Guest Blog

Part I: The Deficiencies of Descriptive Analytics

In this 6-part series, guest blog writer Michael Lamoureux, a freelance procurement expert, explores and takes a deep dive into spend forecasting. With years of deep domain craft in sourcing and supply chain, he shares the secrets of what it takes to keep up in today's fast moving economy. Read Part 2, Part 3, Part 4, Part 5, Part 6.

Analytics is the new hotness. But it's not the same old story that you might remember from twenty years ago when Business Intelligence was all the rage or the same old story from ten years ago when spend analysis first hit the market big time. This is because modern analytics systems have left the age of static reporting on stale OLAP cubes and entered into an age of dynamic reporting on real-time cubes. Why is this important?

To answer the question, we have to first explain why descriptive analytics, once the revolutionary offering of business intelligence systems, is no longer sufficient in today's fast-moving economy. After all, if the technology was good enough for almost a decade, why isn't it good enough today?

When they were first released, first generation Business Intelligence tools like Cognos and Business Objects were revolutionary. For the first time, an organization could build a complete OLAP data cube on its spend data or performance data, organized to its liking, and then create a cross-section of views on this data that would allow it to get complete insight into its spend, or a subset of its organizational performance data, for a quarter or a year. Or it could build a cube on organization-wide on-time delivery performance on the supply side and sell side, determining not only how often it failed to meet its delivery, but how often late deliveries on the sell side corresponded to late deliveries on the buy side. And so on.

In the first case, you could take last year's invoice data, build a cube that contained the spend amount, the products, the volume, supplier, and location data, and then roll it up by category, geography, and quarter, and see not only the total spend but spend patters for the year across the entire organization and, for the first time, do strategic sourcing with complete and accurate spend and volume data on the categories being sourced. And, you could get this insight typically within a week or two of getting all of the data for the prior year in the data warehouse.

Generally speaking, once the data warehouse schema was defined, an organization could export the data from all of the source systems it used in its global operation and get the data properly structured and appropriately normalized and categorized in the data warehouse within a few months.

For an average organization limited to an ERP and Excel, this was a light year advancement in technology. Before these systems, analysts would have to take exports from all of the source systems, slice and dice them into categories, clean them up and normalize the data in each category by hand, and then assemble one sheet per category for each quarter or year (depending on how many transactions there were as Excel had a 1M row limit per sheet) and then create workbooks with roll up summaries by quarter or year by category. An analyst could slave away with the data for months before she had what she needed just to identify the strategic sourcing opportunities within a single category.

But now an organization could see their top categories, their top products and services within each category, and their top suppliers and how the spend breaks down by department and geography, and slice 'n' dice it across these dimensions in near real time on their global spend. They could see how it changed quarter over quarter and month over month, infer some trends that were only a few months out of date, and identify the top categories where there was a majority of spend not sourced to a few key suppliers.

Similarly, they could also identify those categories thought to be strategically sourced to a few suppliers that were actually sourced from a large number of (off-contract) suppliers and those categories where spend was much higher than initially thought. It was a great time to be a sourcing professional. Until it wasn't.

Once an organization worked its way through its top N categories that represented 80% of the spend, signed contracts with the top M suppliers that provided 60% to 80% of the products in the top N categories, identified the biggest spending departments and worked with the lead buyers in those departments to teach them sourcing best practices, they hit a brick wall.

If they wanted to dive into the next N categories and the next M suppliers, look at ways to redefine categories and supplier segmentation to create new opportunities, or redefine geographies for greater local negotiating power, they were out of luck. This would require defining a new OLAP cube, re-mapping and re-categorizing the data by hand, defining new views and reports, and basically starting from scratch. As with the construction of the annual cube, this will take months and by the time it's done, the time left to do anything about it will be minimal as it will soon be time to build next year's standard spend cube and begin the annual process all over again.

And this is the major deficiency of descriptive analytics systems. All they tell you was what was. Not what is. And definitely not what could be as the data is too far out of date to be useful. So, what's the answer? That's what we'll attempt to provide in this series.


About the Guest Writer

Michael Lamoureux, aka the doctor, is the Editor-in-Chief of Sourcing Innovation (.com), a resource for sourcing, procurement, and supply chain professionals who are interested in improving themselves and the overall performance of their organizations. A regular contributor to Spend Matters, he is a Computer Science PhD who has been heavily involved in the Sourcing and Supply Chain Space since 2000 and the e-Commerce space since 1997. As a freelance procurement consultant with extensive expertise in sourcing, procurement, and supply chain processes, he aims to continually push innovation in and beyond the supply chain space. With particular expertise in analytics, modeling, and optimization, he is able to dive much deeper into technology and core issues, striving to help businesses with their internal knowledge transfer, positioning, and planning problems.  

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