Spend Analytics Solutions

What are Spend Analytics solutions? 

Spend analytics solutions provide a single view into all historic, current and planned spending to give the perspective needed to improve that spending through better sourcing, compliance and demand management. It is important to understand that most solutions have some degree of analytics, but spend analytics solutions offer much deeper insights into spend than those typically baked into other coverage areas. In other words, any supplier spending can be examined by a spend analytics solution, from direct materials to indirect materials and services spend (for information on Travel and Expense spending, visit our T&E Guide). 

Many spend analytics solutions also offer data management capabilities. These capabilities can range from simple tasks, such as removing duplicates, to more complex cleansing abilities, such as enriching data. 

In addition to tracking a company’s spend, spend analytics solutions generate business-relevant insights by leveraging historic and predictive datasets across the enterprise. Often, these insights are referred to as ‘actionable insights’ or ‘opportunity analysis,’ and they can help reduce costs (input costs and unnecessary consumption), mitigate risk/non-compliance (internal and supplier) and design better processes and policies/practices. Essentially, spend analytics solutions should not only track spend but also provide some guidance on how spending practices can be improved.

How Spend Analytics solutions factor into the S2P process

Spend analytics occupies an odd place in the source-to-pay (S2P) process in that it really runs in the background of all process steps. This is because spend analytics solutions need to collect data from each S2P step, such as CLM, Sourcing and Invoicing, and deliver insights for decision support at every stage. 

Upstream procurement, or source-to-contract (S2C), is all about finding a supplier, negotiating terms and maintaining that relationship over time. Downstream procurement, procure-to-pay (P2P), is about actually ordering items and the various steps that happen once a supplier is found and negotiated with. Suites typically cover all of these steps, while point solutions/best-of-breed solutions cover only one or a few, such as sourcing and contracting. 

Most solutions in upstream and downstream procurement offer some degree of spend analytics within them: CLM solutions may cover how much spend is on contract, invoice-to-pay (I2P) solutions may cover how much spend has yet to be paid and supplier management solutions may offer analytics, such as which supplier you spend the most money with. 

These spend analytics features embedded within solutions made for another purpose are helpful, but they all share one major flaw: they lack an overarching view of an organization’s entire spend. This lack of holistic visibility makes issues, such as maverick buying, extremely hard to detect. If someone wanted to view all of their spend and had just the aforementioned solutions, they would be left to piece together data from various sources themselves, which leaves them with outdated and inevitably incomplete spend data. 

Why Spend Analytics solutions are important

Small companies often don’t analyze their spending. That’s not to say that they don’t monitor it or that their C-suite, controllers or accountants aren’t recording every dollar the organization spends. However, for small companies, spend analysis is often not a high priority or is done on an ad-hoc basis when budgeting for the following year; it is done in Excel spreadsheets and via emails between appropriate parties. 

This makes it very easy for issues to arise: maverick spend falls under the radar, subscriptions are renewed without proper consideration, suppliers are kept because they are familiar and not because they are cost effective — the list could go on. 

In these cases, the issues are often unknown or just not prioritized because, again, a small company only has so much bandwidth. But these same practices can be found as the company grows, is acquired or ages enough that historic spend data is lost. There will always be a point where the amount of spend data available is impossible to track in Excel sheets and emails and the data updates so quickly that a human can never keep it current and accurate (so, latency becomes an issue due to the ‘offline’ process offering no real-time view). 

This is where spend analytics providers come in (and, ideally, why a spend analytics tool would be in place before the data gets out of hand). 

Spend analytics technology provides an overarching view of an organization’s spend. It does so by pulling data from systems via interfaces from within the S2P application, ERP or financial system or via uploads. With system-to-system connections, spend analytics technology can refresh spend on a daily, weekly or monthly basis with no input from the user. 

This means that with a spend analytics tool, spend data can be analyzed in near real-time regardless of how large the amount, compared against historical data and/or external data (benchmarks, market, etc.) and have insights automatically derived from it. All-in-all, it is a much more accurate, effective and up-to-date way to track spend than via spreadsheets or many disconnected systems. 

How do I know my organization is ready for a Spend Analytics solution?

Any organization with spend data can use a spend analysis tool — there’s no minimum threshold. That said, small, young organizations likely don’t have the need for one. 

