Category Archives: Vendor Review

Vendor Coverage on Sourcing Innovation

This page will be regularly maintained and link to all current vendor coverage on Sourcing Innovation, where “current” is defined to be within the last three years for the most recent vendor coverage.

Note that, as of December 3, 2025, all vendor coverage is sponsored. Any coverage included between December 1, 2022 and December 3, 2025* is because the vendor has sponsored new or upcoming coverage (and the older coverage is deemed to still be valid). All vendors covered adhere to Sourcing Innovation’s policies and review requirements as defined in the FAQ.

Company Primary Offering(s) Coverage
Spendata Analytics (2024 Mar 21)
The Power Tool for the Power Analyst
(2024 Jul 01)
A True Enterprise Analytics Solution
(2026)
New coverage coming soon!

* Any coverage that is currently private that occurred between December 1, 2022 and December 3, 2025 may be made public once a minimum of three years has passed.

Spendata: A True Enterprise Analytics Solution

As we indicated in our last article, while Spendata is the absolute best at spend analysis, it’s not just a spend analysis platform. It’s a general-purpose data analytics platform that can be used for much more than spend analysis.

The current end-state vision for business data analytics is a “data lake” database with a BI front end. The Big X consultancies (aided and abetted by your IT department, which is only too eager to implement another big system) will try to convince you of the data paradise you’ll have if you dump all of your business data into a data lake. Unfortunately, reality doesn’t support the vision, because organizational data is created only to the extent necessary, never verified, riddled with errors from day one, and left to decay over time as it’s never updated. The data lake is ultimately a data cesspool.

Pointing a BI tool at the (dirty) lake will spice up the data with bars, pies, waves, scatters, multi-coloured geometric shapes, and so on, but you won’t find much insight other than the realization that your data is, in fact, dirty. Worse, a published BI dashboard is like a spreadsheet you can’t modify. Try mapping new dimensions, creating new measures, adding new data, or performing even the simplest modification of an existing dimension or hierarchy, and you’ll understand why this author likes to point out that BI should actually stand for Bullsh!t Images, not Business Intelligence.

So how does a spend analysis platform like Spendata end up being a general-purpose data analytics tool? The answer is that the mechanisms and procedures associated with spend analysis and spend analysis databases, specifically data mapping and dimension derivation, can be taken to the next level — extended, generalized, and moved into real time. Once those key architectural steps are taken, the system can be further extended with view-based measures, shared cubes where custom modifications are retained across refreshes, and spreadsheet-like dependencies and recalculation at database scale.

The result is an analysis system that can be adapted not only to any of the common spend analysis problems, such as AP/PO analysis or commodity-specific cubes with item level price X quantity data, but also to savings tracking and sourcing and implementation plans. Extending the system to domains beyond spend analysis is simple: just load different data.
The bottom line is that to do real data analysis, no matter what the domain, you need:

  • the ability to extend the schema at any time
  • the ability to add new derived dimensions at any time
  • the ability to change mappings at any time
  • the ability to build derivations, data views, and mappings that are dependent on other derivations, mappings, views, inputs, linked datasets, and so on, with real-time “recalc”
  • the ability to create new views and reports relevant to the question you have … without dumping the data to Excel
  • … and preserve all of the above on cube data refreshes
  • … in your own copy of the cube so you don’t have to wait for anyone to agree
  • … and get an answer today, not on the next refresh next month when you’ve forgotten why you even had the question in the first place

You don’t get any of that from a spend analysis solution, or a BI solution, or a database pointing at a data lake. You only get that in a modern data analysis solution — which supports all of the above, and more, for any kind of data. A data analysis system works equally well across all types of numeric or set-valued data, including, but not limited to sales data, service data, warranty data, process data, and so on.

As Spendata is a real data analysis solution, it supports all of these analyses with a solution that’s easier and friendlier to use than the spreadsheet you use every day. Let’s walk through some examples so you can understand what a data analysis solution really can do.

SALES ANALYSIS

Spending data consists of numerical amounts that represent the price, tax, duty, shipping, etc. paid for items purchased. Sales data is numerical amounts that represent the price, tax, duty, shipping, etc. paid for items sold.

They are basically the inverse of each other. For every purchase, there is a sale. For every sale, there is a purchase. So, there’s absolutely no reason that you shouldn’t be able to apply the exact the same analysis (possibly in reverse) to sales data as you apply to spend data. That is, IF you have a proper data analysis tool. The latter part is the big IF because if you’re using a custom tool that needs to map all data to a schema with fixed semantics, it won’t understand the data and you’re SOL.

