As you have probably figured out by the large number of postings to date on spend analysis, a strong understanding of the data behind spending is pivotal to the proper identification and management of your spend initiatives. Here are some key questions to ask, and answers you should be expecting.
1. How much flexibility do I have in spend cube creation?
Spend analysis is more than just one A/P level spend cube. It’s many cubes. It’s multiple cubes by supplier by commodity for contract compliance. It’s cubes for purposes outside “normal” spend analysis. For example, this could include an analysis of transactional data not necessarily related to spend per se, such as cell phone usage patterns or help desk support calls, but any data where an improved understanding can lead to process improvements which impact organizational spend. It’s cubes for throw-away analysis, cubes that are derivative of other cubes, and so on.
So, you need to be able to build your own cubes, modify your own cubes, and, in many cases, map your own cubes*. This needs to be easy and fast, so that your analysts spend their time analyzing the data, not wrestling with the data for days or weeks before the analysis can even be started.
* The exception is when you know the data has been properly mapped by an expert in the data source you are using. However, this is not the normal situation at most companies, especially in commodity and indirect spend, and you will find that you have to do mapping the majority of the time.
2. How should I deploy spend analysis?
It doesn’t make sense to share a spend cube between your analysts, unless you want to set up a UFC Grudge Match in the hallway to decide who gets to implement his/her changes next. Unlike the central data warehouse, analysis cubes need to be private, not public (although sharing public cubes can be useful for casual informational purposes). The popular notion of a central data warehouse as the basic ingredient for detailed data analysis is wrong, and that’s why there’s so much unhappiness among data analysts with both BI systems and with the majority of spend analysis systems today.
3. What if I am resource constrained, and I need to outsource spend cube services?
Make sure that if you outsource cube development to the vendor, or a third party that works with the vendor, you can do it in such a way that your own people get trained along the way, so they can take over at any time. With the exception of the more complex direct spend categories, which require complex bill of materials and engineering-specific knowledge to properly map, most spend isn’t that hard to map, and this is especially true in the purchase of commodities. Furthermore, you need to make sure there’s more than one source for services with the spend analysis system you select. This is not only because you might want to throw the services business out to bid, but because you don’t want to be waiting on a resource constrained vendor every time you have spend to map and need help. (Let’s face it, just because you should be able to do mapping on your own, this doesn’t mean you’ll have the skills or insight to be able to do it the first time without some help.)
4. How much reporting flexibility do I have?
Reporting is where the rubber meets the road, and despite marketing noise to the contrary, no set of static reports will get you past the first corner. If your analysts are downloading transactions to their desktops in order to construct a report or conduct an analysis, that should be the first clue that your spend “analysis” system isn’t an “analysis” system at all.
It should be possible for your analysts to construct new reports and models easily and quickly, and they shouldn’t have to be IT experts. After all, that’s the whole point of spend analysis!
5. What should I know about data cleansing?
Cleansing is a term that involves “classifying” like items together (for example, multiple entries of “IBM” in the vendor master) and “mapping” spending to a useful sourcing commodity hierarchy. Classification is mostly the elimination of redundant vendor entries, although when collecting spend from multiple sources, it can include the creation of over-arching General Ledger and Cost Center categories. The spend analysis vendor should provide hierarchy classification tools that make the classification process simple.
Some spend analysis vendors make a big deal about classification, but 95-97% of the problem is redundant entries, not issues such as “Lotus” being owned by “IBM.” You’ll find that in most cases your own commodity managers are well aware of who owns whom, and don’t need any help in this area; but if you’re still doubtful, there are third party vendors who will create a who-owns-whom hierarchy out of your Vendor Master for $0.10 to $0.15 per line item.
Spend analysis vendors also make a big deal about mapping, but that process is also straightforward in the majority of commodity and indirect spend categories. (Direct spend categories, where you have to create hierarchical bill of materials that allow you to determine the impacts of raw material or labor cost increases and to perform make-vs-buy analysis, can be quite involved, but unless you are a manufacturing organization, this is not the norm.)
Make sure your spend analysis system supports an overlay-type mapping scheme that allows you to prioritize mapping rules or mapping rule groups in a reasonable way. Prioritizing rules is important, because it allows you to apply basic engineering principles (the famous 80-20 rule) to mapping your spending. The idea is to organize your rules such that each successive rule group maps more and more specifically, but also so that each successive rule group can focus on a smaller and smaller number of transactions. Using simple techniques that are widely published and well known, you can be up and running with a 90% spending map in just a few days. (And this is usually more than enough to allow you to perform initial analysis to find key categories to focus on as part of your first set of spend management initiatives after acquiring a real spend analysis system.)
You should also ensure that the vendor provides a way to map free-form text descriptions. These can be helpful in cases where there is little or no useful information in terms of supplier or GL coding in commodity and indirect categories or missing engineering classification codes in direct categories.
6. Does the tool support derived and ranged dimensions?
A good tool will not only support various time periods, such as day, week, month, quarter, and year, and time period – over – time period analyses, such as month-over-month, quarter-over-quarter, and year-over-year analyses, but will also support other types of ranged dimensions, such as spend size (that will allow you to bucket your suppliers for a commodity into small, medium, and large spending buckets by dollar volume) and risk level (that will allow you to group your suppliers into low, medium, and high risk buckets based on derived risk factors).
7. Can the user fix any set of filters they choose while pivoting and drilling down into reports?
This might not sound that important, but when trying to figure out why a certain spend category is 2M over last year, when an initiative expected to reduce costs by 10% was undertaken, can be difficult if you can’t find the key source of the problem. For example, let’s say you, as the telecommunications sourcing professional at a large national organization with hundreds of locations, decided to switch long distance carriers. If all divisions and business units implemented the change, then costs should be less, not more (unless everyone is calling significantly more than expected). However, let’s say that IT and HR didn’t switch at ten of your largest locations. With a dozen divisions, and hundreds of locations, it could be difficult to determine this unless you can drill into the data, fixing divisions and units at each step, and find out that 30 intersections of division and business unit are spending more than last year. Then, drilling into each you find that 15 of these are still paying, and thus using, the wrong carrier. However, if you can’t fix multiple dimensions, or apply filters that achieve the same effect, you might only be able to figure out that IT is spending more – and then you might have to call 50 locations to figure out which ones haven’t switched. Flexibility in the analysis and reports is key!
8. What if I have multiple accounting systems?
This is actually excellent news, because you are likely to have huge opportunities for savings, given that those systems probably haven’t been combined in any reasonable way before for spend and procurement analysis.
The key for spend analysis is to ensure that the vendor provides an effective tool for translating (the “T” in the “ETL” acronym) files from one format to another. As with the other spend analysis system tools, this tool must also be accessible to, and usable by, your business analysts. You should never let yourself be at the mercy of IT or a spend analysis vendor when trying to analyze new data sources, or when merging new data sources into an existing cube.
With independence comes power; and ensuring that your analysts can control their own data processing and reporting is the key to spend analysis success. This brings us to our last question.
9. How easy is it to get data in and out of the system?
Importing data should be a piece of cake. It should simply be a matter of pointing the system at the appropriate file or URL connection, specifying the dimensions of the records to import, and pressing “import” to get the data in. Then, as pointed out in the last question, transformation and mapping should be easy for even a junior business analyst.
In addition, since the goal of spend analysis is to identify spend reduction projects, it should be easy to get the data out that you need to not only create a spend project in your sourcing system, and track historical costs, but justify the project’s creation.