Author Archives: thedoctor

Have the Analyst Firms Finally Admitted They Don’t Know What They’re Doing?

the doctor recently went on a big rant about the analyst firms and the utter lack of usefulness in the maps they release, the focus they put on what they don’t understand, and the award categories they invent because, even though they have/had some great talent (and should be doing incredible work), what they’ve publicly released has been mostly valueless to the market they’ve been trying to serve (when it wouldn’t be too hard to provide a lot of value based on all the research and work they do). In the doctor‘s view, this is very sad because if they could demonstrate the value they provide, they would be more relevant across the market (and likely get a lot more business from smaller and/or more innovative providers who think that, because of the budgets the big players like Oracle, SAP, and Coupa have, the analysts are always going to recommend those companies anyway).

However, now he’s gone from sad to mad about something he has just heard from a couple of vendors regarding one of the biggest firms, because, if true, it means not only do they not have a clue about what is and is not valuable in tech, but they are unnecessarily creating confusing and obfuscating technology that still may be best in class.

So what have they done now? Well, apparently they are now basing 30% of the score on whether or not the vendor has “AI” in their platform, something which they’ve repeatedly proven they have ZERO ability to score whatsoever! So, either a vendor makes false, grandiose claims (and tries to use Applied Indirection to fool the Analyst Idiot that they have more than Artificial Idiocy in their Application Implementation), or they get scored low even if they have the best technology built on best practices, proven algorithms, and consistent results that give their customers a 5X to 10X ROI.

True AI adds value, but, in the doctor‘s experience,

  • up to 80% of AI claims are Applied Indirection (at best) or Artificial Idiocy (at worst); in fact, some of the “AI” in spend analysis is still the “AI” they used in the early 2000s, and the doctor would rather not spell out that sad, but still true for some vendors, racial slur
  • up to 80% of the rest, or up to 16% of tech that claims AI, is level one Assistive Intelligence; and this is typically just classic RPA (Robotic Process Automation) using human-defined parameter-based rules, and the “AI” is the automatic parameter adjustment based on user overrides … not very intelligent, eh?
  • up to 80% of the rest, or up to 4% of the tech that claims AI, is level 2 Augmented Intelligence, which is the first level of AI where the tech can learn from human feedback and provide better insights and recommendations over time on one or more specific tasks, and the first level of AI that you should even consider as AI
  • up to 80% of the rest, up to 1% of the tech that claims AI, and the highest level modern technology has generally achieved, is level 3, Apperceptive Intelligence, or Cognitive Intelligence, where the systems can not only learn from specific human feedback to recommendations but from general knowledge and intelligence available to it from integrated data sources to mimic the performance of the best human experts over time, even evolving processes, behaviours, and actions within well-defined bounds
  • and then the rest, 0.1% or less, is nearing level 4, Autonomous Intelligence, where the system can learn, evolve, adapt, and maintain itself over time without human intervention … and hopefully execute meaningful, appropriate decisions grounded in best process and fact that considers all of the relevant information available (and not go off of the rails and advise you to commit suicide because you feel bad, Hail Hitler, or sacrifice a trolley full of people and a cross-walk full of pedestrians because there might be a cat in the road — all things AI has already done)

And even where a platform has semblances of real AI, chances are that the AI (the vendor is now forced to include or arbitrarily be relegated to the dustbin because, apparently, it’s not solutions but buzz-acronymns that matter now) is producing worst results than the best traditional algorithm or methodology on expert curated data sets and dimensions. For example, the vast majority of the market believes AI improves forecasting. It doesn’t. The best AI is still inferior to the best techniques developed in the 70s when applied to the right data dimensions. All the “AI”, which is just fancy, souped-up versions of classical machine learning (using algorithms developed in the 80s and 90s for which we didn’t have enough computing power until recently), does is run all of the data through a model that integrates classification with prediction to filter out the most relevant dimensions and the best curve fitting technique as all these algorithms, at the core, are based on 50+ year old statistics! This means that, at the end of the day, their best case performance is something a human genius figured out 50+ years ago.

But to achieve that best case, the developers have to implement the right AI algorithms, tune them properly, allow them to run long enough to correctly fit (but not over-fit) the training data sets, and monitor those algorithms over time … and to do that they need to be an expert in those algorithms, which they probably aren’t. So, in order to “check a box”, and sell you a product, they are ultimately integrating algorithms that will give you an inferior result (while requiring considerably more computing power that runs up your cloud utilization bill), versus sticking to tried-and-true algorithms and processes that their experts tweaked over years and that their experts can explain and verify at any time.

