Getting Creative with Spend Analysis Datasets (Part 1)

This post is based on insights from the following Spend Matters Perspective: Spend Analysis -- Making Quantum Leaps: Exploring the Realm of Possibility and Untapped Savings with Three New Strategies.

Many of us who grew up with early spend analysis tools or hand-built Access databases filled with tons of great unwashed spend data are well aware of the challenges of assembling even a basic spend cube using legacy approaches. Clearly, first generation dedicated spend analysis tools and solutions -- including non-software components that incorporated a variety of data acquisition, cleansing, classification and refresh services -- lifted a tremendous burden from do-it-yourself types. Flash-forward to less than a decade later and it's no longer about getting to a single "spend" cube, but rather to create dozens of cubes in all kinds of areas. It's about bringing together a range of new datasets that include traditional sources of spend information (such as AP information) plus a range of additional data sources. For some time, simple AP spend cubes have incorporated relatively basic enrichment-type data, such as diversity status, contract status, payment terms, and purchase orders (POs).

We have also seen the augmentation of AP spend cubes with additional information such as p-card usage, AP duplicate payment data, budgeting/forecasting data, value added tax (VAT), goods and services tax (GST), motor vehicle sales tax (MVST), and payroll information. But perhaps most interesting in the evolution of spend analysis has been how organizations are starting to leverage invoice information across a wide variety of categories, either searching their records or requesting data from suppliers. These include new datasets in such areas as telecom (e.g., equipment, long distance, PBX activity, wireless including data/voice) and HR/contingent labor (e.g., temporary staff usage including clerical, administrative and staff augmentation including both pricing and operational data).

By developing invoice-level visibility into these areas, data analysts can rapidly develop insights to drive savings. From basic invoice/auditing to category strategy development -- ranging from getting all locations buying from an agreed-upon contract/rate- card to supplier rationalization and strategic sourcing – insight into invoice-level detail at the line level can quickly drive bottom line results that basic spend analysis (using standard AP cubes/ data-sets) fails to deliver across richer, more complex categories and services.

In Part 2 of this post, we'll examine additional to get creative with spend analysis data. And don't forget to download Spend Analysis -- Making Quantum Leaps: Exploring the Realm of Possibility and Untapped Savings with Three New Strategies, if you're interested in seeing a more comprehensive set of categories and related datasets for consideration.

- Jason Busch

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