Category Archives: SaaS

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!

Don’t Zip Through the Zip-sponsored Spend Matters authored Intake and Procurement RFP! [2024] (Collected Links)

Don’t Zip Through the Zip-sponsored Spend Matters authored Intake and Procurement RFP!

Please note this is NOT coverage of Zip. See this post for Zip solution coverage!

BONUS

BONUS 2

While Not a Significant Source, Some New Vendors are Contributing to the Procurement Stink!

There are many reasons that Procurement Stinks!

Some of them are due to the Marketplace Madness.

Some of the marketplace madness (a small amount, but non-zero), is aptly summarized as follows.


“We’re pre-revenue, pre-product, and pre-idea.
So any help would NOT be appreciated!“

(Which, to give credit where credit is due, is
a slight rewording of the tag-line to an Andertoon).

Those companies will likely be among the first companies to fail. When there is at least 50 companies that are offering every S2P module, and over 100 for most modules, there is only so much room for differentiation. This means that most of the new startups by the young 30-somethings that did NOT do their market research (but think they know it all because they are tech wizards who built a solution that did slightly more than the three inappropriate products they were stuck with at their last job) don’t really do anything different from a product perspective (and, in fact, usually do a heck-of-a-lot less — hence, “pre-product”). It might be a newer tech stack, it might look slicker, it might be a bit easier to use, but they all fail to understand that THIS IS PROCUREMENT.

This means that, at a minimum, any “product” they want to sell has to satisfy the following:

  • they have to demonstrate a significant ROI, within a decent return within the first 12 months before the CFO will even consider cutting a cheque
  • but before that, they have to show how they will generate long term value before they will even get budget (if the value is one-time like a spend analysis project, especially at Big X quotes of seven figures, not likely)
  • they have to show that it fits in with the current tech stack or IT will object
  • they have to show that it is compliant with regulations or Compliance will object
  • they have to show how it will also decrease overall procurement or supply chain risks, or risk management will steer the budget elsewhere
  • they have to demonstrate they will be able to do more and protect the brand or the CEO will object

Procurement tech is not about cool. That’s consumer tech. Procurement tech is not about the most modern stack to power the business. That’s IT tech. Procurement tech is about VALUE. Procurement is expected to cut costs, NOT increase them!

Until the new generation of founders learns that, and learns there is no way that Procurement will NOT be able to make a case for their ??? ??????? that literally does nothing different than the ??? ?????? tech that came before, the old Procurement Pros aren’t going to buy it. And these start-ups won’t hit break-even as a company, and if they don’t get acquired, they will go belly up as the investors realize how over-crowded the space is and any further investment would be throwing good many after bad into the bottomless money pit.

Proper Solution Selection is Harder Than You Think!

In Jon The Revelator‘s recent post on what can 2005 tell us about Procurement AI in 2024 he listed a dozen vendors from 2004 that no longer exist and asked if we recognized these names. To this, the doctor replied every single one and noted that the market is even more fragmented today than it was in 2004 and pointed you to the Source-to-Pay+ Mega-Map. Jon then asked if history will repeat itself, and as per the doctor‘s recent post on Market Madness, it will … with a vengeance!

This response prompted The Revelator to ask which companies would join their brethren from 2004, to which the doctor provided some indications, which were many (and even more numerous in the Market Madness post). So The Revelator then asked what do practitioners need to do during these pending turbulent times? The real answer is quite a bit and, in fact too much to address in a single article, or even a book, so the doctor decided to focus in on stable solution selection.

And while the doctor made it look as easy as 1, 2, 3 in his comment, when he said:

  1. first identify what kind of solution you need
  2. then identify which providers actually offer those solutions for their geography – market size – vertical
  3. then restrict down to those that are *stable*

It’s a lot more complicated than that, and for some companies, some of these steps will consist of many steps within themselves.

What kind of solution is complicated! At a minimum, one needs to consider:

  • what processes are you doing
  • … and which of these are properly, or not, supported by your current tech
  • what processes should you be doing
  • … and what tech will support those
  • and which subsets of tech are the most relevant (and make sense to focus on)

Which providers is harder.

  • many providers will claim to be everything to everyone, but that’s not true
  • the big analyst firms over-focus on the big vendors, because that’s who they have to (contractually) spend most of their time on
  • smaller firms will focus on the smaller vendors, because some of the big ones believe their big cheque to the big firm(s) covers all their marketing/market needs, and may not have the time to dive deep into geography – market size – vertical appropriateness
  • and logo maps don’t give you near enough detail to even get a short list

In other words, it’s a heck of a lot more than just choosing the first 5 names that come back in a Google or a “chat, j’ai pété” search!