However, since Spendata is a general-purpose data analysis tool that builds and maintains its schema on the fly, it doesn’t care if the dataset is spend data or sales data; it’s still transactional data and it’s happy to analyze away. If you need the handholding of a workflow-oriented UI, that can also be configured out of the box using Spendata‘s new “app” capability.

Here are three types of sales analysis that Spendata supports better than CRM/Sales Forecasting systems, and that can’t be done at all with a data lake and a BI tool.

Sales Discount Variation Analysis Over Time By Salesperson … and Client Type

You run a sales team. Are your different salespeople giving the same mix of discounts by product type to the same types of customers by customer size and average sales size?

Sounds easy right? Can’t you simply plot the product/price ratio by month by salesperson in a bubble chart (where volume size correlates to bubble size) against the average trend line and calculate which salespeople are off the most (in the wrong direction)? Sure, but how do you handle client type? You could add a “color” dimension, but when the bubbles overlap and the bubbles blur, can you see it visually? Not likely. And how do you remember a low sales volume customer which is a strategic partner, so has a special deal? Theoretically you could add another column to the table “Salesperson, Product/Price Ratio, Client Type, Over/Under Average”, and that would work as long as you could pre-compute the average discount by Product/Price Ratio and Client Type.

And then you realize that unless you group by category, you have entirely different products in the same product/price ratio and your multi-stage analysis is worthless, so you have to go back and start again, only to find out that the bubble chart is only pseudo-useful (as you can’t really figure it out visually because what is that shade of pink (from the multiple red and white bubbles overlapping) — Fuchsia, Bright, or Barbie — and what does it mean) and you will have to focus on the fixed table to extract any value at all from the analysis.

But then you’ll realize that you still need to see monthly variations in the chart, meaning you want the ability to drag a slider or change the month and have the bubble chart update. Uh-oh, you forgot to individually compute all the amounts by month or select the slider graph! Back to square one, doing it all over again by month. Then you notice some customers have long-term, fixed prices on some products, which messes up the average discount on these products as the prices for these customers are not changing over time. You redo the work for the third (or is it the fourth? time), and then you realize that your definitions of client type “large, medium, and small” are slightly off as a client that should be in large is in medium and two that should be in small were made medium. Aaarrrggghhh!!!

But with Spendata, you simply create or modify dimensions to the cube to segment the data (customer type, product groups, etc.) You leverage a dynamic view-based measure by customer type to set the average prices per time period (used to calculate the discount). You then use filters to define the time range of interest, another view with filters to click through the months over time, a derived view to see the performance by quarter, another by year. If you change the definition of client type (which customers belong to which client type), which products for customers are fixed prices, which SKU’s that are the same type, time range of interest, etc. you simply map them and the entire analysis auto-updates.

This flexibility and power (with no wasted effort) gives you a very deep analysis capability NOT available in any other data analysis platform. For example, you can find out with a few clicks that your “best” salesperson in terms of giving the lowest average discount is actually costing you the most. Turns out, he’s not serving any large customers (who get good discounts) and has several fixed price contracts (which mess up the average discounts). So, the discounts he’s giving the small clients, while less than what large customers get, are significantly more than what other salespeople provide to other small customers. This is something you’d never know if you didn’t have the power of Spendata as your data consultant would give up on the variance analysis at the global level because the salesman’s overall ratio looked good.

Post-Merger White-Space Analysis

White space sales analysis is looking for spaces in the market where you should be selling but are not. For example, if you sell to restaurants, you could look at your sales by geography, normalized by the number of establishments by type or the sales of the restaurants by type in that geography. In a merger, you could measure your penetration at each customer for each of the original companies. You can find white space by looking at each customer (or customer segment) and measuring revenue per customer employee across the two companies. Where is one more effective than the other?

You might think this is no big deal because this was theoretically done during the due diligence and the opportunity for overlap was deemed to be there, as well as the opportunity for whitespace, and whatever was done was good enough. The reality couldn’t be further from the truth.