Some signs that it’s time to step in with a spend analytics solution are:

1. Your spend data is growing too quickly for a human to easily track.
2. Your spend data is split into multiple geographic areas or currencies.
3. You go from tracking spend in one system to another (e.g.., Excel to finance software) or one format to another.
4. You go through a merger or acquisition of any size.
5. You don’t have a data analyst on staff who can track and model data for savings opportunities.
6. You have upcoming contract renewals and need spend insights for negotiation power.
7. You use a BI tool independently but can’t model all of the spend in it easily due to either complexity or breadth of spend.
8. You need large bits of spend reclassified or modeled; i.e., you need to adopt a new or more complex taxonomy.
9. You need category-, industry- or material-specific spend insights and data modeling.
10. You need to tie your spend to other internal and external factors, such as risk, ESG, contract analytics, supplier risk and supplier performance via ‘mashups.’

If any of the above describes your organization, it’s time to begin looking for a spend analytics solution. 

Some factors to consider when beginning the journey are:

• Cost: While more complex needs likely means more complex costs, most solutions offer a ‘basic’ package for organizations that are starting out, and several target SME-sized organizations. These vendors offer spend analytics at a lower price point than those selling complex, enterprise-geared solutions.
• Data preparation: Either independently or through a partner, many spend analytics providers offer data management services in some capacity. This includes during implementation, during which experts will help your organization gather and aggregate all historical spend data and prepare it for loading into the tool. That said, your organization has to have the time and resources to dedicate to starting this journey. 
• Reasons for failure: Most spend analytics adoptions fail due to a lack of prep work on the front end. Take the time to get buy-in from key stakeholders in your organization, train extensively in the tool and truly prepare your data (see the previous point).

How Spend Analytics is done: Common features of Spend Analytics technology

Spend analytics solutions are capable of all of the aforementioned analytics as well as overarching features, such as spend by geography or category (e.g., direct materials or HR). Additionally, they can identify important risk- or ESG-related spending to identify correlations and opportunities, such as spend at risk, using the application for supplier performance management, etc. 

All spend analytics technology offers some elements of data management. Common features related to generic data management include extract, transform, load (ETL) capabilities that cleanse and normalize the spend data before analyzing it. This can include features such as normalizing all spend to one currency, filling in missing supplier data and connecting suppliers to one another and removing duplicate data. It is important to note that many spend analytics solutions can normalize data from a variety of sources, which can be especially helpful when dealing with mergers and acquisitions, system changes or spend that spans many geographies or business units. 

Once the data is loaded and transformed, the spend analytics solution matches it to a data taxonomy based on pre-set rules or AI. Most spend analytics solutions offer some pre-built taxonomies as well as the ability to use a customer-specific taxonomy. Then, the solution derives insights from it. This includes visual elements, such as dashboards, views and workspaces, that give an overarching view of all spend in a current or historic time period. Typically, these dashboards can be filtered to view a specific geography, supplier, PO and contract, but they can also be viewed holistically. Popular BI tools are often used to power this section, such as Tableau, Power Bi and Qlik Sense. 

Additionally, most solutions offer reports, which may simply be a download/email of an aforementioned dashboard or may be a more focused report, such as KPI tracking, compliance, etc.

A dashboard of your organization’s spend is useful on its own, of course, but spend analytics providers typically offer at least a few more advanced features for additional added value. This may include industry benchmarking (how does your spend in a particular area compare to peers?), what-if analysis (if I drop a current supplier, what happens?) or trend analysis (how has my spend changed over time?) to name a few.