And this is an almost reasonable example of what a technology vendor might do (as the best predictive algorithms are not untested “AI” but based on classical, tried-and-true, statistical or optimization functions). Most of what the doctor has seen is MUCH worse than this. And the fact that some big analyst firms are now forcing vendors with good tech to integrate underdeveloped, unproven, and often untested AI just to get a rating, make a map, or be recommended is downright stupid.

SHAME ON ANY ANALYST FIRM THAT DOES THIS! Buzzwords are not products, and unproven tech is not value. Analysts should be recommending the best solutions, regarding of the tech they are based on. the doctor is simply appalled!

Seven Easy Mistakes Source-to-Pay Tech Vendors Can Avoid

A few weeks ago we wrote about Five Easy Mistakes Source-to-Pay Tech Buyers Can Avoid in their effort to procure a fit-for-purpose technology solution to help them with their current challenges because the wrong solution can often be worse than having no solution at all.

However, and this is one thing the doctor knows very well, it’s not just buyers that make mistakes. Vendors do too. Lots of them. Lots more than they’ll care to admit, and these mistakes cost them time, money, sleep, and, sometimes, satisfied customers — which is ultimately the most important thing as satisfied customers will renew software subscriptions indefinitely (while unsatisfied customers will try to end the subscription as soon as possible).

Especially newer vendors, and especially those that haven’t built and/or run a company in our space before. And while some mistakes will be unavoidable (innovation doesn’t happen the first time, some things can only be learned the hard way, etc.), most aren’t. (In fact, the vast majority aren’t.) Usually all that is needed is research and insight, which can often be obtained by overworked founders without enough time by engaging the right advisor*.

So, to help these vendors understand overlooked areas where they are likely making mistakes, and where they should at least get an independent review, so that they can bring you better solutions, we’re bringing to them (and you, so you ask the right questions when considering their solution) the most common mistakes the doctor has seen over and over (and over) in his long career as an (independent) industry analyst, blogger, technical solution reviewer, consultant, researcher, CTO, etc.

Lack of market understanding and the real needs of their potential market

The first time the doctor talks to a new company, either for an introduction, review, or due diligence, one of the first things he hears (or will ask if he doesn’t), is why the founders started this company. And the answer he gets the most by far, so much so that he’s lost count of how many times he’s heard it and struggles to point to significant companies where he didn’t, is because XYZ didn’t do this function we needed to be efficient so we figured there was an opportunity. This would be a perfectly logical response if:

  • XYZ was a company/product that was designed to serve the function the founders were trying to address
  • there weren’t already two dozen products out there that addressed the function already and, at the baseline, did the same thing; literally, the same thing

This becomes especially prevalent during every M&A frenzy where a PE firm decides they need a company that does X, like (accounts) payable(s) during the last frenzy (exacerbated by COVID when PE firms realized/decided that business needed to be conducted entirely online, and decided they all need a virtual collaboration and online payment solution in their network). And the doctor doesn’t want to tell you how many times he heard payments company X was started because bill.com or quickbooks didn’t do basic accounts payable functionality or how few (read: almost none) didn’t do any real research which, in just a few hours, would have uncovered two dozen plus companies with payables capability the founders were sure didn’t exist, and the real opportunity was only in differentiation, specific country/regulatory support, or price-point (as there weren’t a lot of solutions at an affordable price point for smaller mid-size businesses until a few years ago). And even worse, many of these founders thought analysts and potential buyers should be super impressed that they essentially re-invented the software process wheel for a particular function for the twenty-forth time.

So, dear vendor, before you go to market, do your research (or contract someone who can do it properly for you)! And if you don’t understand your real value, contract an analyst that can identify it for you. The market is fickle, unforgiving, and easily swayed by a better presentation (even when from a competitor with lesser technology). Given that the knowledge and resources are out there, there’s no reason NOT to get it right.

Lack of competitive landscape knowledge and the real needs going unserved overall or at an affordable price-point for their target market

Building on our last point, it’s not just knowing what’s out there and what it does, but where your competition is strong and weak, what markets they are going after, what markets you should be going after based on your relative strengths and weaknesses, and what price point that market can easily afford and buy within a reasonable length sales cycle.

the doctor realizes this can be very time consuming, but this is where an implementation consultant or the right analyst can be extremely valuable, as they can quickly provide you with this information based on publicly available knowledge on currently released products based on demos and product reviews they have done, (feedback from) implementations they have been associated with, and (feedback from) integrations that they (or consultants they work with) needed to do, and buyers. A good analyst can do this without sharing any roadmap or non-public details on not-yet released capabilities, and should do that as roadmap and un-released capabilities might never be released, and is not something you should be basing your direction on.