If the whitespace analysis was done with a standard analytics tool, it has all the following problems:

  • matching vendors were missed due to different name entries and missing ids
  • vendors were not familied by parent (within industry, geography, etc.)
  • the improperly merged vendors were only compared against a target file built by the consultants and misses vendors
  • i.e. it’s poor, but no worse than you’d do with a traditional analytics tool

But with Spendata, these problems would be at least minimized, if not eliminated because:

  • Spendata comes with auto-matching capability
  • … that can be used to enrich the suppliers with NAICS categorization (for example)
  • Spendata comes with auto-familying capability so parent-child relationships aren’t missed
  • Spendata can load all of the companies from a firmographic database with their NAICS codes in a separate cube …
  • … and then federation can be used to match the suppliers in use with the suppliers in the appropriate NAICS category for the white space analysis

It’s thus trivial to

  1. load up a cube with organization A’s sales by supplier (which can be the output from a view on a transaction database), and run it through a view that embeds a normalization routine so that all records that actually correspond to the same supplier (or parent-child where only the parent is relevant) are grouped into one line
  2. load up a cube with organization B’s sales by supplier and do the same … and now you know you have exact matches between supplier names
  3. load up the NAICS code database – which is a list of possible customers
  4. build a view that pulls in, for each supplier in the NAICS category of interest, Org A spend, Org B Spend, and Total Spend
  5. create a filter to only show zero spend suppliers — and there’s the whitespace … 100% complete. Now send your sales teams after these.
  6. Create a filter to show where your sales are less than expected (eg. from comparable other customers or Org A or Org B). This is additional whitespace where upselling or further customer penetration is appropriate.

Bill Rate Analysis

A smart company doesn’t just analyze their (total) spend by service provider, they analyze by service role and against the service role average when different divisions/locations are contracting for the same service that should be fulfilled by a professional with roughly the same skills and same experience level. Why? Because if you’re paying, on average, 150/hr for an intermediate DBA across 80% of locations and 250/hr across the remaining 20%, you’re paying as much as 66% too much at those remaining locations, with the exception being San Francisco or New York where your service provider has to pay their locals a cost-of-living top-up just so they can afford to live there.

By the same token, a smart service company is analyzing what they are getting by role, location, and customer and trying to identify the customers that are (the most) profitable and those that are the least (or unprofitable when you take contract size or support requirements into account), so they can focus on those customers that are profitable, and, hopefully, keep them happy with their better talent (and not just the newest turkey on the rafter).

However, just like sales discount variation analysis over time by client type, this is tough as it’s essentially a variation of that analysis, except you are looking at services instead of products, roles instead of client types, and customer instead of sales rep … and then, for your problem clients, looking at which service reps are responsible … so after you do the base analysis (using dynamic view based measures), you’re creating new views with new measures and filters to group by service rep and filter to those too far beyond a threshold. In any other tool, it would be nigh impossible for even an expert analyst. In Spendata, it’s a matter of minutes. Literally.

And this is just the tip of the iceberg in terms of what Spendata can do. In a future article, we’ll dive into a few more areas of analysis that require very specialized tools in different domains, but which can be done with ease in Spendata. Stay tuned!

Spendata: The Power Tool for the Power Spend Analyst — Now Usable By Apprentices as Well!

We haven’t covered Spendata much on Sourcing Innovation (SI), as it was only founded in 2015 and the doctor did a deep dive review on Spend Matters in 2018 when it launched (Part I and Part II, ContentHub subscription required), as well as a brief update here on SI where we said Don’t Throw Away that Old Spend Cube, Spendata Will Recover It For You!. the doctor did pen a 2020 follow up on Spend Matters on how Spendata was Rewriting Spend Analysis from the Ground Up, and that was the last major coverage. And even though the media has been a bit quiet, Spendata has been diligently working as hard on platform improvement over the last four years as they were the first four years and just released Version 2.2 (with a few new enhancements in the queue that they will roll out later this year). (Unlike some players which like to tack on a whole new version number after each minor update, or mini-module inclusion, Spendata only does a major version update when they do considerable revamping and expansion, recognizing that the reality is that most vendors only rewrite their solution from the ground up to be better, faster, and more powerful once a decade, and every other release is just an iteration, and incremental improvement of, the last one.)

So what’s new in Spendata V 2.2? A fair amount, but before we get to that, let’s quickly catch you up (and refer you to the linked articles above for a deep dive).