Spend Analytics use cases

Spend analytics solutions can have varying use cases, but commonly they are made to:

• ETL data: ‘Extract, transform, load’ is the process of getting data from the source system (i.e., the client), transforming it to match the desired structure and loading it into the storage system (in this case, the spend analytics technology).
• Clean and enrich data: Cleanse and enrich (i.e., fill in missing data) data in cases where it is not possible to do in the source systems so the user can feel confident that the analyzed data is complete and accurate. Some solutions do this via rules-based algorithms while others use AI/ML. 
• Categorize data: Solutions place data within appropriate parent and sub-categories by building out a data taxonomy. Taxonomies can be pictured as tree branches with each being a different data ‘category’ of grouped data. In spend analytics, data is most often grouped by categories (e.g., marketing, direct materials, etc.), organization or geography with more granular sub-categories available, e.g., the ‘direct materials’ category could be broken down into different sub-categories like forgings, stampings and fasteners.
• Model data: This typically includes visuals and graphics, either in the form of static reports or dynamic dashboards. Some solutions offer this in house while others use a third-party BI tool.
• Provide insights: Solutions build sophisticated analyses on cost drivers, demand, contracts, working capital, risk, supply chain and overall procurement performance while maintaining data that is flexible and rich. Some solutions also provide guidance on how to realize certain insights, such as potential savings. 

Do I need a service-based or do-it-yourself solution?

One important distinction to note when choosing a spend analytics vendor is that some are service-based, others are do-it-yourself and a very small subset offer a mix of both. 

A service-based spend analytics provider will do everything for you: load and transform the data, set-up the taxonomy, edit the dashboard, provide insights via reporting or consulting, etc. This is a great option for individuals who are not very tech-savvy or do not have the time to dedicate to in-depth analytics. However, these solutions typically lack an element of configuration and control. 

Conversely, a do-it-yourself provider allows users to build their own spend cube, create custom taxonomies, create custom filters and dashboards, do advanced analytics like what-if analysis themselves and so on. While these platforms may be low- or no-code, the price of such control over your spend is some level of required technical knowhow and a greater time commitment. 

There is no right or wrong solution method, but you must understand the difference so you can identify what works best for your organization.

How technology supports Spend Analytics — Top 5 capabilities

These ‘Top 5’ critical digital capabilities stem from the Spend Matters TechMatch workbench — derived from a larger number of requirements scored in the SolutionMap solution benchmark.

The Top 5 capabilities are the highest-weighted critical capabilities that are central to the displayed solution market benchmark. They have been developed by the Spend Matters analyst team and refined by procurement users in tech-selection projects using our market-proven SolutionMap benchmarking dataset and associated TechMatch decision-making tool.

Top 5 Spend Analytics tech capabilities

1. Data modeling

The ability to build more sophisticated analyses on cost drivers, demand, contracts, working capital, risk, supply chain and overall procurement performance while maintaining data that is flexible and rich enough to fully support this type of spend analytics.

The average vendor can use its analytic data schema to go beyond reporting of transactional purchasing and invoicing data to provide deeper quantitative insights on the data, including performance modeling (e.g., normalizing/enhancing spend data for benchmarking), causal factor analysis (i.e., what/who is driving the spending by how much), trending/time-phased analysis and drill downs through master data taxonomies (e.g., category and supplier taxonomies). Additionally, the schema should support extensible data models (e.g., adding lookup tables) and multi-schema support (e.g., each business unit can tailor the models differently on initial implementation).

The top vendors differentiate themselves by extending the analytics schema to also support predictive data analysis, AI/ML models or real-time visibility across many systems. Additionally, they offer multiple predefined data models to choose from, e.g., different industry/category models as a base from which to tailor. Some can use metadata for enhanced real-time observability for the same or similar records updated across multiple systems, which helps display spend data in real-time. Others use AI/ML data models to generate predictive and even prescriptive analytics to provide broader and deeper insights for opportunities to pursue. Some of these high performers also support more advanced ‘outside-in’ analytics models that integrate external data, such as commodity indices, risk data and ESG/CSR data feeds.

The end result for buyers is not just a timely, accurate ledger of purchasing transactions but deeper insights into spend categories, demand/cost drivers, spending processes/controls and top opportunities for improving spend/supply performance.

2. Data pipeline

The ability to automatically extract, transform and load spend data, supply data and related master data into a platform’s aggregated global analytics system from numerous data sources while maximizing data quality and cleanliness.

This capability focuses primarily on enhanced ETL for spend data, including accessing data sources, detecting data types, performing data validation and basic transformation to prepare and pipe the data into targeted analytic data models for further data enhancement (e.g., auto-classification of spend to a category taxonomy) and end user analysis. It includes some master data management (MDM), cleansing/enrichment and integration capabilities, but we formally evaluate related platform capabilities, such as management of metadata, authorizations, etc., elsewhere in our knowledge base.

