Category Archives: Spend Analysis

Spend Analysis IV: Defining “Analysis”

Today I’d like to welcome back Eric Strovink of BIQ (acquired by Opera Solutions, rebranded ElectrifAI) who, as I indicated in part I of this series, is authoring the first part of this series on next generation spend analysis and why it is more than just basic spend visibility. Much, much more!

“No canned report survives first contact with the analyst.”

Analysis = Agility

Reporting on a large transaction dataset is
technically challenging. For example, pointing ordinary reporting
tools at a large dataset doesn’t work well, because what might
seem like a perfectly ordinary and reasonable database query can require minutes to complete, sometimes even hours. That’s why OLAP (“On Line
Analytical Processing”) technology is required in order to
return results quickly on large datasets, and that’s why every data
warehouse uses some variant of it.

OLAP is not a panacea. OLAP database queries only
work within a rigid framework — that is, queries are
fast only within the data dimensions and hierarchies that have
been pre-defined. To ask a question outside of that rigid
framework, and to get an answer to that question in a reasonable
amount of time, the underlying dataset structure must be changed —
either dimensional hierarchies must be altered, data re-mapped, or
entirely new data dimensions created.

Data analysis is an inherently ad hoc process —
to paraphrase Sun Tzu, “no canned report survives
first contact with the analyst.” But, in order to be able to perform the
OLAP queries that support ad hoc reporting, it is necessary to change the dataset structure to support those queries. And, it had better be possible
to do that quickly and easily; otherwise OLAP power cannot
be brought to bear on the ad hoc report, which means that
the report can’t be generated without great pain.

Analysis therefore equates, in a very real sense, to “agility”;
in other words, how quickly and easily one can:

  • generate new dimensions;
  • change existing dimensional hierarchies;
  • map and family new and existing dimensions.

Agility also applies at a higher level. I argued in

Spend Analysis I: The Value Curve that the notion of
one dataset for spending data is limiting, because many
different analysis views — especially commodity-specific
views — can be key to driving additional value. If the spend analysis process involves the creation of multiple datasets over time, and it’s hard or expensive to build or modify datasets, then that process can’t move forward.

Agility also requires that the above operations be performed by
business users with limited IT skills, on their own, without
assistance from vendor or internal experts. If the system is
not agile, then the default decision is not to analyze,
as pointed out in

Spend Analysis II: The Psychology of Analysis. That is
the worst possible outcome for the enterprise, because it perpetuates
information starvation in a land of data plenty.

Analysis = Speed

Here’s a heretical statement, coming from a spend analysis vendor:
anything that a spend analysis system does for you can be done
with ordinary tools. You can use a database system to load a large dataset; you can cleanse your own data by writing database queries; you can write programs to build reports; you can dump data to pivot tables. You can get great answers to your questions. Some old-school sourcing consultants still use manual methods like these, and some home-grown spend analysis systems built around tools like Microsoft Access are still operating today.

However, if you do use a modern spend analysis system, you can produce
those same pivot tables and reports with a few mouse-clicks; and, you can alter their properties and constraints with slice-and-dice operations easily and quickly. For every report that the old-school consultant generates, you’ll have had the opportunity to generate hundreds. Does this mean that your insights will be better than those of the consultant? Not necessarily; but it’s hard to argue that they shouldn’t be.

If your spend analysis system isn’t agile, though, you’ll be back in the same boat as the crusty old consultant, and he’ll be laughing at you. You’ll have to extract transactions from the system and hack at them with the same tools that the consultant uses, with the same productivity loss.

Analysis = Power

It’s important to distinguish between ad hoc reporting and reporting in general. Does the spend analysis system have the ability to produce complex and custom reports, guided by you? Or are its reports written in some programming language like Java or C++, the source code for which is inaccessible to you and unmodifiable by anyone but the vendor?

Analysis power is precisely the power that you wield as a business user,
independent of canned reports supplied by a vendor. Complex, multi-page
reports such as the original MMG Commodity Spending Report (below),
variants of which are now commonplace across the e-sourcing space, should be within your reach to create quickly and easily — without any
programming, database queries, or other IT magic, and yet with full flexibility to build whatever it is that you need.

Next: Spend Analysis V: New Horizons (part 1)

Spend Analysis III: Common Sense Cleansing

Today I’d like to welcome back Eric Strovink of BIQ (acquired by Opera Solutions, rebranded ElectrifAI) who, as I indicated in part I of this series, is going to be authoring the first part of this series on next generation spend analysis and why it is more than just basic spend visibility. Much, much more!

