Make decisions with the industry’s highest data quality

Besides ensuring reliability and trust, high-quality procurement data can:

• increase savings. On average, Sievo customers get $20M incremental savings per $1B spend.

• improve efficiency and productivity. Sievo customers achieve 90% time savings in data preparation and analysis.

• eliminate risks. Sievo customers can identify, prevent, and mitigate internal and external risks that impact Procurement and Supply Chain processes.

Yes. Many Sievo customers initially believed they needed to complete a large-scale master data cleansing project before investing in analytics, or assumed they lacked sufficient data points for meaningful analysis.

This assumption is common because many analytics providers (full suites, best-of-breed tools, and data visualization platforms) require companies to deliver high-quality data themselves.

What sets Sievo apart: Sievo specializes in transforming raw data into the industry's highest-quality procurement data, regardless of original quality or complexity. Sievo identifies which data types are needed and which data points to combine and enrich.

Independent validation: Spend Matters has awarded Sievo multiple "Best-in-Class" ratings for Data Management.

Sievo combines end-to-end data accountability with full customer transparency and control.

Common limitations of other providers:

• Classification services operate as a "black box"

• Can create dependencies on consultants

• Inability to improve classifications means future spend follows incorrect mapping rules, resulting in inaccurate analytics

Sievo's approach:

• End-to-end accountability from data extraction through visualization and ongoing refresh

• Fully transparent processes and tools

• Customers can see how classification and supplier normalization decisions are made

• Users can suggest changes directly in the solution

General-purpose AI lacks both the specialized capabilities and training data required for procurement data management.

Core challenge: For AI to perform well, it needs appropriate capabilities plus training on high-quality, domain-specific data. Procurement data is highly complex, making it difficult for generic AI to develop effective cleansing and categorization models.

Critical capabilities most in-house AI initiatives lack:

• Processing large-scale datasets

• Handling fragmented and inconsistent data from diverse sources

• Interpreting unstructured and semi-structured data

• Incorporating constantly changing market indicators

• Leveraging peer procurement data for validation

Sievo's AI models deliver 3x more accuracy than other models on the market due to three factors:

1. Scale of data processed: Sievo processes, cleanses, and manages 2%+ of global GDP annually, continuously improving capabilities at a scale companies cannot replicate internally

2. Customer feedback integration: Unlike AI built for generalized outputs, Sievo's models learn from end-user feedback across the customer base, with users maintaining control over outputs and contributing expertise that improves the model

3. Combined AI and domain expertise: Sievo employs both an AI-dedicated team and data cleansing and categorization specialists with 20+ years of experience who apply best practices and oversee the entire process

Sievo’s community data is equivalent to 2%+ of global GDP annually, comprising 11+ billion rows of transactions, 100+ million mapped suppliers, and 9.5+ million material codes that power and continuously improve our systems. Cross-customer learning improves accuracy, efficiency, and continuous improvement, such as:

• Cross-customer learnings: The power of cross-customer learning is that it can fill the gaps in unclear or missing data. For example, based on recurring abbreviated supplier names across several customer data sets, we know that supplier “Moft Licss“ actually means “Microsoft Licenses“- and we know where to classify this as well.

• High-confidence supplier mapping decisions: To determine the correct supplier-parenting mapping for new vendors, accuracy increases when the supplier has multiple validated decisions from other customers.

• Proactive updates based on cross-customer feedback: Sievo incorporates validated feedback from individual customers and across customers, where it makes sense (e.g., supplier normalization). This ensures agile, proactive, and up-to-date changes across all customers.

Four factors make internal data management challenging for enterprises:

• Data complexity: siloed, messy, and inconsistent data make it hard for the organization to get a single source of truth

• Manual effort & errors: The paradox of data cleansing is that many tools rely heavily on manual data entry, and the lack of automation requires significant manual labor to correct errors and normalize data, yet this manual effort also makes the data prone to manual errors.

• Difficult to achieve and maintain accuracy: besides the FTE-heavy processes to get to a certain coverage of categorization, it’s even more challenging to reclassify spend, which leads to continuous data accuracy degradation

• Lack of scalability: In-house solutions and the development of specialized internal data capabilities can be very expensive and difficult to maintain or update, making it nearly impossible to scale with large volumes of spend data.

Recommended frequency: Weekly or monthly (or ad-hoc based on specific needs). Less frequent than monthly is not advised.

Why ongoing refresh matters: Although some providers offer data cleansing and categorization as a one-time project, it should be performed on a continuous, refreshable basis to ensure up-to-date data for analysis.

Why daily refresh is not recommended: Procurement analytics should align with financial reporting cycles, which are typically weekly or monthly in enterprises.