The average vendor in this area should be able to ingest data of different formats from different data sources (e.g., flat file feeds and/or APIs from ERP/S2P suites, PO/AP modules, CSV files and p-card feeds) and remember those formats for future use. Basic currency and date formats should be auto-recognized and mapped to target formats. The vendor system should also offer basic MDM capabilities that includes cleansing, standardizing, matching, indexing, enriching and harmonizing source data piped to the centralized analytic data store/schema The vendor should be able to guide the processes through context-specific, rule-based workflows and integration tools/platforms.

Top performers offer more advanced MDM capabilities, such as advanced harmonization through pre-built models, best practices and application of AI/ML. In some top-performing systems, users are presented with an easy matching UI when loading data sets that enables them to quickly generate new formats, match up old fields with new fields and identify additional columns to be automatically added. Additionally, top performers provide knowledge management via AL/ML or advanced algorithms. Some top performers can even emulate broader MDM functionality by synchronizing master data to other third-party software (e.g., contract management or supplier management systems) which helps customers without pre-existing full-blown MDM applications. Lastly, others support a democratized self-service approach that supports user-driven templates, data mapping and interacting with the data directly or via a spreadsheet-based interface like MS Excel.

3. Data cleansing and enrichment

The ability to cleanse and enrich data in cases where it is not possible to do so in the source systems so the user can feel confident that the analyzed data is complete and accurate.

The average vendor should provide multi-taxonomy support with taxonomies that can be user defined and cross-linked across the underlying schema. For cleansing, the average vendor should be able to use complex regular-expression-based rule support for identifying complex errors with unusual patterns and cross-field verification requirements across multiple types of identifiers. The system should also be able to group/family data into core taxonomies/hierarchies without losing the original entities. Data should be automatically validated against vendor-cultivated databases, all integrated data sources, all necessary alerts generated and any changes logged for audit purposes. Numeric and measurement data should be automatically subjected to statistical quality checks to verify likelihood of correctness.

Top vendors differentiate themselves by doing the above and allowing additional flexibility across taxonomies, such as supporting standardized ones like UN/SPSC concurrently with internal proprietary taxonomies or allowing users to pick whether they follow best practices or apply their own rules. For data cleansing, top vendors have hybrid or full-fledged AI capabilities that can evolve rules based on changing errors, learned pattern similarity and manual overrides to reduce user frustration while training the AI and improving data quality. Additionally, data validation for top vendors can include supplier-data specific capabilities that validate/cross-reference to aggregated/curated ‘outside-in’ supplier data feeds, e.g., for risk and compliance.

Some top performing vendors can also apply their own knowledge bases through interactive supplier repositories and AI/ML techniques. These techniques can categorize incoming data based on similarities to already captured data, further cutting down the amount of time a buyer needs to spend on data management. Finally, some vendors have expanded their data cleansing and enrichment to improve data fidelity and quality beyond spend/supplier data into product/part data (which ties to information like inventory, assets and revenues/profits), contract data to improve visibility into compliance and future spending, supplier systems, worker data, IT-related data (e.g., SaaS licenses), supply chain planning systems and other areas. This is often done on a services basis and/or with partners, but it will eventually migrate internally into native solution capabilities.

4. Spend cube analysis

The ability to allow procurement power users and business users to tailor spend insights to their unique needs with core analytics for spend cubes, e.g., ole-based, category tuned, drillable and filters.

The average vendor should support native cross tabs/pivot tables, including Excel 2-D cross-tabs/pivot tables and some level of 3-D cross-tabs/pivot tables. For cubes, the average vendor should support multiple cubes on standard spend data, with both auto-derived and user-derived dimensions. Users should be able to derive their own measures, filters and views that they can apply to dashboards or individual views. 

Top vendors differentiate themselves by supporting these cross tabs/pivot tables and k-d (‘k-dimensional’) cross-tabs/pivot tables with the ability to fix points for real-time drill down. Beyond just a standard spend cube, top vendors have workspaces that contain multiple cubes on multiple datasets that can be linked across derived dimensions to allow ‘drill around’ spend analysis. Users should also be able to derive and even share their own advanced measures, filters and views, such as cross-transaction derivation/linkages to enable tailoring and re-use of the analytics by end-to-end process, use case, role, KPI, etc. 

