Where does ‘Big Data’ fit into Procurement?

I spent about a year working as an Energy Analyst in Procurement at a large Telecommunications company. I’m by no means an expert but these are my own thoughts on where I feel ‘big data’ fits into procurement.

Firstly for the stake of this argument let us consider procurement as a the purchase of goods for the rest of a large company – and fundamentally it is a cost-control function for a business. These are some ideas of where ‘big data’ can fit in a procurement organization. It is by no means exhaustive.

  1. Tools for supporting pricing information. I worked on tools like this in the past, but getting good pricing information helps you benchmark your performance. This is really important if your prices are subject to markets like energy markets or commodity markets.
  2. Machine learning for recognizing contracts – lots of procurement is about dealing with contracts – one could apply natural language processing to finding similar contracts or similar documents. This could be invaluable for lowering costs in organizations.
  3. Total Cost Modelling – when you analyse a complex item in a supply chain like a
    phone mast – you’ll find a number of residual parts such as steel, batteries, etc etc. For services this gets even more complicated because of the nature and lack of visibility of the costs. One can leverage applied statistics and monte-carlo simulations to help better understand the natures of these variable costs, and better model your total cost of ownership.


Since traditional methods for reducing costs are fast evaporating, CPOs (Chief Procurement Officers) should increase the time and effort invested in total cost modelling. In doing so, they will not only inform internal decisions, but also deliver to procurement an opportunity to drive strategy, thereby developing the top line impact modern businesses desire from them.

When it comes to practicalities, building an analytics capability has to start with a definition of the problem and a clear understanding of the boundary conditions. Limiting procurement’s scope by simply working with the data that is easily available will also limit the outcomes. CPOs need to contemplate the relationships between data sources and data points and look for indications of likely trends without direct access to ‘proof’ data.

Particularly of interest to procurement professionals will be the deluge of information from the ‘internet of things’. However this data needs good governance (it needs to be fit for purpose) and good analysis to take advantage of. We’ll talk more about such things in the future.

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