Today’s post is by Eric Strovink of BIQ.

I’ve suggested previously in this series that analysis doesn’t have to be done by an applied mathematician; the key is to get insights about data. Sometimes those insights do require rigorous statistical analysis or modeling, to be sure. Much more often, though, one simply needs to examine the laundry, and the dirty socks stand out without any mathematical legerdemain.

Examining the laundry requires data manipulation. This usually takes the form of data warehousing, i.e. classic database management technology, extended in the case of transactional data to OLAP (“Online Analytical Processing”), SQL and or MDX, and reporting languages and tools. Problem is, business data analysts typically have insufficient IT skills to wield these tools effectively; and when they do have the skill, they seldom have the time. Thus, ad hoc analysis of data remains largely aspirational.

Custom data warehouses have value for organizations. ERP systems are a good example. But the data warehouse is a dangerous partner. It is not the source of all wisdom. It cannot possibly contain all the useful data in the enterprise. Warehouse vendors have trouble admitting this. For example, for years ERP sales types claimed that all spending was already tracked and controlled by the ERP system, so there was no need for a specialized third-party “spend analysis” system. These days all the major ERP vendors offer bolt-on spend analysis.

Spend analysis has the same issue. It introduces another static data warehouse, an OLAP data warehouse, along with data mapping tools that are typically not provided to the end user. As above, the data warehouse is a dangerous partner. It is not the source of all wisdom. It cannot possibly contain all the useful spend data in the enterprise. Spend analysis is not just A/P analysis; it can’t be done with just one dataset; and it’s not a set of static reports.

Once an opportunity is identified, more analysis is required to decide how to award business optimally. The Holy Grail of sourcing optimization has been a tool that is approachable for business users; but this goal has proved to be elusive. The good news is that “guided optimization” is now available from multiple vendors at reasonable price points. Although optimists (mostly experts at optimization) have argued for several years now that optimization is easy enough for end users without guidance, I take the practical view that it doesn’t really matter whether that’s true or not. As long as optimization is available at a reasonable price, whether it has a services component or not, the savings it delivers are worthwhile.

By no means is this series an exhaustive review of data analysis. For example, interesting technical advances such as Predictive Model Markup Language (PMML) are enabling predictive analytics to be bundled into everyday business processes. Scenario analysis is also a powerful tool for painting a picture of potential futures based on changes in behavior. But the vendors of these technologies either must make them accessible to end users, or offer affordable services around them. Otherwise they will remain exotic and inaccessible.

The bottom line is that analysis tools must be accessible to end users. It must be easy and fast to build datasets and gain insight from them. Optimization software should automatically perform sensitivity analysis for you, as the doctor has advocated. Ad hoc analysis should be the rule, not the exception. Analysis should not require vendor or IT support; if it does, it likely won’t happen.

The more you look, the more savings you will find; and when you walk into the CFO’s office waving a check, you will get attention as well as the resources to find even more.

Previous: Analytics V: Spend “Analysis”

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Today’s post is by Eric Strovink of BIQ.

As an engineer who originally entered the supply management space in 2001 to build a new spend analysis system, over the last 9 years I’ve watched marketing departments consistently “dumb down” the original broad and exciting definition of spend analysis that I remember from those days, to something really quite ordinary. For example, here are the steps required for classic data warehousing:

1. Define a database schema and a set of standard reports (once, or rarely)
2. Gather and transform data such that it matches the schema
3. Load the transformed data into the database
4. Publish to the user base
5. Repeat steps 2-4 for life of warehouse

And here are the steps required for what has come to be termed “spend analysis”:

1. Define a database schema and a set of standard reports (once, or rarely)
2. Gather and transform data such that it matches the schema
3. Load the transformed data into the database
4. Group and map the data via a rules engine
5. Publish to the user base
6. Repeat steps 2-5 for life of warehouse

Not much difference.

You might ask, how can spend analysis vendors compete with each other, when the steps are so simple, and when commodity technologies such as commercial OLAP databases, commercial OLAP viewers, and commercial OLAP reporting engines can be brought to bear on any data warehouse? Well, it’s been tough, and it’s especially tough now that ERP vendors are joining the fun, but they compete in several ways:

• Our step 4 is better [than those other guys’ step 4].
• [briefly, until it failed the laugh test] Our static reports are so insightful that you don’t even need anyone on staff any more.
• [suite vendors’ (tired) mantra] “Integration” with other modules
• “Enrichment” of the spend dataset with MWBE data, supplier scoring on various criteria, and any other ways that might exist to try to add checklist features for analysts that may broaden interest in the spend analysis dataset beyond simple visibility.

It’s all very discouraging, but the doctor and I will continue to point out that spend analysis is not just A/P analysis; it can’t be done with just one dataset; and it’s not a set of static reports or a dopey dashboard, even though some vendors and IT departments would like to think it is. Spend analysis is a data analysis problem just like any other data analysis problem, and it requires extensible and user-friendly tools that empower people to explore their data for opportunities without third-party assistance. Those data come from multiple sources, not just the A/P system; many datasets will need to be built and analyzed; and from them, hugely important lessons will be learned.

The above notwithstanding, building a single A/P spend cube is a useful exercise. If you’ve never done it before, you will find things that will save you money. But that’s just the tip of the iceberg.

Previous: Analytics IV: OLAP: The Imperfect Answer

Next: Analytics VI: Conclusion

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