Some vendors even offer highly customizable cross tabs/pivot tables, such as those that allow individual data points to be fixed and filtered around or those with the capability to link and filter across multiple cross tabs and in-cell visualizations. Essentially, these vendors offer the user multiple ways to build cross tabs/pivot tables. Looking beyond the ability to link cubes across spend, supplies, ESG and risk factors in a single workspace, which allows for analysis across all dimensions and negates the need to work across multiple reports, some vendors allow the analytics to dynamically link to external data (e.g., benchmarks and commodity indices) and internal data (e.g., KPI targets) to create enhanced spend dashboards that can be saved/shared. 

5. Advanced analytics

The ability to conduct predictive analytics (e.g., predictive cost/spend models) and prescriptive analytics (giving recommendations on opportunities/risk) — including using AI/ML technology — so that organizations can stop being reactive and begin seizing potential spend improvement opportunities proactively.

This capability focuses on using higher-impact analytics to create business value by analyzing spend data more broadly, deeply and proactively rather than just summarizing historical transaction data in the hope that users will know what to do with it. The system should provide predictive analytics based on augmented models, such as time-phased demand/supply volume models for direct spend, with machine learning for parameter adjustment, e.g., predicting future spend based on internal/external model inputs. 

Additionally, users should have some control over the predictive analytics, such as changing thresholds or inserting calculations so that the prediction/prescription models can be validated, tuned and maintained. Essentially, the solution should not be a ‘black box.’ Similarly, what-if analysis should be available. These are built-in trend analyses for spend over time by supplier, commodity, geography, etc. that can generate comparative ‘what-if’ reports that cover hypotheticals in which purchases increase/decrease by a user-defined percentage or range. Some vendors use predictive algorithms to identify the relevance, urgency and potential impact of outliers relative to upcoming purchases and sourcing events, allowing for immediate action. Some vendors allow the user to select the trend data they want to use in their dashboards, rather than simply spitting out a trend on the backend. This allows for more targeted, relevant trends for each customer. Other vendors also meld their risk tolerance analysis with predictive capabilities to test different business scenarios.

Another type of analysis capability focuses on clustering and outlier detection. The average vendor should provide core support for advanced analytics, such as outlier identification via standard statistical and clustering algorithms that can flag not just data quality issues but also potential fraud, errors or operational events. Top vendors use advanced ML algorithms that adapt to changing data streams and patterns to detect outliers that standard algorithms would miss. For top vendors, the hybrid/AI system automatically defines various time windows for rolling integrity analysis. Beyond detecting trends of interest, the system may also detect potential issues by using external benchmarks and anonymized performance across the provider’s client base by using anonymized community intelligence. 

Top vendors support predictive analytics with neural networks and related AI models for advanced prediction on real-time data and/or shallow learning and similar next-gen techniques to detect temporary/rapid changes. They also allow for more control over the predictive analytics, such as the ability to train/insert new AI/ML models, create variants of embedded algorithms or build new algorithms inside the tool from scratch. Top vendors also provide what-if analysis and advanced multi-factor models to generate ranges and banded trend charts for specific scenarios that can be integrated with other third-party data science tools.

Why selecting Spend Analytics technology can be difficult

You have to weigh many factors when choosing any technological product. Here are some of the most important factors you should consider when starting your spend analytics selection:

1. Crowded solution field:
   There are a lot of providers (see Discover Spend Analytics Vendors) section below that all offer slightly different capabilities, various levels of services (see Do I need a service-based or do-it-yourself solution?) and different levels of analytical depth. It’s important to find a solution that has both robust data modeling capabilities and KPIs relevant to your business. 
2. Interoperability:
   You need a tool that can aggregate data from your various active or legacy systems and the formats in those systems, transform that data and load it in a uniform and easy-to-understand manner.
3. Understandable output:
   Processing data is great, but you need to be able to review the results in a way that makes sense for your organization. It’s important to ensure available reports are user friendly, relevant to your business, and that they provide new, useful insights for your team. Some solutions even provide actionable insights or provide a service to help you find the most value within insights. 
4. Inflated expectations:
   SA tools, even those that use AI to speed up the process, are not magic. There will always be some level of labor on the part of the customer to gather and prepare data, especially if it has been left to grow and rot unencumbered for many years. 
5. Time constraints:
   Selecting the appropriate tool will take some work and can be a very lengthy process. Different providers specialize in different industries and types of spend (direct vs. indirect), and so, they offer different functionalities. It is important to have an internal point of contact who is well-versed in what the business needs when it comes to analysis and is willing to put the time and effort into selecting the right tool. This means asking detailed questions and demoing a variety of products before selecting one.

