Today I’d like to welcome back Eric Strovink of BIQ 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