One of the foundational elements to Procurement is to have good access to data. However, it would seem that most Procurement departments struggle with spend analytics. Now, don't get me wrong, there are several companies that have a spend analytics tool, but do they actually have robust analytics that are trusted and leveraged for decision making?
So, where do we go wrong? Why is such a foundational element of Procurement so hard to tackle? (Yes, it is Saturday morning during football season!)
Let's start with technology. This does not seem to be the problem. There are plenty of tools out there, whether it is a specialized spend analytics solution or one of the great visual tools like Tableau, Power BI, etc.. The technology is able to manage huge data sets, create visualizations, and allow end users to interact with the data easily. In fact, if you want to do some fun reflection, think about how the technology around spend analytics has progressed vs many of the processes/practices of Procurement. Now that you are done laughing, we can move away from the spend analytics technology being the problem - or the solve.
So, this brings to me to the issue - getting crap faster. Yes, that is it. We are getting crap faster with most spend analytics technology implementations. Why? Because spend analytics is a process issue. Let's discuss a couple of examples.
1) The data itself. If you do not have the data in systems or have the ability to manage unstructured data effectively, the best tool does not do much for you. For instance, having low compliance to a Non Po No Pay policy will result in struggles in your data. Also, not having good master data management and governance will result in messy data. These items are not easy to fix. They require new policies, investments in people, new processes to be put in place. Dissecting the root cause in your data and driving process change will enable actual spend analytics. (Note: It does not need to be perfect, but it cannot be overlooked.)
2) Scope of the data. Building a spend cube is not too hard, especially for some of those millennials on your team. However, where there is glory in your data is relating it to other business data to solve business problems. How is the supplier's spend relating to other key business trends? How does the spend relate to the past RFP activity or upcoming contract expirations? To be problem solvers for the company, combining spend data with business data is critical. If you are only building a data set to see spend, you are falling short of the "analytics" part of the equation.
3) People. Do you have actual qualified individuals on your team that support analytics? Not reporting but true analytics? There is talent out there who received masters degrees in advanced analytics. Are you investing in them? How often do you get the same request for data within your team? I would guess rarely. Having the right people who can build ad hoc analytics, who can solve data problems, who know how to combine data with other business data are, in my opinion, a more important investment than even the technology.
4) Adaptability. Things change all the time. Company strategies, leadership, requirements for your data sets. There is a absolute need to have the ability to manage those changes yourself versus having to open an IT project for every change. First of all, that is expensive. Second of all, it is the number one killer of the sustainability of the analytics offerings. If the analytics platform (the data available and how it is used) cannot keep up with change, it will not be used or valuable any longer.
So, the next time you are hearing that your need a spend analytics technology and it will be a solve to your data and analytics needs, question why we still collectively struggle with spend analytics. The examples above are not easy to solve. Like my Father always tells me, "If it was easy, it would be done already". We can tackle this enigma if we think process vs technology.
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