Data driven Procurement

Author: Alejandro Basterrechea (22.08.2020)

Becoming a data driven procurement organisation: from knowing what happened, to knowing when it happens, to knowing before it happens

Procurement has the unique position of being the connector, the network integrator but above all to become the source of key information to enable fact based decision making. 

This article is not about data analytics, big data or analytics technology, but about raising the bar for procurement, changing the paradigm that procurement only focuses on savings or transactions. It's about taking the advantage of the unique opportunity of generating value through data and being the spider in the web.

Regardless of the maturity level, industry, size, system landscape, geographical footprint or any other factor affecting your procurement organisation, there is one thing all procurement teams have in common: we all sit on a goldmine. 

The goldmine is the vast amount of data and information procurement has access to. Probably more than any other function in a company. In other words and as mentioned by Steve Martin’s article and I quote “without question, the 21st century’s most valuable commodity is data – a resource that is growing at an exponential rate” (Source: Data – the World’s New Precious Commodity). 

Being able to provide management and stakeholders with the right visibility to enable them steer their business, puts Procurement in a position of a trusted advisor and a strategic business partner.

Information is knowledge, knowledge is power

Procurement has access to data and information in two major dimensions: internal and external. In addition, the more integrated procurement is within the business, the wider the spectrum of information. 

However, the value doesn't come from accessing the data, but transforming data into valuable information that enables smart accurate decisions. It includes context, business understanding and analytical thinking.

Where to start?

Here is where technology plays a key role, since processing vast amounts of data from different sources and reducing complexity while acting fast is nearly impossible for human beings alone. Therefore, you need to be in a position to answer at least this 10 questions:

1. What does your system architecture look like?
2. How is your data ecosystem?
3. What is your single source of truth?
4. What is the level of data accuracy that you have?
5. Who is your audience?
6. Do you measure some KPIs already?
7. What is your current technology landscape for processing data?
8. What are your internal and external sources?
9. What is the level of skills in your organisation to be able to manage the data?
10. Do you have a data driven team culture?

Once you know where you are, you can define your starting point and the road ahead.

The generation of value comes from putting all the pieces together and delivering information that is:

• Accurate: people trust the numbers
• Reliable: they are constantly and continuously accurate
• Automated: some effort to set up, low effort to maintain
• Efficient: fast, easy, no army of analyst needed
• Self explanatory: does not require a scientist to explain, any buyer can understand
• Accessible: easy to find, mobile, self service
• Insightful: looking beyond the surface, find the detailed information no one else sees
• Foresightful: looking beyond now, ability to predict or even prescribe
• Contextual: understanding the business environment of the information

How to create a data analytics roadmap?

According to McKinsey, such a roadmap can be described in 3 main stages:

1. Data Management: looks into the past and present. Mostly focusing on data accuracy and reliability. Increase in standardisation, automation and efficiency is important.
2. Standard Analytics: more insightful and preventive (e.g. alerts). It applies forecasting trends and statistical analysis.
3. Advanced Analytics: it's about foresight, simulation modelling, predictive and prescriptive analytics

(Source: Article from 20.04.2018, How advanced analytics can benefit infrastructure capital planning by John Levene, Sacha Litman, Ian Schillinger, and Chris Toomey by McKinsey & Co.)

Based on McKinsey's effort matrix, Procurement can derive a roadmap and technological landscape.

(Example for visualisation purpose)

Building a data driven organisation

• How do we ensure our organisation fully utilise the advantages and data becomes part of our DNA?
• Do our customers recognise us as strategic advisors for fact based decision making?
• How do we balance between a data jungle and agile/fast decision making? 

It starts with leadership. If decision making requires facts and data at the very top, organisations will follow. But data alone doesn't mean you will make smart decisions, it requires a certain degree of intuition, experience, logical thinking and common sense. 

First you need to establish a culture of fact based arguments. No strategy, negotiation, discussion can take place with a full set of facts that supports a proposal or argument. This has to be embedded in every team meeting, negotiation preparation, project definition or commodity strategy. Any project status has to have the right set of KPIs or metrics. Any team discussion and decision has to have a fact based argumentation and decision has to be supported by reliable and accurate data. Only when you establish such a culture within the team, the team will go out and spread this with their customers or stakeholders.

Second, it is important that every leader has a data driven mindset. When a leader drives a data driven organisation, the leader must be in a position to ask the right questions, answer with facts, be egoless about being wrong if the facts show otherwise and most important, be fearless about the truth a data set might tell about its organisation.

Third, you need to start somewhere. Having the right KPIs or metrics is key to drive an organisation. However, organisations evolve, and therefore, adapting KPIs or metrics from time to time will become necessary. Finding the right balance between purely KPI driven vs deep dives in certain areas is important. Just KPIs do not tell the full picture, plus it could be the snapshot of a moment. An advanced analytics organisation looks beyond purely industry standard KPIs or Benchmarks. If you think, you are not measuring the right things, eliminate those metrics, try new ones, fail, correct and continue.

Fourth, invest in the right technology at the right time. In some cases you can leapfrog the technology evolution, but it will depend on the complexity and priorities of your organisation. However, the return of investment of knowing something is always higher than the return of investment of knowing nothing. Your suppliers, your customers, your competitors are already investing in technology.

Fifth, people will become the most important driver of a data driven organisation. Developing the right skills and hiring the right talent is key. The buyer of the future will rely more on its analytics skills than on its negotiation skills. AI-driven negotiation algorithms might become better negotiators than humans over time. But having context, knowing the business requirements, understanding human needs, having a network and being able to process the right data, it's what the buyer of the future will require.

Finally, customer obsession is key. We deliver content that understands the business context of our customers and gives them the information required to make strategic fact based decisions for their business. We need to understand what is needed at the right time and the right place.

Additional recommended articles:

https://www.learnbigdatatools.com/big-data-analytics-landscape-2019/

https://www.mckinsey.com/business-functions/operations/our-insights/the-era-of-advanced-analytics-in-procurement-has-begun

https://www.rolandberger.com/en/Insights/Global-Topics/Procurement-Endgame/

https://offers.evalueserve.com/white-papers/beyond-spend-analytics

https://www.de.kearney.com/procurement/article?/a/the-future-of-procurement-rediscovering-the-fundamentals

Note: all content represents my own personal experience as well as observations and discussions with peers; and expresses my personal opinion on this matter.