How Spend Matters can help you select Spend Analytics technology

Spend Matters specializes in procurement technology diligence. In addition to projects and advisory, Spend Matters offers Insider, the only membership community and technology comparison tool of its kind: access to Spend Matters SolutionMap vendor rankings dataset combined with independent, zero pay-to-play, brutally honest coverage of solution providers, market developments and trends affecting procurement, finance and supply chain. 

We can help you find a solution that can:

• Aggregate spend data from many systems/time periods and report on trends/changes.
• Customize all aspects (KPIs, reporting, data modeling, data classification, etc.) to your business needs.
• Run predictive analytics (based on AI/ML) based on your spend data to help you plan for the future, including risk mitigation capabilities.

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Discover Spend Analytics vendors

These are the vendors we are covering today (or very soon). Visit their vendor directory pages on Spend Matters for a quick vendor overview, demographic information and relevant articles, including vendor analyses.

Solution Provider	What it does
Anaplan	Anaplan’s platform can be configured to support procurement, finance and supply chain operations equally. In addition to spend analytics, it has MDM capabilities and offers features focused on procurement planning and modeling.
AnyData Solutions	AnyData is a true ‘analytics’ platform with native, integrated visualization technology and real-time updates. It began as a spend analysis tool focused on data-driven savings and has since evolved to include contract management and supplier relationship management.
Arkestro	Arkestro, formerly Bid Ops, positions itself as ‘predictive procurement orchestration.’ Essentially, it uses AI/ML and data science to learn from customer’s spending behavior and provides a service-based approach to cleansing, modeling and tracking data.
Corcentric	Corcentric is an S2P suite provider. It offers spend analytics, but not as a separate product (i.e., it is rolled into the platform holistically). Corcentric comes with built-in dashboards and a report generator to build reports, including over 50 out-of-the-box KPIs and reports.
Coupa	Coupa is an S2P suite provider with a global footprint. Spend analytics is not a standalone module, but it is rolled into the platform as a whole. Coupa offers full MDM capabilities and strong opportunity analysis alongside typical spend tracking and visualization capabilities.
Covalyze	Covalyze isn’t quite traditional spend analytics; it actually offers parts-specific spend analytics. In other words, it targets direct spend cost optimization through target price calculators, similar parts analysis and direct materials savings analysis.
Creactives	Creactives is an Italian spend analytics provider. In addition to offering common SA features such as data cleansing and analysis, it also specializes in materials MDM. In other words, it is built for automated parts and material classification.
ebidtopay	ebidtopay is an S2P suite provider and quite unique as it is family-owned and operated. For spend analytics, ebidtopay partners with Qlik for many advanced capabilities, but It natively covers data cleansing, configurable spend visualizations, spend dashboarding and KPI tracking.
Efficio	Efficio is a consultancy that built out a custom procurement solution, eFlow, in 2016. Specific to analytics, it offers spend reporting, dashboarding and category-specific insights. Potential savings estimates and opportunities in the dashboard are pre-vetted by Efficio consultants.
GEP	GEP is an S2P suite provider with a global footprint. It is well known for having a very solid technology offering and robust data model. It embeds various analytics and expert insights throughout its suite and uses AI (including GenAI) for many data management and analytics processes.
Ignite Procurement	Ignite offers spend and supplier analytics and focuses on the Nordic/Western European regions. It operates a drag-and-drop model interface for data management, provides 25 predefined dashboards and tracks carbon accounting data.
ISPnext	ISPnext is an S2P suite provider. It focuses on its local market: the Netherlands, Luxembourg and Belgium. It began as an AP automation provider, but now it offers a full suite. In terms of analytics, it offers configurable persona-based spend dashboards.
Ivalua	Ivalua is an S2P suite provider with a global footprint. Its solution is built on a single tech-stack and data model. It offers a spend workbench for data cleansing, classification and categorization, 75+ reports out-of-the-box and custom report-building.
ivoflow	ivoflow is a direct spend management solution provider that specializes in serving manufacturers. Ivoflow supports quote and cost-break-down comparisons, including landed-costs-assessment and negotiation guidelines. It also provides data analytics, dashboards, KPIs, category recommendations and supplier recommendations.
Jaggaer	Jaggaer, formerly SciQuest, is a S2P suite provider with a global footprint. It offers a data warehouse for users to manipulate any data as they wish. The solution offers a spend management and classification engine that feeds into reporting and allows for comparative product analysis.
LevaData	LevaData is technically a direct sourcing solution, but it offers direct materials spend insights and focuses on cost optimization. It monitors over 40,000 suppliers/manufacturers and has data on over 1 billion parts, so it can track granular pricing trends and cost-savings opportunities.
Mercanis	Mercanis is an S2C suite that includes a spend analysis module. Its spend analysis module is based on AP data, to which it applies algorithms, data enrichment and cleansing rules to analyze the data and generate insights.
MeRLIN	MeRLIN is a mid-market suite. Its analytics dashboards are persona-based, and it offers a drag-and-drop dashboard builder.