Spendata was built upon a post-modern view of spend analysis where a practitioner should be able to take immediate action on any data she can get her hands on whenever she can get her hands on it and derive whatever insights she can get for process (or spend) improvement. You never have perfect data, and waiting until Duey, Clutterbuck, and Howell1 get all your records in order to even run your first report when you have a dozen different systems to integrate data from, multiple data formats to map, millions of records to classify, cleanse and enrich, and third party data feeds to integrate will take many months, if not a year, and during that year where you quest for the mythical perfect cube you will continue to lose 5% due to process waste, abuse, and fraud, and 3% to 15% (or more) across spend categories where you don’t have good management but could stem the flow simply by identifying them and putting in place a few simple rules or processes. And you can identify some of these opportunities simply by analyzing one system, one category, and one set of suppliers. And then moving on to the next one. And, in the process, Spendata automatically creates and maintains the underlying schema as you slowly build up the dimensions, the mapping, cleansing, and categorization rules, and the basic reports and metrics you need to monitor spend and processes. And maybe you can only do 60% to 80% piecemeal, but during that “piecemeal year”, you can identify over half of your process and cost savings opportunities and start saving now, versus waiting a year to even start the effort. When it comes to spend (related) data analysis, no adage is more true than “don’t put off until tomorrow what you can do today” with Spendata, because, and especially when you start, you don’t need complete or perfect data … you’d be amazed how much insight you can get with 90% in a system or category, and then if the data is inconclusive, keeping drilling and mapping until you get into the 95% to 98% accuracy range.

Spendata was also designed from the ground up to run locally and entirely in the browser, because no one wants to wait for an overburdened server across a slow internet connection, and do so in real time … and by that we mean do real analysis in real time. Spendata can process millions of records a minute in the browser, which allows for real time data loads, cube definitions, category re-mappings, dynamically derived dimensions, roll-ups, and drill downs in real-time on any well-defined data set of interest. (Since most analysis should be department level, category level, regional, etc., and over a relevant time span, that should not include every transaction for the last 10 years because beyond a few years, it’s only the quarter over quarter or year over year totals that become relevant, most relevant data sets for meaningful analysis even for large companies are under a few million transactions.) The goal was to overcome the limitations of the first two generations of spend analysis solutions where the user was limited to drilling around in, and deriving summaries of, fixed (R)OLAP cubes and instead allow a user to define the segmentations they wanted, the way they wanted, on existing or newly loaded (or enriched federated data) in real time. Analysis is NOT a fixed report, it is the ability to look at data in various ways until you uncover an inefficiency or an opportunity. (Nor is it simply throwing a suite of AI tools against a data set — these tools can discover patterns and outliers, but still require a human to judge whether a process improvement can be made or a better contract secured.)

Spendata was built as a third generation spend analysis solution where

  • data can be loaded and processed at any point of the analysis
  • the schema is developed and modified on the fly
  • derived dimensions can be created instantly based on any combination of raw and previously defined derived dimensions
  • additional datasets from internal or external sources can be loaded as their own cubes, which can then be federated and (jointly) drilled for additional insight
  • new dimensions can be built and mapped across these federations that allow for meaningful linkages (such as commodities to cost drivers, savings results to contracts and purchasing projects, opportunities by size, complexity, or ABS analysis, etc.)
  • all existing objects — dimensions, dashboards, views (think dynamic reports that update with the data), and even workspaces can be cloned for easy experimentation
  • filters, which can define views, are their own objects, can be managed as their own objects, and can be, through Spendata‘s novel filter coin implementation, dragged between objects (and even used for easy multi-dimensional mapping)
  • all derivations are defined by rules and formula, and are automatically rederived when any of the underlying data changes
  • cubes can be defined as instances of other cubes, and automatically update when the source cube updates
  • infinite scrolling crosstabs with easy Excel workbook generation on any view and data subset for those who insist on looking at the data old school (as well as “walk downs” from a high-level “view” to a low-level drill-down that demonstrates precisely how an insight was found
  • functional widgets which are not just static or semi-dynamic reporting views, but programmable containers that can dynamically inject data into pre-defined analysis and dimension derivations that a user can use to generate what-if scenarios and custom views with a few quick clicks of the mouse
  • offline spend analysis is also available, in the browser (cached) or on Electron.js (where the later is preferred for Enterprise data analysis clients)

Furthermore, with reference to all of the above, analyst changes to the workspace, including new datasets, new dashboards and views, new dimensions, and so on are preserved across refresh, which is Spendata’s “inheritance” capability that allows individual analysts to create their own analyses and have them automatically updated with new data, without losing their work …

… and this was all in the initial release. (Which, FYI, no other vendor has yet caught up to. NONE of them have full inheritance or Spendata‘s security model. And this was the foundation for all of the advanced features Spendata has been building since its release six years ago.)