Not knowing your true capitalization needs pre-profitability

While we should applaud companies that can bootstrap, and provide a standing ovation to companies that can raise angel / VC capital early to accelerate development, we should ONLY do so if they make an effort to understand their true cost of development, how long it’s really going to take to make that first sale, how long after that until they will make enough sales to support the minimum headcount they will need to sell and support those customers, and how much cash it’s going to take to get them there and raise it, or at least pre-negotiate follow on raises/loans to get there after the first investment.

Too many good companies fail because

  • they don’t take the time to estimate the true cost
  • they do, but don’t stick to their guns and when the investors say “final offer” at 70% of the estimated amount, they say “we’ll make it work”

And they try. They make a valiant effort. And as money dwindles, they put in 80 hour work weeks, developing more, faster. They amp-up cold-calling, content generation, reach outs, etc. They make their most heroic efforts. But all for naught. You can rush development, but you can’t rush a sales cycle. People need to realize they need a solution, do their research, qualify you, get a budget, go out to RFP, follow an archaic corporate process, and, then, hopefully, they can buy your product. If you’re lucky, you’re entering the process after they get the budget, but then you are fighting against a favoured “incumbent” that they plan to buy from (once they eliminate you), but usually it’s before, which means, on average, you are waiting six months for them to get a budget in the next cycle. If you’re a few months away from closing the doors, that doesn’t help you.

So if you can’t get what you need, don’t start. We all know entrepreneurship sounds great. We all know it’s a great experience to have on your resume. But it’s stupid to start something you know has no chance of succeeding. After all, there’s always another opportunity out there where you stand a chance of success. (And that’s it, even if you have enough in a typical case scenario, pandemics can happen, disasters can happen, markets can shift, or a better solution can be released halfway through your development by someone else that had the idea before you and is currently developing it in stealth mode with double your funding and a marketing budget out of the gate.) So, dear vendor, wait until have you a true chance. Otherwise, you’re wasting your contribution and letting down your early adopters when you close your doors (and that hurts us all when they lose faith in smaller companies and go back to ERP).

Overvaluing the tech (and AI)

A good tool is worth good money, and a great tool is worth great money. And if the great tool significantly increases efficiency, identifies meaningful cost avoidance, and delivers a 5X ROI, such a tool can be worth hundreds of thousands (or even millions of dollars if it is used by hundreds, or thousands, of users globally). But good and great is relative to what it does, how many people in the business it’s used by, the value it is delivering, and, ultimately, the budget the business class you are targeting can afford to pay based on the first three factors.

Tech for the sake of tech, while cool, has no value beyond being cool. Even if you have a lot of actual “AI” baked in (and let’s face it, if you do, the “AI” is only solving a very focussed, niche, problem), it’s still valueless unless it delivers value. It doesn’t matter how long you took to build it, how much it cost you (which can be a very poor measure because if you didn’t have a good team, overpaid that team, didn’t have the product or goal well designed when you started, p!ss3d hundreds of thousands away on Class A office space and big parties, it might have cost you tens of millions to build something a smarter, more focussed, cost conscious team could have built for two million), or how unique it is — in business, it needs value.

And before you try to sell it, you need to understand that value from a customer’s viewpoint, otherwise you’re going to have quite a challenge and customers who would otherwise jump on something fairly priced will not buy it even when it could be the best solution for them. (It’s not what the competition is selling it for, it’s what it should be sold for … one of the reasons too many Procurement departments don’t have modern tech is that they can’t get the budget for software priced using traditional enterprise software pricing that only the F500s/G3000s can afford.)

Undervaluing the tech (and AI)

Again, a good tool is worth good money and a great tool is worth great money when it delivers the right efficiency gains, cost avoidance, and value to a business that is losing a lot of money due to inefficiency and lack of insight.