Mithra-AI	Mithra is an AI-enabled spend analytics solution. It focuses on being user friendly, and it offers features such as a taxonomy builder and category-specific actionable insights.
OneMarket	OneMarket was born from a BPO provider and offers service-enabled S2P technology. OneMarket Insights is its spend analytics arm, which consolidates ERP, expense and P-card/T-card data and enriches it with supplemental data (including diversity, risk and sustainability information) before categorizing and publishing it through an online presentation capability.
Onventis	Onventis is an S2P suite provider. It acquired Spendency, a standalone spend analytics provider, in 2021. Since then, Onventis analytics has been built out as a strong self-service offering. It largely caters to the mid-market in Europe.
Oracle	Oracle Procurement offers a procurement analytics capability that uses AI. It classifies data, visualizes it and provides actionable insights. It also offers some aspects of supplier performance management within its spend analytics module.
PRGX Global	PRGX is a recovery audit firm that also offers S2P analytics. This includes features such as merchandise and M&A analytics as well as spend and payment analytics. The module itself also focuses on factors such as billing compliance, contract compliance and supplier audit compliance.
PRO(a)ACT	PRO(a)ACT is a Swedish consulting and purchase analysis technology company. It uses AI to cleanse data, enriches it with third-party data and then visualizes it.
Proactis	Proactis is an S2P suite provider. Its spend analytics capability is split into two solutions: Proactis Rego and Proactis Spend Intelligence. Proactis Rego is for spend management. It integrates with ERP and financial systems to pull in data and aggregate it into a holistic dashboard view. Proactis Spend Intelligence is for spend reporting and further dashboarding.
Raindrop	Raindrop is an S2P suite provider. It is designed for small to medium-size enterprises. Its spend intelligence module focuses on visualizing data and allows the user to download any information they would like to.
Robobai	Robobai is an S2C provider. It offers spend visibility that uses AI to cleanse data and allows the user to analyze spend by supplier, region and country. It also offers payments analytics for optimizing treasury data.
Rosslyn	Rosslyn is a best-of-breed spend analytics provider. It covers a wide variety of use cases, including data enrichment, data visualizations, spend insights and some aspects of supplier information management and contract management. Users can query all data elements within the platform.
SAP Ariba	SAP is an S2P suite provider with a global footprint. Ariba is the company’s S2P platform. Its spend analysis lies in its spend control tower, which runs across the entire platform. Its dashboards are role-based, it offers ML-based classification and it allows for benchmarking against public indices like Producer Price Index and Category Development Index and/or against peer groups from the SAP Ariba user community.
SCALUE	SCALUE is a procurement analytics platform that offers automated ETL and three levels of its platform (essentials, advanced, and excellence) that gradually offer more savings opportunities and risk insights.
Sievo	Sievo offers spend and CO2 analytics. Sievo runs its analytics platform as a service, so it takes care of all data loading and maintenance, creation of cubes and mapping. Once the data is loaded and mapped into the tool, users can interact with their spend data via dashboards and reports. Sievo also offers actionable insights.
Simfoni	Simfoni is a spend analytics provider. It offers spend intelligence that categorizes spend, evaluates trends, automatically identifies potential savings opportunities and allows organizations to track savings over time. It also offers spend automation, which caters to downstream procurement.
Spendata	Spendata is a do-it-yourself spend analytics provider. It emphasizes complete data control and allows users to build spend cubes, map and family data, and manipulate data however they’d like. Any visualizations are done in a third-party tool.
SpendHQ	SpendHQ procurement analytics platform that offers spend analytics and related services, such as ESG benchmarking and project performance management. It offers do-it-yourself reporting, deep drill-down and filters, centralized supplier insights, document management and expert classification and categorization.
Spendkey	Spendkey is a European spend analytics provider that caters mostly to the mid-market. Spendkey is a dashboard-driven spend analysis solution with a slew of out-of-the-box dynamic dashboards that provide standard insights into an organization’s spend across categories and suppliers.
SpendQube	SpendQube is a consultant-backed spend analytics provider that focuses on being user friendly. SpendQube combines AI and human touchpoints to effectively manage data and offers opportunity assessment and spend consulting alongside its data cleansing and visualization capabilities.
Spendscape by McKinsey	McKinsey acquired spend analytics provider Orpheus in 2020 and rebranded it Spendscape. Since then, it has expanded the tool. Spendscape offers the previous capabilities of Orpheus (data categorization, spend analysis, and savings tracking) as well as CO2 analytics, category strategy/market intelligence, and managed services.
SpendView by Analytics8	Analytics8 is a full-service data analytics consulting agency. Its SpendView product cleanses, classifies and performs various analyses on spend data, including correlation and what-if analysis. It also offers supplier scorecarding.
Suplari	Suplari is a spend analytics provider that also offers some aspects of diversity and contract management. It offers data cleansing and normalization, dashboarding and an insights dashboard with over 60 pre-defined insights.
The Smart Cube	WNS acquired The Smart Cube in 2022. It offers category intelligence, commodity intelligence, supplier risk intelligence and procurement analytics.