After that, as per our updates in 2018 and 2020, Spendata extended their platform with:

  • Unparalleled Security — as the Spendata server is designed to download ONLY the application to the browser, or Spendata‘s demo cubes and knowledge bases, it has no access to your enterprise data;
  • Cube subclassing & auto-rationalization — power users can securely setup derived cubes and sub-cubes off of the organizational master data cubes for the different types of organizational analysis that are required, and each of these sub-cubes can make changes to the default schema/taxonomy, mappings, and (derived) dimensions, and all auto-update when the master cube, or any parent cube in the hierarchy, is updated
  • AI-Based Mapping Rule Identification from Cube Reverse Engineering — Spendata can analyze your current cube (or even a report of vendor by commodity from your old consultant) and derive the rules that were used for mapping, which you can accept, edit, or reject — we all know black box mapping doesn’t work (no matter how much retraining you do, as every “fix” all of a sudden causes an older transaction to be misclassified); but generating the right rules that can be human understood and human maintained guarantees 100% correct classification 100% of the time
  • API access to all functions, including creating and building workspaces, adding datasets, building dimensions, filtering, and data export. All Spendata functions are scriptable and automatable (as opposed to BI tools with limited or nonexistent API support for key functions around building, distributing, and maintaining cubes).

However, as we noted in our introduction, even though this put Spendata leagues beyond the competition (as we still haven’t seen another solution with this level of security; cube subclassing with full inheritance; dynamic workspace, cube, and view creation; etc.), they didn’t stop there. In the rest of this article, we’ll discuss what’s new from the viewpoint of Spendata Competitors:

Spendata Competitors: 7 Things I Hate About You

Cue the Miley Cyrus, because if competitors weren’t scared of Spendata before, if they understand ANY of this, they’ll be scared now (as Spendata is a literal wrecking ball in analytic power). Spendata is now incredibly close to negating entire product lines of not just its competitors, but some of the biggest software enterprises on the planet, and 3.0 may trigger a seismic shift on how people define entire classes of applications. But that’s a post for a later day (but should cue you up for the post that will follow this on on just precisely what Spendata 2.2 really is and can do for you). For now, we’re just going to discuss seven (7) of the most significant enhancements since our last coverage of Spendata.

Dynamic Mapping

Filters can now be used for mapping — and as these filters update, the mapping updates dynamically. Real-time reclassify on the fly in a derived cube using any filter coin, including one dragged out of a drill down in a view. Analysis is now a truly continuous process as you never have to go back and change a rule, reload data, and rebuild a cube to make a correction or see what happens under a reclassification.

View-Based Measures

Integrate any rolled up result back into the base cube on the base transactions as a derived dimension. While this could be done using scripts in earlier versions, it required sophisticated coding skills. Now, it’s almost as easy as a drag-and-drop of a filter coin.

Hierarchical Dashboard Menus

Not only can you organize your dashboards in menus and submenus and sub-sub menus as needed, but you can easily bookmark drill downs and add them under a hierarchical menu — makes it super easy to create point-based walkthroughs that tell a story — and then output them all into a workbook using Spendata‘s capability to output any view, dashboard, or entire workspace as desired.

Search via Excel

While Spendata eliminates the need for Excel for Data Analysis, the reality is that is where most organizational data is (unfortunately) stored, how most data is submitted by vendors to Procurement, and where most Procurement Professionals are the most comfortable. Thus, in the latest version of Spendata, you can drag and drop groups of cells from Excel into Spendata and if you drag and drop them into the search field, it auto-creates a RegEx “OR” that maintains the inputs exactly and finds all matches in the cube you are searching against.

Perfect Star Schema Output

Even though Spendata can do everything any BI tool on the market can do, the reality is that many executives are used to their pretty PowerBI graphs and charts and want to see their (mostly static) reports in PowerBI. So, in order to appease the consultancies that had to support these executives that are (at least) a generation behind on analytics, they encoded the ability to output an entire workspace to a perfect star schema (where all keys are unique and numeric) that is so good that many users see a PowerBI speed up by a factor of almost 10. (As any analyst forced to use PowerBI will tell you, when you give PowerBI any data that is NOT in a perfect star schema, it may not even be able to load the data, and that it’s ability to work with non-numeric keys at a speed faster than you remember on an 8088 is nonexistent.)

