In today’s competitive business landscape, CFOs face mounting pressure to maximize operational value and achieve a higher RoI. Meticulous planning of various expenses through strategic spend management plays a vital role in driving long-term growth and sustainability. Data analytics is crucial in this realm as it transforms spend-related data into valuable cost-saving opportunities by analysing transaction data and spending patterns.
Data serves as the bedrock of spend analytics, providing the foundation for deep insights into spending patterns, supplier relationships, and overall expenditure. Quality data is essential for accurate spend categorization, trend analysis, and performance measurement. It allows organizations to monitor progress, identify improvement opportunities, and make strategic adjustments with confidence.
CFOs are increasingly recognizing the importance of data analytics in spend management. In a 2022 report titled “Big Data and Business Analytics Market Statistics – 2030”, Allied Market Research predicts that the global big data and business analytics market size will reach $684.12 billion by 2030.
Data analytics and spend management in the procurement process
Gartner, a renowned expert in technology, market research, and strategic consulting, highlights that spend management is primarily associated with the procure-to-pay process, which includes strategic activities like sourcing, supplier management, and contract maintenance. According to the Chartered Institute of Procurement & Supply, companies often allocate more than 70% of their revenue to procurement, making it a powerful lever for cost optimization. Even a modest reduction in procurement spending can result in significant bottom-line benefits.
With this realisation, Procurement, Finance, and Accounts Payable (AP) departments are facing increasing pressure to enhance efficiency, streamline processes, and effectively manage both direct and indirect spend. According to Deloitte’s 2020 CPO Flash Survey, cost management has become a top priority for procurement and AP leaders, receiving nearly eight times more attention in day-to-day operations.
Data analytics in procurement optimizes costs, covering analysis, demand predictions, and supplier management. Digital tools and automation have revolutionised procurement analytics and enabled organisations to handle large data volumes, automate processing, and gain real-time insights for effective spend management. AP automation software have significantly enhanced the speed, accuracy, and scalability of data analytics for spend management, enabling CFOs to make data-driven decisions more efficiently and effectively.
Digital spend analysis solutions have evolved through four generations. In the initial generation (1990-2000), the analysis relied on Microsoft Excel and other spreadsheets and focused on past spend patterns. The second generation (2000-2010) introduced dedicated desktop spend analysis software with on-premises data hosting. The third generation (2010-2015) brought browser-based spend analytics dashboards with improved usability and visualizations, available as licensed software or Software-as-a-Service (SaaS). In the current fourth generation (2015-today), AI-powered, automated procurement analytics solutions leverage advanced technologies and analyse data from multiple sources. These solutions are securely hosted on the cloud and delivered as SaaS, providing flexibility and scalability.
Essential activities of data analytics in spend management
Two kinds of analysis form the core of data analytics when used for spend management
- Analysis of Past Activities: The analysis of historical procurement data helps gain meaningful insights that drive informed decision-making. By carefully studying spending patterns, organizations can identify areas of inefficiency, wasteful expenditure, and potential cost savings. This process involves segmenting spending data by suppliers and categories to gain a deeper understanding of where resources are allocated and where optimization opportunities lie. Additionally, benchmarking prices across different suppliers provides organizations with valuable insights into market competitiveness and enables them to optimize contract utilization for maximum value.
- Predictive Analytics: Predictive analytics in procurement leverages historical data to anticipate future trends and outcomes. It includes price and demand forecasting and risk analysis. Price forecasting helps in monitoring price fluctuations for commodities and goods, negotiating better pricing agreements, and optimizing cost savings. Demand forecasting aligns procurement strategies with anticipated future needs by analysing historical data to anticipate changes in demand patterns. Risk analysis identifies and mitigates potential disruptions in the supply chain by assessing and predicting risks. Proactively implementing risk mitigation strategies optimizes supplier relationships and ensures business continuity.
Roles of data analytics in procurement management
Historically, data analytics in the procure-to-pay process primarily focused on understanding past spend and supplier performance. However, there has been a shift towards automated and prescriptive decision-making, driving the evolution of spend analysis solutions to meet the digital transformation needs of the procurement process. Data Analytics extends its benefits across various procurement functions, contributing to effective spend management and driving positive financial outcomes:
Category Management:
- Prioritizing suppliers based on their cost-effectiveness.