Many observers would acknowledge that there’s not a lot of difference between viewing cleansed spend data with SAP BW or Cognos or Business Objects, and viewing cleansed spend data with a custom data warehouse from a spend analysis vendor. They’re all OLAP data warehouses; they all have competent data viewers; they all provide visibility into multidimensional data. What has historically differentiated spend analysis from BI systems is the cleansing process itself (along with, in contrast to the BI view, the decoupling of data dimensions from the accounting system).

Because it’s hard to distinguish one data warehouse from another, cleansing has become an important differentiator for many spend analysis vendors. The vendor has typically developed a viewpoint as to the relative merits of manual labor/offshore resources, automated tools, custom databases, and so on, and sells its SA product and services around that viewpoint. Unfortunately, all the resulting hype and focus on cleansing services, from both these vendors and the analysts who follow them, has obscured a simple reality — namely, that effective data cleansing methods have been around for years, are well understood, and are easy to implement.

The basic concept, originated and refined by various consultants and procurement professionals during the early to mid-1990’s, is to build commodity mapping rules for top vendors and top GL codes (top means ordered top-down by spending) — in other words, to apply common sense 80-20 engineering principles to spend mapping. GL mapping catches the “tail” of the spend distribution, albeit approximately; vendor mapping ensures that the most important vendors are mapped correctly; and a combination of GL and vendor mapping handles the case of vendors who supply multiple commodities. If more accuracy is needed, one simply maps more of the top GLs and vendors. Practitioners routinely report mapping accuracies of 95% and above. More importantly, this straightforward methodology enables sourcers to achieve good visibility into a typical spend dataset very quickly, which in turn allows them to focus their spend management efforts (and further cleansing) on the most promising commodities.

Is it necessary to map every vendor? Almost never; although third-party vendor mapping services are readily available, if you need them. And, as far as vendor familying is concerned, grouping together multiple instances of the same vendor clears up more than 95% of the problem. Who-owns-whom familying using commercial databases seldom provides additional insight; besides, inside buyers are usually well aware of the few relationships that actually matter. For example, you won’t get any savings from UTC by buying from Carrier and from Otis Elevator. And, it would be a mistake to group Hilton Hotels under their owners, since they are all franchisees.

[N.B. There are of course cases where insufficient data exist to use classical mapping techniques. For example, if the dataset is limited to line item descriptions, then phrase mapping is required; if the dataset has vendor information only, then vendor mapping is the only alternative. Commodity maps based on insufficient data are inaccurate commodity maps, but they are better than nothing.]

80-20 logic also applies to the overall spend mapping problem. Consider a financial services firm with an indirect spend base. Before even starting to look at the data, every veteran sourcer
knows where to start looking first for potential savings: contract labor, commercial print, PCs and computing, and so on. Here is a segment of the typical indirect spending breakdown, originally published by The Mitchell Madison Group:

< Graphic No Longer Available >

If you have limited resources, it can be counterproductive to start mapping commodities that likely won’t produce savings, when good estimates can often be made as to where the big hits are likely to be. If you can score some successes now, there will be plenty of time to extend the reach of the system later. If there are sufficient resources to attack only a couple of commodities, it makes sense to focus on those commodities alone, rather than to attempt to map the entire commodity tree.

The bottom line is that data cleansing needn’t be a complex, expensive, offline process. By applying common sense to the cleansing problem, i.e. by attacking it incrementally and intelligently over time, mapping rules can be developed, refined, and applied when needed. In fact, whether you choose to have an initial spend dataset created by outside resources, or you decide to create it yourself, the conclusion is the same:
cleansing should be an online, ongoing process, guided by feedback and insight gleaned directly (and incestuously) from the powerful visibility tools of the spend analysis system itself. And, as a corollary, cleansing tools must be placed directly into the hands of purchasing professionals so that they can create and refine mappings on-the-fly, without any assistance from vendors or internal IT experts.

Next: Defining “Analysis”

Spend Analysis II: The Psychology of Analysis

Today I’d like to welcome back Eric Strovink of BIQ (acquired by Opera Solutions, rebranded ElectrifAI( who, as I indicated in part I of this series, is going to be authoring the first part of this series on next generation spend analysis and why it is more than just basic spend visibility. Much, much more!

Data analysis that should be performed is often avoided, because
it carries too much risk for the stakeholder. Let’s consider two examples.