Thus, you also have to be careful NOT to undervalue the tool or slash the price in an effort to get customers in the door quickly or sell to smaller organizations than you should be selling to, especially if the tech was expensive to build and no other organization could build it for less than 80% (or more) of what your organization invested into it and the cost of maintenance/continued development (due to advanced tech or unique capabilities or lots of integrations) is high. The reality is that once you set a price, that doesn’t become the floor, it becomes the ceiling, and if the price is not sustainable, you will go out of business and that will hurt not only you, but any early adopter that buys into you (and, again, that will hurt us all when they lose faith in smaller companies and go back to ERP).

Overestimating the DiY nature of the tech

Easy for you is not easy for a buyer. Remember, you’re the expert in the Tech — you built it, as well as an expert in the inefficiencies in the tech that came before — that’s why you built it, and an expert in the workflows that work well — that’s how you built it. You have the deep knowledge of the tech, the deep knowledge of the best practices, and the deep knowledge to know when a problem is best addressed by the tool, and when it’s not, and how out-of-the-box the support is, and how much has to be customized.

On the other hand, your potential client might be spending most of their time in Gmail and Excel, have never used the previous tool, and have no knowledge of current best practices. The customer may need training on the best practices, the workflows, and the tech, as well as a large reference library to remind themselves on how to use the tool if certain aspects of the process are not done very often (like once a month at most).

If services are needed, customers are not going to respond well to software only, or believe a low-cost when they know they will need the services. Understand the balance, present it appropriately, and sell it appropriately.

Misunderstanding the average customer capability & TQ

Building on the above, in addition to not overestimating the DiY nature of the tech, you should not misunderstand the average customer capability and the Technical Quotient of your target market. As we noted above, not all Procurement departments are advanced in the tech they have access to, and not all Procurement Professionals are adept with / used to modern tech. One has to remember that, for the longest time, Procurement was literally the island of misfit toys, and their understanding of technology and technology-enabled best practices was literally non-existent (as they typically didn’t use technology beyond the fax machine).

Even today, they may not be familiar with much more than basic consumer software for searching, e-reading, e-commerce, email, and gaming. Customized, deep, enterprise software may not be in their experience or repertoire.

Alternatively, if you are selling to a risk management or data analytics departments at big companies, they may have hired data scientists with deep training in mathematics and computer science used to not only using difficult mathematical (like Matlab and Octave) and analytics platforms (Qlik and Tableau), but building their own using open source analytics and data science platforms (like SciKit and Dataiku).

Know your audience and what they are capable of.

Failing to put the relationship first

In consumer software, it’s a transaction. But in enterprise software, it’s a relationship that you need to build, support, and adapt with. If the customer wants a transaction, they’ll use mass-market user-subscription based software or shareware. They’re going to you because they need services and support from a software provider that are experts in the technology and the process, can help them achieve their goals, and will keep the SaaS platform relevant.

* (HINT! HINT! STARTUPS/BEST-OF-BREEDS, STOP ASSUMING YOU KNOW IT ALL AND CAN DO IT ALL IN HOUSE! YOU CAN’T! YOU DON’T HAVE THE BUDGET FOR A FULL TIME EXPERT IN EVERY AREA. BUT YOU CAN OFTEN GET AWAY WITH PART TIME/SHORT TERM CONSULTING / ADVISORY. SO JUST DO IT!)

An Ode to Suites …

No matter what you think you’ve done
You’ll find it’s not enough
No matter what you think you know
Buyers have it tough

It’s a given, startup law
Someone’s faster on the draw
No matter where you hide
They’re comin’ after you

Now matter how the race is run
It always ends the same
Your customers abandon you
For a newer SaaS play

You can shake it for a while
Live it up in style
No matter what you do
They’re comin’ after you

Shakedown, breakdown, takedown
Everybody wants into the crowded space
Breakdown, takedown, you’re ousted

Let down, your guard, honey
Just about the time you think that it’s alright
Breakdown, takedown, you’re ousted

This is a town where everyone
Is racing for the top
This is a place where second best
… will never do

It’s O.K. to want to shine
But once you step across that line
No matter where you hide
They’re comin’ after you

Shakedown, breakdown, takedown
Everybody wants into the crowded light
Breakdown, takedown, you’re ousted

Shakedown, breakdown, honey
Just about the time you think that it’s alright
Breakdown, takedown, you’re ousted

AnyData: Your Mid-Market BoB Analytics Solution for Opportunity Identification, Compliance Tracking, and Associated Project Management

AnyData, which we first covered on Sourcing Innovation back in 2017 (in AnyData: Another Analytics Arriviste from Across the Atlantic), has matured significantly since 2017, especially with its addition of auto-rule generation using AI in 2020 (as chronicled in AnyData, making spend analytics accessible by anyone! [Or, ‘The mid-market analytics quandary’]) over on the Spend Matters Content Hub, Pro/Insider subscription required).