- Addressing supply risks to minimize potential disruptions and resulting financial losses.
- Fostering supplier relationships to negotiate better terms, pricing, and discounts.
Strategic Sourcing:
- Determining optimal timing for sourcing to secure the best prices and terms.
- Supplier selection based on data-driven evaluations of their cost, quality, and reliability.
- Evaluating supplier quality and risk positions to minimize financial risks.
Contract Management:
- Alerting when contracts require renegotiation to optimize contract terms and costs.
- Identifying maverick spend
- Enhancing contract coverage by analysing spend patterns and ensuring comprehensive contract alignment.
Source-to-Pay (S2P) Process:
- Measuring and optimizing purchase order cycles to reduce costs.
- Optimizing payment terms to maximize cash flow and enhance working capital management.
- Improving payment accuracy to minimize errors and discrepancies that can impact spend.
- Identifying rebate opportunities to unlock additional cost savings.
- Reducing fraud through data analysis and anomaly detection to safeguard spend resources.
Sustainability and Corporate Social Responsibility (CSR):
- Assessing environmental and social impacts to align procurement practices with sustainable objectives.
- Managing supply chain and procurement-related risks to mitigate potential financial and reputational risks.
Risk Management:
- Unravelling relationships between supply, price, environment, CSR initiatives, and risks to identify potential areas of risk exposure.
- Highlighting opportunities for risk mitigation to ensure spend resources are protected.
Performance Measurement:
- Tracking savings realized for profit and loss (P&L) reporting to demonstrate the impact of spend management initiatives on the organization’s financial performance.
The path to data analytics-based spend management
Implementing a data-driven analytical approach for spend management presents its unique set of obstacles. Interestingly, the most significant challenge does not stem from technology itself, but rather from the resistance to change exhibited by individuals and organizations. The presence of cultural dynamics, such as pandemics, recessions, and the growing emphasis on self-service, further compounds the difficulty of transitioning to a data-driven approach.
The structural challenge posed by the exponential growth of data is a formidable one. A considerable portion of this data, approximately 80%, is unstructured, posing challenges in terms of capturing and quantifying it effectively. It is crucial for companies to recognize that data flows across traditional boundaries, often lacking clear ownership or control. As a result, managing this fluidity becomes even more complex as organizations strive to consistently extract value from their data.
Without the appropriate data extraction tools and skilled data scientists, the data analytical approach to spend management can be daunting. Hiring a chief data officer (CDO) to handle these responsibilities may not always be feasible. It’s crucial for organizations to find practical solutions and leverage available resources to navigate the complexities of data management and drive data-driven decision-making.
Fostering collaboration between data scientists and finance leaders is vital. Breaking down silos and merging domain knowledge with technical expertise allows organizations to leverage the full potential of analytics and drive innovation. Establishing consistent data standards and programming languages across the organization eliminates inefficiencies caused by variations and enables the seamless sharing of ideas and knowledge.
Empowering employees with the necessary tools and skills to analyse data independently is essential. By automating tasks, providing access to relevant information, and offering specialized analytical training when needed, organizations enhance employee satisfaction and effectiveness, creating a positive data-driven work environment. Encouraging teams to articulate and document the reasoning behind analytical approaches and considering alternatives and trade-offs fosters a deeper understanding, drives innovation, and improves decision-making.
Senior executives such as CFOs should set the expectation that decisions must be data-driven and lead by example. When top managers actively engage in evidence-based actions, such as relying on data to make product launch decisions or thoroughly reviewing proposals, it influences the entire company and drives significant shifts in company-wide norms.
Takeaway
In a 2010 study called “Analytics: The New Path to Value,” conducted by MIT Sloan Management Review and IBM Institute for Business Value, it was found that high-performing organizations were five times more likely to leverage analytics compared to their low-performing counterparts. This highlights the significance of analytics in achieving success in data-driven spend management. To accomplish this, organizations need to undergo a cultural shift that involves integrating data analytics seamlessly into all functional units, establishing effective communication channels, and recognizing the transformative power of analytics. By encouraging unrestricted data flow and integrating data analysis as a standard business practice, organizations can transform their information operations and drive enhanced outcomes in spend management.