(1) Suppose I am an insurance company CPO with access to one or more
analysts; and that some number of analyst hours are available to me,
in order to investigate savings ideas that occur to me from time to time.

Now, suppose I begin wondering whether the company’s current policy of
auctioning off totaled vehicles is wise. I reason: what if we’re
actually losing money on some of these wrecks? I think: perhaps there
is a closed-form sheet I can provide to my adjusters that lists make/model/year and gives them an auction/no auction decision; perhaps that sheet would save the company money.

My problem is that I’m not entirely sure that this idea is worthwhile.
Perhaps the company makes money on almost every auction, and I will waste the valuable time of one of my analysts by chasing phantom savings that aren’t there. I must weigh not only the cost of the analysts’ time, but also the lost opportunity cost associated with the analyst chasing a low-probability idea — against using that analyst for some immediately useful purpose, such as prettying up a report that the CEO complained about, or double-checking a number for the CFO.

I reason as follows: if I think it’s going to take longer than X hours
to determine whether this is a good idea or not, then I can’t chase the
idea. I don’t have the resources to do so, and perhaps I never will.

However, if I know that my analyst can load up a new spend dataset with
auction costs and revenues within minutes; and I know that a subsequent
slice/dice by make/model/year would be trivial; and I know
that a report of precisely the format I need could be produced without
significant effort; then the decision is a no-brainer. I make the decision
to analyze rather than the decision not to analyze.

(2) Suppose I am a CPO with a large A/P spend data warehouse available to me, but the particular question I want answered is not supported by the dimensions and hierarchies that it contains. Those dimensions and hierarchies were built perhaps by the IT department, or perhaps by a spend analysis vendor, or perhaps by a team of internal support people who are responsible for maintaining the warehouse; and those dimensions and hierarchies were the result of a number of committee decisions that will be difficult to alter. Furthermore, the data warehouse is being used by hundreds of other people in the organization — which means that I’ll need the permission of all those potential users to change or add anything.

I reason as follows: I know it will take weeks, perhaps months to convince
my colleagues to change the dataset organization, even if they can be
convinced to do so; and once they are convinced, it will take even longer
for whomever it is that controls the warehouse to implement the changes, perhaps at high cost that I will need to justify; so is it really worthwhile for me to pursue using the warehouse to answer my question?

I decide: probably not. Which means that my analyst will have to spend many hours extracting raw transactions from the warehouse; re-organizing them herself on her personal computer, using Access or other desktop tools; and then creating the report that I need. As above, I reason as follows: if I think it’s going to take longer than X hours to answer my question, then I’ll live without the answer rather than risk wasting precious analyst cycles.

However, if I know that my analyst can tweak her private copy of the dataset, adding dimensions and changing hierarchies in just a few minutes, and that my answer will be available shortly thereafter, I make the decision to analyze rather than the decision not to analyze.

A flexible and powerful spend analysis system can make a huge psychological
difference to an organization. It changes the analysis playing field
from “we just can’t afford to look into this” to “of course we should
look into this!”

Next installment: Common Sense Cleansing

Spend Analysis I: The Value Curve

Today I’d like to welcome Eric Strovink of BIQ (acquired by Opera Solutions, rebranded ElectrifAI) who, as I indicated in my There’s No Spend Analysis Without the Slice ‘N’ Dice post, is going to be authoring the first part of this series examining what is required for a true spend analysis system, spend analysis 2.0 if you are part of the 2.0 movement, as opposed to just a basic spend visibility system.

Spend Analysis has always suffered from what the late British humorist
Stephen Potter might have called the “So What Diathesis.” In other words,
now that you have your spending loaded and classified, what next? Well,
if you’ve never seen your purchasing data loaded into a spend analysis
system, you’re in for a treat, because you can find savings opportunities
just by drilling around. It’s often that easy — drill around; find
opportunities.

However, once the low-hanging fruit is harvested, which can take
anywhere from 6 to 12 months, the value of the spend analysis system
declines steeply — at which point Mr. Potter’s observation comes home
to roost. As illustrated below, there is a moment at which the cost of
the spend analysis system begins to exceed its ongoing value.

It is shortly after this time that (1) usage of the product drops to low
levels; (2) the rest of the organization begins to question the value of
the software; and (3) stakeholders come under pressure to justify continued
high expenditures.

That’s why it’s odd to hear people talk about “The Spending Cube” —
in capital letters — as though there were only one data cube ever
to be built. Actually, there are many different ways to look at spend,
and there’s lots of spend data that simply can’t be organized into a
single data cube anyway. How about a compliance cube, oriented around
invoice level data? A purchasing card cube, specific to p-card idiosyncrasies?
A T&E cube, built from travel agency data on “best price” versus
“actual price,” tracking employee travel and the reasons for the discrepancies?

