By Muhammed Mafawalla • Published 2 June 2025 • 10 min Read

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Beyond the Comfort Zone of Excel

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Every business leader understands the power of Excel for tracking performance and analysing trends. But what if your spreadsheets could predict what will happen next quarter, identify which customers are likely to churn, or determine which operational changes will yield the highest returns?

This is the promise of data science. At Turning Point Advisory, we have guided high-growth companies from reactive reporting to proactive, data-driven decision making that drives measurable business outcomes.

This article maps the four-stage journey from spreadsheet analytics to advanced data science, providing executives with a practical roadmap for building competitive data capabilities.

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The Four Levels of Data Analytics

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Think of these as evolutionary stages, each building upon the previous level:

- Level 1: Descriptive Analytics - What happened? Using historical data to understand past performance.

- Level 2: Diagnostic Analytics - Why did it happen? Understanding the drivers behind business outcomes.

- Level 3: Predictive Analytics - What will happen? Using statistical models to forecast future outcomes.

- Level 4: Prescriptive Analytics - What should we do about it? Advanced analytics that recommends optimal actions.

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Level 1: Descriptive Analytics - The Foundation

What It Includes:

• Monthly sales reports and dashboards

• Customer segmentation analysis

• Operational metrics tracking

• Financial reporting and variance analysis

Business Value: Creates transparency, enables accountability, and provides the foundation for higher-level analytics.

Tools: Reporting platforms such as Excel, Power BI, Tableau

Investment: Highest ROI with lowest risk. Most organisations achieve significant value by consolidating data sources and automating manual reporting.

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Level 2: Diagnostic Analytics - Understanding the "Why"

What It Includes:

• Correlation analysis between variables

• Cohort analysis for customer behaviour

• Root cause analysis for operational issues

• Market basket analysis for cross-selling

Business Value: Move from reactive to responsive decision-making by understanding what drives your results.

Example: A retail company discovers that customers acquired through digital channels have 40% lower retention rates, revealing onboarding issues in their digital strategy.

Requirements: Statistical analysis capabilities, data integration, advanced analytical skills.

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Level 3: Predictive Analytics - Seeing Around Corners

What It Includes:

• Customer lifetime value modelling

• Demand forecasting and inventory optimisation

• Credit risk assessment and fraud detection

• Sales pipeline forecasting and lead scoring

Business Value: Transform from reactive to proactive, anticipating challenges and opportunities before competitors.

Example: A software company predicts customer churn with 85% accuracy, enabling proactive retention efforts that improve customer lifetime value by 23%.

Investment: Significant investment in data infrastructure and analytical talent, but typically delivers 3-5x ROI within 18-24 months.

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Level 4: Prescriptive Analytics - Automated Intelligence

What It Includes:

• Dynamic pricing optimisation

• Resource allocation optimisation

• Supply chain optimisation with real-time adjustments

• Personalised marketing with automated testing

Business Value: Operate with near-optimal efficiency, creating substantial competitive advantages.

Example: An e-commerce company's automated inventory system reduces carrying costs by 15% while improving product availability by 12%.

Investment: Highest investment level but delivers transformational outcomes. Requires significant organisational change management.

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Your Strategic Roadmap

Assess Your Current State

Consider your:

• Data infrastructure integration

• Existing analytical capabilities

• Decision-making processes

• Current technology investment

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Four-Phase Progression Strategy

Phase 1: Foundation (Months 1-6)

• Consolidate data sources and improve quality

• Implement robust dashboards

• Build organisational data literacy

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Phase 2: Enhancement (Months 6-18)

• Develop diagnostic analytics capabilities

• Invest in statistical analysis tools

• Build cross-functional analytical teams

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Phase 3: Prediction (Months 12-24)

• Implement predictive models for high-impact use cases

• Develop machine learning capabilities

• Create model governance processes

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Phase 4: Optimisation (Months 18-36)

• Deploy prescriptive analytics for complex decisions

• Automate analytical processes

• Integrate analytics into operational workflows

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Common Pitfalls to Avoid

- The "Boiling the Ocean" Trap: Jumping to advanced analytics without foundational capabilities. Solution: Follow the progression pathway systematically.

- The "Tool-First" Approach: Investing in technology without clear business objectives. Solution: Start with business problems, not tools.

- The "Data Perfection" Paralysis: Waiting for perfect data before starting. Solution: Begin with available data and improve iteratively.

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Conclusion: Your Competitive Advantage Awaits

By understanding the four levels of analytical maturity and planning thoughtful progression, organisations can build data science capabilities that deliver measurable value at each stage.

At Turning Point Advisory, we specialise in guiding high-growth companies through this journey. Our approach combines technical expertise with strategic insight, ensuring your data science investments deliver faster, smarter, and more scalable business transformation.

The question isn't whether your organisation will need advanced analytical capabilities—it's how quickly you can develop them. The companies that begin this journey today will define tomorrow's competitive landscape.

Contact us to discuss how we can help your organisation develop the analytical capabilities that will drive your next phase of growth.