic initiatives with confidence.

Moving Up the Analytical Maturity Curve

Progressing along the maturity curve requires investments in technology, data infrastructure, and skills. Key enablers include:

1. Data Management: Reliable data sources, integration across systems, and strong governance.
2. Technology Stack: Tools and platforms for visualization, machine learning, and decision support.
3. Talent: Skilled analysts, data scientists, and decision-makers to interpret and act on insights.
4. Culture: A data-driven mindset where decisions are grounded in analytics.

Organizations must align their analytical ambitions with their resources and capabilities. Not every organization needs to reach prescriptive analytics immediately, but incremental progress ensures increasing business value over time.

Where Do LLMs Fit into the Analytical Maturity Curve?

GenerativeAI constructs like Large Language Models (LLMs), are transformative tools that primarily fall into the Descriptive and Diagnostic stages of the maturity curve. LLMs themselves are constructed through the analysis of vast amounts of unstructured and structured data and are a distillation of the relationships that exist within this data (Diagnostic analytics). Moreover, leveraging this distillation, they can be used to identify relationships, summarize information, and extract insights in new data fed to them. For example:

• Descriptive: GenerativeAI can be used to help clean data. It can also be used with various degrees of success to generate summarizing reports and charts based on data inputs.
• Diagnostic: GenerativeAI can also be used to identify basic trends and provide explanations of input data, based on comparisons to the large corpus of historical text data used to train the LLM or imported into the LLM.

While LLMs are not inherently Predictive or Prescriptive, nor can they reliably innately perform these more advance analytical functions, the value that they can provide in the Descriptive and Diagnostics phases of the maturity model can still be significant although often indirect; i.e. enabling the delivery of value by other systems and analytical functions. Regardless, the reason why so many organizations have struggled to leverage GenAI to deliver significant measurable business value is directly related to where they fall on this maturity / business value curve. They’re simply not currently able to reliably tackle the tougher business analytics challenges (pricing, optimization, forecasting, risk mgmt, etc.) that deliver higher returns once solved.

The reason why so many organizations have struggled to leverage GenAI to deliver significant measurable business value is directly related to where they are positioned on this maturity / business value curve. They’re simply not currently able to reliably tackle the tougher business analytics challenges that deliver higher returns once solved. - VentureArmor AI

It is worth noting, however that despite the fact that LLMs reside and operate in these lower maturity / business value nodes, they can and often do also act as catalysts for higher-level analytics. For instance:

• In Predictive analytics, LLMs can support model prototyping by helping analysts generate code or refine models.
• In Prescriptive analytics, LLMs can assist in scenario planning by simulating outcomes based on qualitative inputs.

As one moves up the maturity and business value curve, strong machine learning tools and analytical frameworks are needed to tackle the often extremely complex analytical challenges that exist in the Predictive and Prescriptive analytical domains. Please note that here, we categorize various kinds of traditional statistical analyses as being in the Machine Learning (ML) domain. These same ML tools can also be applied lower in the curve as well, in the Descriptive and Diagnostic domains to clean data, and summarize it within BI tools (eg: descriptive statistics), and analyze relationships (eg: correlation analyses).

Long-Term Outlook For GenAI – Agentic AI:

While LLMs themselves do not directly have the capabilities needed to perform Predictive and Prescriptive analyses, they will at some point in the near future be able to reliably call and manage the specialized machine learning tools needed to perform these kinds of more advanced analytics. In doing so, they will be able to deliver massive value at scale across the full range of analytics maturity nodes, including Prescriptive analytics. As of the time of this writing in December 2024 we’re not there yet, however.

This situation would be analogous to owning a humanoid robot that itself is incapable of cutting grass because it lacks a built-in grass trimming blade. However, if it were able to access a lawn mower, and could competently use it, not only could it mow lawns, but it could potentially mow lawns perfectly 24/7. With Agentic AI (i.e. relating to AI “Agents”), this is a possibility, and the implications are massive.

This touches in a small way on the very broad topic of Agentic AI, which are systems designed to autonomously pursue complex goals and workflows with limited direct human supervision. These AI constructs exhibit autonomous decision-making, planning, and adaptive execution using a variety of tools to complete multi-step processes. More on this topic in a later article.

Conclusion

The analytical maturity curve represents the evolution from understanding past performance to optimizing future decisions. Each stage—descriptive, diagnostic, predictive, and prescriptive—builds on the last, delivering greater value as sophistication increases. While LLMs are powerful tools for analyzing relationships within language and text, advancing to predictive and prescriptive analytics requires (for now) structured data, advanced tools, and a skilled workforce. By strategically progressing along this curve, organizations unlock the full potential of their data to drive impactful decisions and achieve sustainable growth.

About VentureArmor

At VentureArmor, we specialize in helping businesses unlock the power of AI to drive operational excellence and customer satisfaction. Our expertise in AI analytics and data-driven solutions enables us to deliver tailored solutions that meet the unique needs of our clients. Contact us to learn more about how we can help your organization achieve its goals through the strategic application of AI.