Starting out as a Visual Development Framework / Data Hub, AnyData progressed into a good analytics offering, augmented by a good contract management (governance) offering, augmented by a good supplier management (primarily supplier data hub) offering, and that’s about where it was until 2020 when it significantly improved its analytics offering from a usability perspective and began the journey to take the rest of the platform from good to great.

Over the last two years, it has continued to refine its analytics offering from a quick-start/ease-of-use perspective where it has you up and running and doing real analytics in a guided self-service model in as little 15 minutes, added key field extraction and [better] analytics to its contract management module, included ESG/Sustainability support in its Supplier Management module, and built a brand new Project (Activity) (Savings) Tracking module with default templates for analytics-based savings projects, contract ([re-]negotiation projects), and supplier data gathering/compliance projects. In addition, using their rapid development capability, they can build custom, smart, project forms for any project type an organization wants to track very quickly (typically a few hours, a day at most) for a low fee [in addition to the standard templates you get included in the base subscription].

Since introducing their enhanced analytics offering three years ago with AI-based rule-derivation and easy re-classification, they have enhanced each part of the process to guide an average Procurement Practitioner and fledgeling analyst through each step of the process in a best-practice way that teaches them the basics while allowing them to go wide and deep as soon as they are ready.

For most clients, the AnyData process is this:

  1. Upload the file
  2. Determine if you want to
    • load the data Fresh,
    • Merge the data in, or
    • merge new data in while Replacing existing data for a time-period
  3. Select a Taxonomy (Standard, Industry Best Practice, or Tailored)
  4. Configure the Automation Process as needed
    (fields to analyze, priority, confidence intervals, etc. … or use the defaults)
  5. Review the Business KPI Dashboard and, optionally, drill into the Categorization Data Quality Dashboard
    • Optionally: Define rules for any significant or relevant unclassified spend (note that 100% mapping is not necessary, and not achievable by ANY solution on the market; 90%+ of the spend in a category is enough)
    • Optionally: Override the auto-generated rules, especially if you have a few products or services you treat atypically (for the chosen taxonomy; e.g. printer cartridges in electronics vs. office suppliers)
    • Optionally: Review, drag and drop the taxonometric (sub)categories as desired
    • Optionally: Enhance data/classifications as needed
  6. Review the Significant Opportunity Dashboards
  7. Dive into the Dashboards of Interest,
    which you can modify as needed to update / add analysis in a visual manner as needed
  8. Kick-off one or more analytics/savings project on an opportunity of interest
  9. Repeat from any step in the process as needed

A few points of note:

  • Through the SaaS front-end, file size is limited by browser limits but since they support compressed file uploads (and real-time decompression), that can easily be a 20GB file (uncompressed); if the initial file is too big, they support SFTP
  • AnyData is fast, so under a million rows of data only takes minutes to fully process
  • AnyData has best-practice starting taxonomies for multiple industries and can provide you with one upon setup
  • They tie their industry best-practice taxonomies to the standard taxonomies (like UNSPSC) and can make use of multiple data points for classification and for the industries they know well, their default rule generation will correctly classify 80%+ out of the gate
  • Their visual focus and the ability to drag and drop categories makes taxonomy classification super easy (and fields makes rules classification easy as well)
  • Rules can be on any combination of fields and use multiple types of matching (exact, partial, regex, etc.)
  • The Data Quality dashboard gives you a quick overview of the quality of data you uploaded (and the confidence you can have in the auto-generated rules without reviewing any manually, until you identify a need to do so in a category deep-dive)
  • The opportunity dashboards identify the best opportunities uncovered in the automated out-of-the-box analysis
  • It’s a click to start a savings/analytics project
  • You can jump back to any step at any time and continue on (down a different path) …

It’s tailored for quick start, quick execution, and quick time-to-value for a mid-market staffed by Procurement Professionals who are short on time and short on analytics training as it trains those procurement professionals how to do proper analytics through (semi-)guided workflows as they go.

If you’re a Mid-Market looking for a Best-of-Breed (BoB) analytics solution, AnyData should definitely be on your short list, especially since it’s one of the few offerings that can be obtained at an incredibly affordable price point due to the high DiY nature of the tool (and the focus on self-selection and self-serve SaaS sales). There aren’t many tools where you can get enterprise subscriptions starting at less than 2K/month, and few equal AnyData.