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Glossary: Related Definitions

Term	Definition
Data Cleansing & Enrichment	The ability to identify and resolve errors and inconsistencies, such as misspelled supplier names or supplier abbreviations that cause duplicate supplier profiles, and then fill in any missing data (following the previous example, perhaps that supplier profile was missing an address). In other words, cleansing and enriching data makes it more accurate, complete, and usable.
Data Modeling	The ability to build more sophisticated analyses on cost drivers, demand, contracts, working capital, risk, supply chain and overall procurement performance while maintaining data that is flexible and rich enough to fully support this type of spend analytics. This typically includes visuals and graphics, either in the form of static reports or dynamic dashboards. Some solutions offer this in house while others use a third-party BI tool.
Data Normalization	The ability to take dissimilar data and modify it to fit the desired format. Some solutions can normalize both structured and unstructured data while others can only normalize structured data.
ETL	This stands for “extract, transform, load” and is the process of getting data from the source system (i.e., the client), shaping it to match the desired structure and entering it into the storage system (in this case, the spend analytics technology).
MDM	Master data management refers to the process of managing (which includes ensuring the accuracy of) data for the entire organization. This includes not only the actual data, but the metadata behind it. Additionally, an MDM system must be able to distribute data (a.k.a. Golden records) to other systems and pull data from other systems.
Spend Cube Analysis	The ability to allow procurement power users and business users to tailor spend insights to their unique needs with core analytics for spend cubes, e.g., role-based, category-tuned, drillable and filters. In other words, the ability to interact with the data via the views and filters that make the most sense for your organization.
Taxonomy	A data taxonomy is a structured hierarchy of data. Taxonomies can be thought of a bit like a tree, where each branch is a different data ‘category’ of grouped data. In spend analytics, data is most often grouped by category (e.g., marketing, direct materials, etc.), organization or geography with more granular sub-categories available (i.e., the ‘direct materials’ category could be broken down into different sub-categories like forgings, stampings, fasteners, etc.).