Power Tags

You might be thinking “tags, so what“. And if you are equating tags with a hashtag or a dynamically defined user attribute, then we understand. However, Spendata has completely redefined what a tag is and what you can do with it. The best way to understand it is a Microsoft Excel Cell on Steroids. It can be a label. It can be a replica of a value in any view (that dynamically updates if the field in the view updates). It can be a button that links to another dashboard (or a bookmark to any drill-down filtered view in that dashboard). Or all of this. Or, in the next Spendata release, a value that forms the foundation for new derivations and measures in the workspace just like you can reference a random cell in an Excel function. In fact, using tags, you can already build very sophisticated what-if analysis on-the-fly that many providers have to custom build in their core solutions (and take weeks, if not months, to do so) using the seventh new capability of Spendata, and usually do it in hours (at most).

Embedded Applications

In the latest version of Spendata, you can embed custom applications into your workspace. These applications can contain custom scripts, functions, views, dashboards, and even entire datasets that can be used to instantly augment the workspace with new analytic capability, and if the appropriate core columns exist, even automatically federate data across the application datasets and the native workspace.

Need a custom set of preconfigured views and segments for that ABC Analysis? No sweat, just import the ABC Analysis application. Need to do a price variance analysis across products and geographies, along with category summaries? No problem. Just import the Price Variance and Category Analysis application. Need to identify opportunities for renegotiation post M&A, cost reduction through supply base consolidation, and new potential tail spend suppliers? No problem, just import the M&A Analysis app into the workspace for the company under consideration and let it do a company A vs B comparison by supplier, category, and product; generate the views where consolidation would more than double supplier spend, save more than 100K on switching a product from a current supplier to a lower cost supplier; and opportunities for bringing on new tail spend suppliers based upon potential cost reductions. All with one click. Not sure just what the applications can do? Start with the demo workspaces and apps, define your needs, and if the apps don’t exist in the Spendata library, a partner can quickly configure a custom app for you.

And this is just the beginning of what you can do with Spendata. Because Spedata is NOT a Spend Analysis tool. That’s just something it happens to do better than any other analysis tool on the market (in the hands of an analyst willing to truly understand what it does and how to use it — although with apps, drag-and-drop, and easy formula definition through wizardly pop-ups, it’s really not hard to learn how to do more with Spendata than any other analysis tool).

But more on this in our next article. For The Times They Are a-Changin’.

1 Duey, Clutterbuck, and Howell keeps Dewey, Cheatem, and Howe on retainer … it’s the only way they can make sure you pay the inflated invoices if you ever wake up and realize how much you’ve been fleeced for …

Tealbook: Laying the Groundwork for the Supplier Data Foundations

It wasn’t that long ago that we asked you if you had a data foundation because a procurement management platform, should you be lucky enough to get one (which is much more than a suite), generally only supports the data it needs for Procurement to function and doesn’t support the rest of the organization. Furthermore, when you look across the Source-to-Pay and Supply Chain spectrums, there are a lot of different applications that support a lot of different processes that have a lot of different data requirements that need to be maintained as different data types in different encoding formats.

Furthermore, as we noted in the aforementioned post, it’s rare enough to find MDM capability that will even support procurement. This is because most suites are built on transactions, most supplier networks on relational supplier data records, and contracts on documents and simple, hierarchical, meta-data indexes. But you also need models, meta-models, semi-structured, unstructured, and media support. And more. The need is broad, and even if you restrict the need to supplier data, it’s quite broad.

As you will soon garner from our ongoing Source-to-Pay+ is Extensive series, in which we just started tackling Supplier Management in Part XV, the supplier data an organization needs is extremely varied and extensive. Given that Supplier Management is a CORNED QUIP mash with ten (10) major areas of functionality, not counting broader enterprise needs around ESG, innovation, product design / manufacturing management, and other needs tied to operations management, engineering, and enterprise risk management, among other functions, it’s easy to see just how difficult even Supplier Master Data Management can be.