It Doesn’t Matter Where You Start, You End with BoB in Analytics!

In a recent article, we asked in the battle of Suite vs. BoB (Best-of-Breed), which do you choose, and ended up with the answer of neither, but potentially both, because, as indicated in our article we asked in our post on Where’s the Procurement Management Platform, you need a true platform (that enables the creation of a true source-to-pay plus ecosystem for the various workflows and processes that need to be managed).

As a result, we indicated you could start where you wanted, provided:

  • you could conceivably manage it,
  • the vendor offers, and publicly publishes, a complete Open API, and
  • the vendor offers the necessary quick-start services.

(And for even more details on each of these requirements, stay tuned for our upcoming article on how it doesn’t matter where you start, you end with BoB in SXM).

But where do you end up? For some Procurement Practitioners, it depends on:

  • the module,
  • the organization’s biggest need for workflow/process management, and
  • the organization’s biggest savings/cost avoidance/value creation opportunities.

(And again, we’ll have even more details in our upcoming article on how you end with BoB in SXM for more details.)

But for Analytics, like SXM, you will end at BoB for analytics as no suite equals the best in class (BiC) (spend) analytics solutions (even if they are built in BiC technologies for generic analytics like Qlik or Tableau) as the true BoB spend analysis solutions (which are fewer and further between than you would expect) are leagues beyond them.

Moreover, for Analytics, you should start with BiC, even if the suite has a pre-packaged solution that’s pretty good, enough to get going, more than your fledgeling analysts are likely to be able to handle in the first year, and appears to be offering the module cheap as an add on to everything else they are selling you. Why?

Lots and lots of reasons. Here are five to get you started:

  • Top X Opportunities: Suites will only show you your top 10 categories, top 10 suppliers, top unmanaged tail categories, etc. No guarantee that these primitive, canned, analysis will be YOUR biggest opportunities. BoB will come with hundreds of built-in analytics, considerably more customization capability, and the power to find opportunities that pre-built suites and dashboards will never give you.
  • Better Classification: Suites will do a decent classification, usually through their black box AI (trained on billions and trillions), but even if they get to 95%, it won’t be great, it won’t be manageable, and it won’t be customizable to your organization’s need. BoB, when it uses AI, will use it to create rules, that can be corrected and overridden, that you can customize to your specific taxonometric needs for optimized Procurement (and no standard industry classification is worth its weight in protactinium), usually starting with an out-of-the-box taxonomy customized to your industry using the vendor’s experience and community knowledge.
  • Better Analytics: many of these tools have a lot more capability in terms of report construction, dimension derivation, metric support, integrated data science, etc. etc. etc.
  • Better UX: while UX is completely subjective, and as per a (previous/upcoming) rant, is not something an analyst should be scoring and advising you on (as the best UX is the one that works best for you), in general, the probability is very high that you will find these BoB tools more customizeable in workflow and configuration, more logical in workflow, and much easier to use (if this wasn’t the case, no one would buy these tools and the vendors would have closed their [virtual] doors a long time ago)
  • Beyond Analytics: most BoB solutions will have integrated opportunity selection and project/savings tracking, performance/throughput/project metric support, and/or risk-based analytics. The value of analytics is continually overlooked because the “Savings” is identified in the sourcing event, captured in the contract, and realized in Procurement, and no one wants to acknowledge the opportunity would not even have been identified without analytics.

And, finally, why not get used to using a best-in-class tool from the get-go so you don’t have to relearn a new tool when you max out the capabilities of the suite solution and are ready for the next level? Especially when, as you get better and better at analytics and dive deeper and deeper into categories, you can improve the taxonometric mappings, track all the opportunities you identify (and your progress), do what-if analysis when the mood strikes, and get productive in a tool that will do [much, much] more for you in the long run?

So, while you might select a suite SIM module as a foundation for your supplier data store when you need to start centralizing supplier data somewhere for your sourcing projects and procurement buys (which is where your organization has determined it needs to start its S2P journey), when you’re ready for analytics, just go straight to BoB. (And if the C-Suite wants to see reports in the fancy suite, buy the basic reporting package and let them use the basic dashboards. And if the suite supports custom dashboards, then pump the appropriate analytics back in as reporting data. Get good with best-in-class analytics from the go with the best solution you can.)