What does this module / this process contain:
Collecting the data: Identification of available data sources and extraction of the data. This sounds simple, but it is sometimes quite complex, depending on which data sources are available and what export options these data sources have. The data is usually exported monthly. The goal is a fully automated data export, but there can also be partial manual data export, which you then try to automate with RPA (Robot Process Automation).
Data cleansing: Cleansing is about detecting inaccuracies and removing corrupt records and redundancies from a set of data. This includes finding and eliminating errors and discrepancies in descriptions and transactions to ensure accuracy. Through data cleansing, you can identify which contacts in your database are incomplete or irrelevant. Typos are removed and missing codes are validated and corrected for up-to-date information.
Data enrichment: This refers to the process of enhancing, refining, and improving raw spend data. It also includes standardizing the spend data for easy viewing. Enriching the spend data makes sure that all the header and line-level names and details are accurate and to a specific naming standard. Data is often missing specific fields, and misspellings and abbreviations are common — as are incorrectly coded fields.
Data classification and consolidation: Classification typically involves grouping several suppliers of the same parent company or organization. Siemens, for example, has a large number of subsidiaries that can have completely different names. On the other hand, not all companies that have Siemens in their name are the same company, even if they have the same address and the same domain name. This has to be differentiated and assigned correctly. At the same time, you can also categorize the data into meaningful groups (like marketing, office supplies, software) to identify how and where the business is spending its money. Unifying heterogeneous spend data into clearly defined categories makes spend easier to address and manage across the whole organization. Classification is about harmonizing all purchasing transactions to a single taxonomy, enabling procurement to gain visibility of the global spending to make better sourcing decisions.
Spend analyses: The last step is to identify opportunities for savings and other procurement improvements. Analysis can be geared towards investigating all sorts of business problems, such as ensuring that you have negotiated the best contract deals per supplier, or confirming buyers are purchasing from preferred suppliers. With this, you can identify opportunities for reducing the number of suppliers per category and negotiating better rates. The best probable method for cost savings can only be realized after the confirmed estimates have been calculated properly. Spend analysis itself is again divided into different areas: tail spend analyses, supplier spend analyses, category spend analyses, item spend analyses, payment term analyses and contract spend analyses. In addition, some tools have capabilities to analyse category performance, supplier performance, process performance, price performance, currency performance and more.
What are the main goals:
- Identification of expenses so you know where to start improving.
- Automatic correction of incorrect data and automatic enrichment of the data so that you can recognize the real spend, even if invoices were assigned incorrectly.
- Visualization of the data in a way that makes it easy to identify the right actions.
- Cost reduction by bundling spend, understanding price developments, identifying the best negotiation period and better preparation of price negotiations.
- Control of the improvement measures so that you can see which measures made sense and which did not (as lessons learned and for further improvements).
Specifics per industry:
Manufacturing: for manufacturing companies, the production material is the largest block of expenditure. Since production companies always have an ERP system such as SAP, which automatically determines the requirements and also generates orders, most of the requirements are already correctly assigned to the correct product group and the correct cost type.
For the mid-market, specialised providers such as tacto offer spend analytics as part of a procurement platform that needs little IT effort and works with the data quality typical of mid-sized manufacturers.
At manufacturing companies, the focus is not on the analysis of expenses but rather on the analysis of the material price development, the payment conditions, the granting of discounts or the delivery performance. Not every software vendor can provide additional information beyond the pure spend.
Latest innovations:
Classification: the assignment of spend to the right product group based on machine learning is an essential point. Since in the past you had to manually define a rule for each new invoice, modern systems can now automatically allocate over 98% of the spend. This not only enables faster analyses but also 90% lower costs for data cleansing. In addition, the data quality is increased because modern systems can also identify outliers using pattern recognition.
Recommendations: the automatic generation of recommendations as to which measures would be possible and most efficient based on the analyzed data is the fine art of spend analytics.
Generative AI and agents: since 2024, spend analytics tools have added natural-language interfaces. Instead of building a report, a category manager asks "Which suppliers in packaging increased prices by more than 5% this year?" and gets the answer with the underlying invoices. The next step, now offered by several vendors, are AI agents that monitor spend continuously and open a sourcing event or a supplier review on their own when a threshold is crossed. In April 2026, SpendHQ acquired Sligo AI to add exactly this kind of agentic layer.
Integration: the automatic adoption of measures, e.g. in an action management tool, but also the automatic update of measures in relation to the improvement achieved are not easy, but more and more tool providers are already creating an automatic feedback loop here that allows conclusions to be drawn as to how well and efficiently measures have been implemented.
Extensions and additional functions:
Savings tracking: it is developed to plan, execute and track every cost-saving initiative across various projects in an organization. Savings tracking software helps users plan and roll out discrete savings projects and entire cost-reduction programs. The software defines the scope and helps establish savings targets for every strategic sourcing program. It also helps in capturing results and measuring savings performance.
Most procurement teams use "Source-to-Pay" or finance software to configure, manage, and track custom savings. More advanced software allows an organization to track recognized and unrecognized savings and repayment agreements according to company policies. This provides transparency and accuracy in monitoring and reporting savings.