You want a vendor that is going to be around, or if acquired, a solution that is going to be maintained because it’s growing year-over-year, wasn’t built on an oversized investment (pressuring the firm to increase prices or cut costs or grow too fast), 10+ to 50+ customers (depending on solution type and implementation / replacement time and cost and risk tolerance), etc. Where do you get that data? How do you ask in a way that won’t result in the sale person clamming up?

It’s more than most Procurement organization’s can handle as they just don’t have the TQ (Technical Quotient) or the market knowledge. They need to get help from an expert who does who is not biased towards any particular vendor and will follow a proper process, not just throw an RFP over the wall to three providers they have worked with before (as that’s no better than a refined “chat, j’ai pété” search)! And it can be hard to identify the right expert (and the only hint the doctor will give you now is you’re less likely to find one at a random Big X or Mid-Sized Consultancy — some of the Big X, especially those that have been acquiring expert AI and Analytics firms over the past few years, and mid-sized consultancies have them, but these experts are few and far between, spread thin, and unless you are a Fortune 500 / Global 3000, at most of these firms you will be fighting for the senior expert’s time). You might just need a niche consultancy with experts who specialize in this. There are a few, but not as many as the space needs.  [Take into account when you should use a Big X and that it is up to you to properly specify the project, evaluate the proposal, and vet the personnel proposed.  Otherwise, it’s your failure.]

More Valid Uses for Gen-AI … this time IN Procurement!

Some of you were upset that my last post on Valid Uses for Gen-AI weren’t very Procurement centric, arguing that there were valid uses for Gen-AI in Procurement and that the doctor should have focussed on, or at least included, those because why else would almost every vendor and their dog be including “AI” front and center on their web-site (about 85%+)!

Well, you’re right! To be completely fair, the doctor should acknowledge these valid uses, even if they are very few and very far between. So he will. Those of you following him closely will note that he mentioned some of these in his comment on LinkedIn to Sarah Scudder’s post on how “AI is a buzzword“.

AI is a lot more than a buzzword, but let’s give Gen-AI it’s due … in Procurement … first.

With Gen-AI you can:

1. Create a “you” chat-bot capable of responding to a number of free-form requests that can be mapped to standard types.
This is especially useful if the organization employs one or more annoying employees who always waits too long to request goods and then, after you place the order, insist on emailing you every day to ask “are they here yet” in reference to their request, even though you flat out told them the boats are coming by ship, it takes 24 days to sail the goods across the ocean once they are on the ship, typically 3 days to get them to the port, 3 to 14 days to get them on that ship, 3 to 7 days to get the ship into a dock, 3 to 4 days to unload the ship, and 3 to 4 days from the fort, for a minimum delivery time of 35 days, or 5 weeks, and asking week one just shows how stupid this employee is.

2. Similarly, you can create a “you” chatbot for RFP Question Response.
More specifically, you can create a bot that can simply regurgitate the answers to sales people who won’t read the spec and insist on emailing you on a daily basis with questions you already answered, and which they would realize if they weren’t so damn lazy and just read the full RFP.

3. Create meaningless RFPs from random “spec sheets”.
Specifically, take all those random “spec sheets” the organizational stakeholder downloaded from the internet just so you can check a box, send it out, and make him happy. (Even though no good RFP ever resulted from using vendor RFP templates or spec sheets.) Which is especially useless if you have a subscription with a big analyst firm that includes helping you identify the top 5 vendors you are going to invite to the RFP where you will focus on the service, integration, implementation, and relationship aspects as the analyst firm qualified the tech will meet your needs. (After all, sales, marketing, human resources, and other non-technical buyers love to be helpful in this way and don’t realize that just about every “sales automation”, “content management”, and “application system” has all of the same core features and you can usually make do with any one of a dozen or more low-cost “consumerized” freeware/shareware/pay-per-user SaaS subscriptions.)

4. Or, do something slightly more useful and auto-fill your RFPs with vendor-ish data.
You could use the AI to ingest ALL of a vendor’s website, marketing, and sales materials as well as third party summaries and reviews and auto-fill as much of your RFP as you can before sending it to the vendor, and then approximately score each field based on key words, to ensure that the vendor is likely capable of meeting all of your minimum requirements across the board before you ask them to fill out the RFP and, more importantly, spend hours, or days, reviewing their response.