Considering not a single Supplier Management solution vendor (as you will come to understand as we progress through the Source-to-Pay is Extensive series) covers all of the basic functions we’re outlining, it’s obviously that not a single vendor can effectively do Supplier Master Data Management today. However, Tealbook, which has realized this since their exception, is aiming to be the first to fix this problem. As of the first release of their open API next month, they are transitioning to a Supplier Data Platform and will no longer focus on being just a supplier discovery platform or diversity data enrichment platform. (They will still offer those services, and will be upgrading them in Q4 with general release expected by the end of 2023, but their primary focus will be on the supplier data foundation that enables this.)

This is significant, and illustrates how far they’ve come in the nine (9) years since their founding when their original focus was on building a community supplier intelligence platform that was reliable, scalable, extensible, and appropriate for new supplier discovery (via a large database of verified suppliers with community reviews). From these humble beginnings, where they didn’t even have a million suppliers in their platform after their third year of existence, they grew into the largest supplier network with over 5 Million detailed supplier profiles that is integrated with the largest S2P suites out there (Ariba, GEP, Ivalua, Jaggaer, and Workday, to name a few) and powers some of the largest organizations on the planet. As part of this empowerment, they can take in an organization’s entire supplier data ecosystem, transform it into their standard formats, match to their records, verify or correct existing data, and then enrich the organization’s supplier records before sending them back. In addition, they integrate with a multitude of BI tools, databases / lakes / warehouses (including ERPs), digital platforms, and so on.

To summarize, that’s a ten fold increase in suppliers and an explosion in global utilization and usage. At the same time, the platform has been augmented with over 2.3M supplier certifications, global diversity data, and the ability to track an organization’s tier 2 supplier diversity data. Quite impressive.

And while this meets most of an organization’s discovery needs, Tealbook knew that it didn’t meet all of an organization’s supplier data needs, especially when you think about all of the regulatory, financial, compliance, performance, sustainability, risk, contract, product/service, relationship, quality, and enablement/innovation data an organization needs to maintain on a supplier. As a result, they have been aggressively working on two key pieces of functionality. An extended universal supplier profile and a fully open, extensible, API that an organization can use to do supplier master data management across their enterprise with the Tealbook Supplier Data Platform. An organization can use the Tealbook Supplier Data Platform to classify, cleanse, and enrich supplier records; augment those records with third party data for sustainability, compliance, and risk; find new suppliers in the network; and so on.

In short, Tealbook is on a mission to be the organization’s trusted supplier data source, and to constantly improve their data offering both with their own ML/AI enabled technology that monitors over 400M+ public websites for supplier-related data (supplier web sites, business registries, certification providers, supplier data providers, etc. etc. etc.), maintains data provenance (when was it last updated, by what/who, etc.), and provides trust scores (in their proprietary framework that indicates Tealbook’s confidence in accuracy and correctness).

The real mission begins next month when they release their new Open API that will allow an organization to integrate, and interact with, Tealbook the way it needs to across its enterprise applications. Congruent with this release, they will also start releasing their enrichment data-packs that will, within the next year, allow the organization to plug-and-play the data they need to confirm firmographics, contact channels and key information, diversity, supplier offerings, financials, certifications, and basic risk data (which Tealbook will offer through partnerships with specialty supplier data providers, giving an organization a one-stop shop vs. having to license with multiple providers separately to build its 360-degree supplier profile).

Then, over the next year, Tealbook will enhance the usability of their data platform by first rebuilding their diversity and discovery applications and then building out new applications around sustainability, risk, benchmarking, and other areas that their customers would rather a data platform handle for them.

Do You Have a Procurement FocalPoint?

Last month we asked where’s the procurement management platform primarily because we now have a plethora of procurement-centric applications but very little integration between them. However, once you tackle that issue, you have the secondary issue of all these applications, but often no clear starting point and, even worse, no way for an average organizational employee outside of Procurement to interact with Procurement beyond an inbound email to “please get this for me” and the eventual, possibly many months later, outbound email to “we got it, it’s finally here … it will be on your desk tomorrow“.

This is a big problem, even in organizations that supposedly have market leading source-to-pay suites. While all the modules are connected, and the integrated workflow will guide a buyer from project selection to sourcing to supplier selection to award to contracting to supplier onboarding to order creation to receipt creation to invoice confirmation and payment approval and loop back to the order creation until pending contract expiration when the contract can be renewed, renegotiated, or
revoked and the sourcing process started all over. This is great, but for predefined sourcing projects on encoded categories only!

