6 most painful mistakes in spend analysis

SammeliSammalkorpi.jpegWhile procurement leaders increasingly turn spend data into a competitive advantage, laggards continue to repeat painful mistakes.

You may have noticed that spend analytics is a hot topic right now. According to Deloitte’s Global Chief Procurement Officer Survey 2018, the majority of CPOs across the world expect analytics to have the greatest bearing on procurement over the next two years. As a provider of spend analytics software, Sievo has been busy and has seen more interest than ever from both private and public organisations to get their spend data into shape.

As leading procurement organisations find new opportunities to turn spend data into value, others repeat the same painful mistakes. Sammeli Sammalkorpi, VP: Customers and Co-founder of procurement analytics solutions provider Sievo, highlights the top six mistakes that you should avoid making in your spend analysis projects.

#1: You can’t manage what you can’t measure
We still come across far too many organisations that don’t have full visibility in terms of their procurement spend. Some may have information technology limitations in respect of the software that they use and some may lack the in-house skills to combine spend from different source systems. Having many enterprise resource planning and sourcing systems is no excuse: you simply can’t manage what you can’t measure.

#2: Bad data is no better than no data
Another common mistake we see is that smart procurement professionals need to rely on unreliable spend data. The room for error is especially high if you still conduct your spend analysis in Excel or infrequently-done manual spend cubes. Rarely can you make good decisions based on bad data.

#3: Don’t rely on the United Nations to classify your data
You may have heard of the United Nations Standard Products and Services Code (UNSPSC), a taxonomy standard developed by the UN that is still commonly used to classify products and services in procurement. While a universal standard taxonomy is an admirable vision, most complex organisations have a far better understanding of their own spend. You thus know your own business better than the UN.

#4: Analytics is not just about fancy graphs and dashboards
Thanks to the wide adoption of business intelligence (BI) tools, analytics has become widely engrained across most organisations. Still, it makes me shiver when I hear the question “can’t we just do this in Tableau or Power BI?” Analytics is much more than graphs and dashboards. In spend analysis, 90% of the challenge comes from getting the right data and insights in view. Once you’ve mastered the data, the graphs are just window-dressing.

#5: Don’t just look back at past spend data
For too many organisations, spend analysis is a reactive task, only looking at past spend performance. Leading organisations, in turn, take a forward-looking approach to spend, for example, through procurement spend forecasting or by proactively benchmarking procurement performance against peers or market indices.

#6: Letting humans do what machines can do better
At Sievo, we’ve made more advances in machine learning spend classification in the last 12 months than we’ve made in the previous 14 years in manual spend classification. We still believe that experienced procurement professionals need to be at the helm of spend classification, however, we also see the enormous potential to teach machines to take care of repetitive, complex and time-consuming processes.

But, that’s enough about dwelling on mistakes. If you’d like to hear about things that you could do right in spend analysis, reach out to friends at Cloudia for an introduction to Sievo’s spend analysis solutions. We’ve always got time for friends of friends, and you may find we’ve got even more in common!

Sammeli Sammalkorpi is the Co-founder of Sievo. His primary focus is to ensure that Sievo’s customers can turn their procurement data into realised business value.


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