The Role of Big Data in Expense Management

What big data means, and why data literacy matters

Big data, a term first coined in the 1980s, but it took decades before its impact was truly felt. With cloud computing, artificial intelligence (AI), and machine learning (ML) becoming popular, organisations are using a lot of data.

This helps them find important insights, especially in expense management.

Our partner CWT mentions in their blog: "Demystifying Data: Emerging tech to empower data literacy for business users" that there is a rising problem regarding many people lacking skills in analytics and understanding of data.

"Data literacy - the ability to read, work with, analyse and communicate data effectively, understanding quality, sources and analytical methods in context. Without this foundational competency, companies risk misinterpreting data, diminishing the potential benefit of expense analytics."

-CWT, "Demystifying Data: Emerging tech to empower data literacy for business users," August 20,2024. CWT.

Evolution of big data analytics

Due to rapid data growth, companies shifted from traditional data warehouses to modern frameworks capable of real‑time stream processing. AI and ML now enhance the extraction of insights with speed and precision.

Big data in expense management

Analytics tools now dive into detailed spending patterns - transaction records, receipts, employee reports, vendor invoices - to spot cost-saving opportunities and inefficiencies.

They also find unusual activities, like fraud, by comparing current expenses to past trends.

They automate approvals to make workflows easier, allowing finance teams to focus on important tasks.

Addressing the challenges

• Data quality and integrity

Combining data from diverse sources (e.g., CRM, social media) requires robust ETL processes and data governance to ensure consistency, accuracy and reliability.

• Privacy and security

Teams must protect sensitive intellectual property, financial and customer data using access controls, endpoint security and real‑time monitoring.

• Change management

Transitioning smoothly requires stakeholder buy‑in. Starting with pilot projects helps build trust and demonstrates value, gradually ushering in a data-driven culture.

Enhancing data literacy

To extract maximum value from analytics, organisations must make big data approachable for all users, not just specialist analysts.

According to the CWT blog post, a robust reporting ecosystem relies on three pillars:

• Comprehensive, accurate and timely data
• Relevant, clear and actionable content
• The right tools for customisation and collaboration

For non‑technical business users, tools must provide short summaries, reasons behind patterns, clear recommendations, and forecasts, without requiring deep BI tool expertise.

Future trends in big data analytics 

1. Generative AI in BI tools

AI-driven assistants can now query data using natural language ("What’s our spend this year?"), fetch the correct metrics, visualise results, and continuously improve with feedback, reducing reliance on analytics experts.

2. AI highlights and natural‑language recommendations

Tools can automatically generate executive summaries, explain trends, correlations and outliers in plain English, making insights digestible for end users.

3. Automated analysis

BI platforms are now capable of running change‑detection, outlier and correlation analyses on demand, empowering users to proactively identify anomalies and performance drivers.

4. Predictive analytics

Combining internal data with external indicators (e.g., market trends, weather) helps forecast future spending, enabling proactive budgeting and policy decisions.

CWT’s work shows how predictive models can parse hundreds of indicators to predict travel programme costs.

5. 'What‑if' and prescriptive scenarios

Configurable dashboards now allow users to create hypothetical scenarios. For example, they can ask, “What if mileage rate increased by 5%?”

This helps to measure impacts like cost savings or carbon reduction. These insights support better policy decisions.

Conclusion

Big data, AI and ML are transforming expense management, making it smarter, more automated and insightful. But the true game-changer is data literacy. The ability for all business users (not just analysts) to understand, interpret and leverage data.

By integrating intuitive analytics tools, AI-generated insights, predictive models, and what‑if scenario planning, organisations can empower their teams to make better decisions.

"Success depends on accurate data, meaningful content and accessible tools, making advanced analytics an inclusive experience."

-CWT, "Demystifying Data: Emerging tech to empower data literacy for business users," August 20,2024. CWT.