It’s not great for any category not already encoded and typically strategically sourced, and it’s atrocious as new product and service needs arise within the organization, as new hires need new assets for onboarding, as customer requirements change and the organization needs to adapt rapidly and source new products or services to meet new, or one-off, needs. There’s no intake, and no collaboration with the organizational stakeholders Procurement is there to serve.

And that’s a huge problem. That’s why you’re seeing a few companies talking about “intake”, “orchestration”, or “PPM” (which stands for either Procurement Performance Management or Procurement Process Management, depending on who is talking about it) because, without this capability, a Procurement platform will never be complete or support the organization.

Following the introductory post on the procurement management platform, we lamented and celebrated that Per Angusta was going away and being integrated into SpendHQ as the foundations of a new PPM. It’s a great start, but today the focus of SpendHQ is on managing the existing workflows and creating visibility into existing projects — and savings tracking is limited to integrated projects. However, when it comes to intake support and project tracking for arbitrary organizational needs, that’s not there yet.

However, there are other players which are strong here, and one of those players is Focal Point, which was built from the ground up as an intake-to-orchestrate solution that is capable of

  • capturing all organizational requests for Procurement and Procurement-related activities,
  • assigning those requests to customizable workflows using either built in automation rules or manual (re-)assignment,
  • allowing an end-user to see exactly where any request is in the process at any time,
  • allowing for in-platform communication between the stakeholder and Procurement,
  • integrating with any external tool through jump-out/jump-in to support the process, and
  • supporting whatever approval chains are required, among other intake and orchestration functions.

The tool was built to solve the most significant problem the founders repeatedly saw as CPOs and implementers of various leading sourcing solutions — little to no intake management or general purpose procurement process orchestration. And it does it incredibly well. The visual workflow construction is extremely usable, and the wizards that power both the process, form construction, and form completion automatically extend and compress the form as needed based upon user selections and actual needs, making for a very smooth flow.

All of the workflow elements and steps support deep conditional logic, allowing the organization to create as many branches as possible but ensuring that the end user making a request, and the end buyer assigned to deal with that request, only see the relevant paths and only need to enter the relevant information to be guided by the platform.

There can be as many intake types, with associated branching workflows, as the organization needs, each can have the appropriate level of automation, and, most importantly, each can have as many milestones as needed to walk the process through at a high level, allowing the requester to easily see at a high level where the process is, and then, if interested, dive into the detailed workflow within the current milestone to get a more accurate picture of where the process is.

The only thing the platform doesn’t do is actual sourcing, supplier management, contract management, analytics, procurement, or payment management. It expects the organization to have tools for this already and integrates into the appropriate modules in those tools as needed to accomplish the workflow in progress.

In terms of getting up and running, Focal Point typically has a fully fleshed out, functioning, and integrated instance that captures all of the organization’s workflows up and running within 90 days, even if the organization is a multi-(multi-)billion dollar organization, which is Focal Point’s target market size. This is because it’s typically the 1B+ organizations that have a lot of tools, and a lot of stakeholders, but no way to manage those tools effectively or to give stakeholders any visibility into where their requests are and how their spending is being managed.

The reason it typically takes 90 days is that, unlike many sourcing suite providers, who just flip a virtual switch and drop an empty SaaS suite on you and say “good luck“, Focal Point fully configures the platform as part of their statement of work. This includes:

  • working with the organization to understand all of their requirements and current workflows
  • encoding all of those intake workflows with milestones, task-breakdowns, and existing platform jump-outs
  • integrating any existing procurement system you need to complete the workflow
  • creating a UAT instance and allowing for at least one iteration and approval before it goes live
  • training your team on how to use the system and maintain the workflows

So even though Focal Point has obviously achieved efficiency in terms of workflow creation and customization, external platform integration, and implementation project management, it takes time for an average organization to collect and document their existing processes and requirements and for FocalPoint (or a third party consulting organization if that is the customer’s preference) to fill in the gaps, so it’s not possible to get it much below 90 days. But when you think about the fact that they have fully implemented a 10B+ organization in that timeframe, when some major suite players will take 18 months working with a consulting partner to fully implement those solutions, that’s an incredible time to value, which is generated day one when every request flows into the tool; gets tracked, assigned, and executed; and stakeholders have full visibility into the process and can intervene if necessary.

Focal Point solves the problem it was built to solve, fills the hole the vast majority of sourcing and procurement solutions make, and does it incredibly well. If any part of this post resonates with you, the doctor encourages you to check them out.