On beyond Accounts Payable
Spend analysis works best when all the sources of spend are
consolidated into one consistent view. BIQ can be used very
successfully to integrate information from different accounting
systems (see Practical Solutions for Multiple Accounting Systems).
And, as we argue elsewhere (see Suite Silliness and
Where's the Analysis?), integration of a spend analysis with the ERP system
or with a sourcing suite is either ill-advised or illusory.
Spend analysis needs to sit independently on top of all
sources of spend data, mapping and otherwise altering them so that
solid spend visibility is achieved.
But what about other data in the enterprise?
With BIQ, you can build as many datasets as you like
(see One Spend Cube Is Never Enough). For example:
Invoice-level Analysis
Some leading theoreticians in the spend analysis
space landed 10 years ago at the Mitchell Madison Group.
They derived two different ways to analyze spend: (1) top-down,
through the A/P system, and (2) bottom-up, through invoice-level
BIQ users are analyzing data from sources quite different than the A/P system.
Some of it is spending data; much of it is not.
detail. The former approach is what has
evolved today into traditional spend analysis.
The invoice-level analysis approach was first employed
during an MMG engagement with a large commercial bank.
This bank had a burgeoning business in a foreign country,
but costs were spiraling out of control. The analysis team
entered several months' worth of invoice-level detail into
a database, and then poked at it with desktop tools. What
they found was opportunity: errors in invoicing, failure
to comply with contract pricing, and so on. Armed with
these data, the team went back to the bank's suppliers
and extracted rebates that paid for the study many times over.
That was then; this is now. Invoices are in much better
shape these days, and many suppliers can supply them
electronically. And, instead of the primitive tools applied
by the original analysis team, tools like BIQ can be brought
to bear on the problem with far greater efficacy. In fact,
individual BIQ cubes — by commodity — are often appropriate,
since some commodities have their own interesting flavor of
data, requiring different cuts at it. Commercial print data,
for example, is an enormously rich area for analysis, since
there are so many variables in a job (paper, ink, cuts, folds,
pre- and post-processing, press type, and so on).
P-card Analysis
Purchasing cards can be a rich source of transaction detail.
It's fascinating data, but analyzing it and classifying it is
a chore — and one certainly can't afford to dedicate a 6-figure
system to the task. Building a BIQ P-card dataset, though, is inexpensive and easy.
Insurance Claims Data
Another fruitful area for exploration is insurance claims data.
BIQ has already analyzed large datasets from claims systems, and
with its new 64-bit server capability, datasets of 100M+
transactions can be supported with reasonable response times.
Non-Spend Data
BIQ customers are analyzing non-spend as well as spend data. These applications include:
Call center data analysis. Most call center software systems are helpless to deal with the
enormous volume of transactional data generated, other than through the production of static reports and
summaries. And, call center system data are not integrated together with sales data, as is
critical, for example, in a telemarketing shop. Such integration is where high
value occurs — and integration of
data from disparate sources is BIQ's strong point
(see Practical Solutions for Multiple Accounting Systems).
So BIQ is being used not only to analyze the raw call center data, but also to
enrich it with sales information.
TV ratings analysis. Several customers are restructuring Nielsen data using
BIQ's analysis framework, with great results. Nielsen data are too large for spreadsheets,
but too complex for relational analysis. Inside BIQ's Viewer, relationships become easy
to spot, and analyses become easy to do.
HR data analysis. BIQ is being used to correlate HR data between multiple plant locations
to establish company-wide pay grades and define consistent bonus and benefits policies. BIQ's ability
to populate analysis spreadsheets with HR data, and to combine those data with survey results and other
input, results in high-value insights and actionable information.