5. Identify unusual or risky requests or clauses in a “ready to go” contract.
Compare the contract draft handed to you by the helpful stakeholder to the default ones in your library that were (co-)drafted by actual Procurement professionals and vetted by Legal and don’t have unusual, risky, or just plain stupid clauses. For example, an unvetted draft could have a clause that says your organization accepts all liability risk, you agree to pay before goods are even shipped, you’ll accept substitute SKUs without verification, etc. (because the helpful stakeholder just took the vendor’s suggested one-sided contract and handed it to you).

6. Automatic out-of-policy request denial.
Program it to just say “denied” for any request that doesn’t fall close to organizational norms.

7. Generate Kindergarten level summaries of standard reports for the C-Suite.
Got a C-suite full of bankers, accountants, and lawyers who don’t have a clue what the business actually does and need simplified reports translated to banker-speak and legalese? No problem!

Of course, the real question is to ask not what Gen-AI can do for you but what can you do without Gen-AI because the doctor would argue that you don’t need Gen-AI for any of this and that the non-Gen-AI solutions are better and more economical!

Let’s take these valid uses one-by-one:

1. You could hire a virtual admin assistant / AP clerk in the Phillippines, Thailand, or some other developing country with okay English skills to do that for 1K a month!
Furthermore, this full time worker could also respond to other, more generic, requests as well, and do some meaningful work, such as properly transcribing hand-written invoices (or correcting OCR errors), etc. And give your employees the comfort of a real, dependable, human for a fraction of the cost of that overpriced AI bullsh!t they are trying to shove down your throat.

2. Classic “AI” that works on key phrases in the hands of the admin assistant will work just as well.
It will find the most appropriate data, and then the admin can verify that the question can be answered by the paragraph(s) included in the RFP, or that the sales person actually read the RFP and is asking for a clarification on the text, or a more detailed specification. The sales person gets the desired response the first time, no time is wasted, and you haven’t p!ssed off the sales person by forcing him to interact with an artificially idiotic bot.

3. When they said the best things in life are free, they weren’t referring to vendor RFPs.
In fact, those free RFPs and spec sheets will be the most expensive documents you ever handle. Every single one was designed to lock you into the vendor’s solution because every single one focussed not on what a customer needed, but the capabilities and, most importantly, features that were most unique to the vendor. So if you use those RFPs and sheets, you will end up selecting that vendor, be that vendor right, or wrong, for you. The best RFPs and spec sheets are the ones created by you, or at least an independent consultant or analyst working in your best interest. No AI can do this — only an intelligent human that can do a proper needs, platform, and gap analysis and translate that into proper requirements.

4. Okay, you need AI for this … but … traditional, now classic, AI could do that quite well.
Modern Gen-AI doesn’t do any better, and the amount of human verified documents and data you need to sufficiently train the new LLMs to be as accurate as traditional, now classic, AI, is more than all but a handful of organizations have. So you’re going to pay more (both for the tech and the compute time) to get less. Why? In what world does that make sense?

5. Okay, you need NLP at a minimum for this, but you don’t need more. And you barely need AI.
All you have to do is is use classical NLP to identify clause types, do weighted comparisons to standard clauses, analyze sentence structures and gauge intent, and identify clauses that are missing, deviating from standard, and not present in standard contracts. And, as per our last use, do it just as well without needing nearly as much data to effectively train. Leading contracts analytics vendors have been doing this for over a decade.

6. Even first generation e-Procurement platforms could encode rules for auto-approval, auto-denial, and conditional workflows.
In other words, you just need the rules-based automation that we’ve had for decades. And every e-Procurement, Catalog Management, and Tail Spend application does this.

7. Any semi-modern reporting or analytics platforms can allow the templates to be customized to any level of detail or summary desired.
And if you have a modern spend analysis platform, this is super easy. Furthermore, if your C-Suite is filled entirely with accountants, bankers, and lawyers who don’t understand what the business does, because they fired all the STEM professionals who understood what the business actually does, then your organization has a much bigger problem than reporting.

In other words, there isn’t a single use case where you actually need Gen-AI, as traditional approaches not only get the job done in each of these situations, but traditional approaches do it better, cheaper, and more reliably with zero chance of hallucination.

At the end of the day you want a real solution that solves a real problem. And the best way to identify such a solution is to remember that Gen-AI is really short for GENerated Artificial Idiocy. So if you want a real solution that solves a real problem, simply avoid any solution that puts AI first. This way you won’t get a “solution” that is:

  • Artificial Idiocy enabled
  • Artificial Idiocy backed
  • Artificial Idiocy enhanced
  • Artificial Idiocy driven

As Sarah Scudder noted on “AI is a buzzword“, AI is a delivery mechanism which, scientifically speaking, is a method by which the virus spreads itself. This is probably the best non-technical description of what AI is ever! And the best explanation of why you should never trust AI!