In fact, it’s obvious to anyone who has worked with multiple datasets at
the A/P, PO, and invoice level that there are many, many different kinds
of data to analyze. Each dataset addresses more opportunity, and presents
another chance to apply a sophisticated analysis tool. Some of these
datasets aren’t “spending” datasets at all, but consist of demand-side
information — for example, cell phone or fleet vehicle usage records,
or operational data such as equipment recovery and maintenance logs.

If a spend analysis system makes it easy to load data and create new datasets,
which it should; and if the system supports as many datasets as you’d like,
as it ought; then there really isn’t any limit to how often the system can
be used, or to how many different kinds of data it can be applied. Which
means that a full-utilization spend analysis system value curve looks more
like this:

In other words, each use of the spend analysis system provides high
initial value, as well as residual value; but the system is used again
and again for new sets of data. The value of the spend analysis
software therefore remains high over time.

Next installment: The Psychology of Spend Analysis

There’s No Spend Analysis without the Slice ‘N’ Dice

When I was in Boston, I was lucky enough to spend the better part of the day with Eric Strovink of BIQ, and have a few extended conversations with individuals at some of the local consulting firms that specialize in sourcing, and am now more than convinced that any tool that mandates a single cube, or makes it difficult to change the cube, is not a spend analysis tool, merely a spend data warehouse with built in canned reporting (and, if you’re really lucky, limited ad-hoc capabilities).

Not that there’s anything wrong with a centralized spend warehouse with a consistent view of your total spend, especially one that integrates multiple internal and external data sources and allows you to drill down and understand your spend at a detailed level. Of all the e-Sourcing software tools, it is the one most likely to make your CFO do backflips, especially if it has good reporting (and this is a big if – not all spend analysis tools on the market do), since it makes it really easy for the CFO to tell the CEO where the money is going and comply with all those pesky reporting requirements.

However, the value of such a tool is quite limited to you as a purchasing agent. Now, it’s true that the first time you’ll use it you’ll save big-time, especially if it’s the first time you have visibility into the majority of your spend, but the reality is that this is the only time you’ll see such significant savings. After you’ve identified all of the low hanging fruit identified by the single view provided to you by the system, analyzed each instance of over-spending, and taken corrective actions, you’ll find that you’ll be unable to identify additional savings and the system will simply function as a glorified data warehouse that you only use once a quarter to create those reports for your CFO and check that your teammates our buying off the negotiated contracts – something that you could do almost as well with your existing ERP system and a significantly cheaper Business Intelligence / OLAP tool like Business Objects or COGNOS and some grunt work.

Remember, I’m not saying that traditional spend analysis systems like those provided by e-Sourcing providers like Procuri (acquired by Ariba, acquired by SAP) and Emptoris (acquired by IBM, sunset in 2017) are not without value – if you do not have a good, integrated, data warehouse that integrates your various accounting, purchasing, and inventory systems to provide you a single view of your spend or a good reporting system to produce all of the reports your CFO needs, then you’ll find these systems very valuable. However, it’s important that you understand that the primary value of these systems is in the total spend visibility they provide from a financial viewpoint, not the spend analysis capability you really require to identify potential overspending and cut-costs, because you’ll only be able to do this once – thanks to the single organizational view they are built on. (In other words, you’ll save big when you fist implement the system but future savings will be limited to your capability to quickly catch and stop maverick spend.) So, if you need a system to consolidate your spend data, produce the tedious reports required by all of the new financial reporting requirements, and give you some basic across-the-board spend visibility, or, more importantly, you need a spend data warehouse that integrates with the rest of your e-Sourcing suite, be sure to check these systems out – but understand what they are really worth to you before you sign the check.

In order to help you understand where these systems fail in true spend-analysis, why you need to be able to dynamically create multiple cubes on the fly which support dynamic dimensions, meta-aggregation, cross-dimensional roll-ups, and even federated data sets, I’m happy to inform you that Eric Strovink has agreed to co-author a series of posts outlining what real spend analysis is, how it differs from basic spend visibility, what it does for you, and why you need to get there. Stay tuned. (This series is bound to be as informative as my CombineNet series which, when combined with Paul’s informative posts and rebuttals, is probably one of the best non-marketing filtered sources of information out there on decision optimization.)