Navigating Procurement Data: The Evolution of Spend Analytics (Part 1)

Discussing the future of procurement and spend analytics is one of the most exciting topics we think about these days. But it’s important to trace the evolution of data management and procurement to understand how we can reassemble various elements of current approaches to build next generation tools and approaches. In today’s analysis, we’ll look at the evolution of spend analytics over the past decade focusing on twelve individual elements/approaches, including fundamental ways to use tools as well as more recent innovations in the sector:

1)    Think about the importance of data consolidation across business units and suppliers to provide visibility on the maximum percentage of spend and related information. This was one of the first tactics that early spend analysis and supplier management systems could enable (and we still use these approaches as a guiding principle to gain visibility into spending and suppliers for reporting, planning, and compliance).

2)    Spend and supplier consolidation and planning has also brought the need to find ways of getting data into systems (beyond batch uploads). This has evolved from traditional ETL (extract, transfer, load) models to open APIs and integration frameworks/hubs, including integrations that take shape between organizations, not just between systems.

3)    Once organizations were able to consolidate data, the need for tools to automate the cleansing and classification has been essential given different data entry standards as well as simple typing mistakes that happen in data entry. More recently, cleansing and classification has also helped consolidate global data in cases where transliteration challenges (e.g., Arabic, Chinese) factor in. This is the realm of artificial intelligence (AI), machine learning and a variety of approaches.

4)    Reporting and basic data manipulation evolutions (i.e., how we work with information) has evolved from the use of spreadsheets, in which knowledge was hidden in columns and rows, through the progression to reporting (and automated reporting) and dashboards for both procurement and non-procurement professionals and the business overall. This democratization of data visibility is one of the most overlooked benefits of modern procurement and spend analytics.

5)    Quantitative and analysis tools for expert users have evolved dramatically, along with the creation of spend cubes and datasets on the fly to explore different opportunities. As we note in the paper, Spend Analysis – Making Quantum Leaps: Exploring the Realm of Possibility and Savings with Three New Strategies, “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. Further, as we note, “Canned reports find canned answers … get out of the reporting mindset and looking beyond standard, canned reports as a means to identify new savings opportunities.”

6)    Data visualization tools have also evolved dramatically in the past decade. Earlier spend analysis deployments featured standard reporting and dashboard capability. The most recent visualization tools take advantage not only of greater capabilities (e.g., ability to drill into data on a granular basis) but new types of design elements and structure, including the ability to gain access to and manipulate information in tablet and mobile environments (e.g., with a finger swipe instead of a key stroke). For a great overview of highly diverse design approaches to visualization, check out this article from Smashing Magazine (as well as some of the graphics below that Ryder provided).

Click images to enlarge.

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Stay tuned for Part 2 of our overview of the evolution of spend analytics as we look at:

  • Exploring data in context concert
  • Basic and advanced data enrichment approaches
  • Consolidation of spending and related SKU or (non-SKU) based information with broader supplier master data
  • Multi-tier visibility
  • Scalability of systems to support big data
  • Consumerization of IT


This post was co-authored by Jason Busch and Ryder Daniels.

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