Embedding analytics to drive adoption and maturity

In today’s data-driven landscape, every organisation progresses along a data escalator: A maturity model that spans from basic reporting to AI-driven, optimised decision-making. But advancing along this path isn’t just about having more data—it’s about embedding the right insights into the right moments.

Traditional dashboards worked well for descriptive and diagnostic analytics, but they’re no longer enough. The next evolution lies in embedded analytics powered by artificial intelligence (AI), bringing intelligent insights directly into users’ workflows to drive real-time action, faster decisions, and broader adoption.

Understanding the data escalator

The data escalator is a framework that outlines how organisations transform their relationship with data—starting with understanding what happened and progressing to AI and automation that determines the next best action.

Each level builds on the previous one, increasing in complexity, value, and impact.

1. Descriptive analytics: What happened?

This is the foundation of data maturity. Descriptive analytics analyses historical data to summarise past events and performance. It helps answer questions such as:

• What were our total sales last quarter?
• Which region had the highest return rate?
• How many customer support tickets were logged last week?

At this stage, organisations rely on dashboards, basic reports, and visualisations to monitor key performance indicators (KPIs) and trends. It’s essential for operational awareness but offers limited insight into causes or future outcomes.

2. Diagnostic analytics: Why did it happen?

Diagnostic analytics examines the root causes behind trends or anomalies revealed in descriptive analytics.

This level is about contextualising the data and answering:

• Why did sales drop in Q2?
• Was it due to a specific product line, market condition, or inventory issue?
• Did an increase in return rates correlate with a product defect or supplier issue?

It involves data correlation, drill-downs, and segmentation to connect symptoms to sources. This step transforms data into actionable insight.

3. Predictive analytics: What might happen?

Predictive analytics leverages historical patterns, statistical models, and machine learning to forecast likely future outcomes. Rather than simply reacting to events, businesses begin anticipating challenges and opportunities before they unfold, enabling them to answer questions such as:

• Will we meet our revenue goals next quarter?
• Which customers are likely to churn?
• When is this machine most likely to fail based on past performance?

This level helps organisations shift from reactive to proactive—identifying trends before they fully materialise.

4. Prescriptive analytics: What should we do?

Prescriptive analytics delivers the highest value by not only forecasting outcomes but recommending optimal actions to influence them. It combines AI-driven insights, business rules, and simulation techniques to:

• Suggest the best inventory reorder strategy to avoid stockouts
• Optimise delivery routes based on weather and traffic conditions
• Personalise promotions for customers based on behaviour and preferences

At this level, insights are integrated into systems in real time, automating decision-making and guiding human action toward desired outcomes. This represents the pinnacle of the data escalator and enables continuous optimisation.

Why this progression matters

Each step of the escalator delivers greater business value, but success requires stronger data governance and integration, a culture of data literacy and trust, and tools that make analytics accessible to all users.

Embedded analytics and generative AI can significantly accelerate this journey by lowering barriers to insight, integrating analytics into daily workflows, and reducing reliance on specialised data teams.

The challenge of adoption

Despite data’s transformative potential, user adoption remains a major hurdle.

Common issues include:

• Workflow disruption: Dashboards are often siloed from daily tools, requiring users to switch context
• Technical barriers: Users may lack skills to explore dashboards or create reports independently
• Dependence on analysts and IT: Data requests get backlogged, delaying insights and increasing operational costs
• Decision fatigue: With limited time and context, employees default to intuition rather than evidence-based decision making

These obstacles keep teams stuck in reactive, manual decision-making, preventing stall progress on the data escalator.

How embedded analytics bridges the gap

Embedded analytics integrates insights directly into the applications people already use—such as an enterprise resource planning (ERP), customer relationship management (CRM), or human resource (HR) systems—enabling decision-making in context.

Key benefits:

• Contextual intelligence: Insights appear precisely where work happens, eliminating the need to navigate between systems
• Faster, informed action: Teams act in real time with relevant metrics at their fingertips
• Lower support burden: Business users get answers themselves, reducing demand on IT and analysts
• Wider adoption: Familiar, non-technical interfaces increase engagement across departments

For example, with Infor’s embedded analytics widget, users can access real-time insights directly within their ERP screen. The system responds to business context—like the customer, product, or transaction in view—and delivers related data automatically, without switching tools or building reports.

How GenAI supercharges embedded analytics

Generative AI (GenAI) and machine learning integration pushes analytics even further:

• Natural language queries: Users can simply ask, “Why are freight costs rising this month?” and receive instant summaries, visualisations, and root cause analysis.
• Automated trend detection: AI can reveal anomalies or outliers like unexpected delays or revenue shifts that dashboards might miss.
• Predictive and prescriptive recommendations: AI anticipates outcomes and suggests what actions to take, helping users make proactive, optimised decisions.

Looking ahead: Analytics as a native experience

The future of analytics isn’t about creating more dashboards—it’s about making analytics invisible yet powerful, embedded into the day-to-day experience of work. By aligning data strategy with embedded delivery models, organisations can accelerate progress along the data escalator, increase adoption across technical and non-technical roles, and transform insights into intelligent action.

Explore Infor's cloud analytics solutions: https://www.infor.com/platform/data-insights/birst-analytics