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2020 The Year To Address The Elephant in Every Room – Data



North Carolina State Poole College of Management recently produced this 28 page report entitled “2019 Supply Chain Data Quality and Governance Study”.  The report contains some great illustrations of the current state of Data governance across the organizations that were surveyed, with a specific focus on the supply chain.

In this blog, I have attempted to extrapolate some of the main points shared by those who are winning, or at least, not losing the war on bad data.

Getting Data right is an imperative

In the report 60% of CPOS select poor data quality, standardization and governance as being the biggest challenge in mastering digital complexity.  In another recent report, Gartner predicted that by 2023, organizations that don’t really tackle the issue of supplier master data management will very likely have the wrong information for half of their suppliers.  When you think that many CPOs are looking to Supply Chain professionals in their organizations for data which will be used to make strategic decisions on AI, IOT, blockchain and contract automation, there is definitely a compelling reason to get this right.  But it’s not easy.

Blog - Overall Supply Chain Data Quality - Capture1

Source: 2019 Supply Chain Data Quality and Governance Study

Data Governance means that an organization can ensure that high data quality is the norm across the complete lifecycle of the data.  This means no siloes. Having data standards and investing in improving the skills of the whole team in regards to managing and interpreting data. 

There’s nothing wrong with Excel, but if you are aiming higher than “just getting by”, you need something more sophisticated.  There is definitely an aspiration to reach the point where data can be used as a matter of course to influence strategic decisions, but of course, excel is not the best tool for this job.

According to Spend Matters’ Pierre Mitchell in 2019 “over 40% of senior Procurement leaders have been pursuing advanced digital technologies (AI, RPA, blockchain and others) even though they’ve not always tackled the basics!”  all of these projects and blockchain and contract automation are all dependent on getting the foundations of data quality and governance right

To improve supply chain data quality, organizations have recognized that although data governance is “difficult,” it is an imperative. Some of the major steps to improve data quality include:

  • Standardize and automate the process of supplier data capture and maintenance.
  • Cleanse and enrich existing and new data, and harmonize across systems.
  • Capture additional supplier details through forms/assessments/surveys to drive more  complete information as well as improved compliance, risk management and communication.
  • Provide an enterprise view of clear, comprehensive and accurate supplier information.
  • Provide training to suppliers so they can improve data quality and consistency

Source: 2019 Supply Chain Data Quality and Governance Study

The “future state” around improving supply chain data involves leadership recognizing the goal of what a quality supply chain data will look like, as depicted below:

Blog - Depiction of leadership recognizing the goal of what a quality supply chain data will look like - Capture2

Source: 2019 Supply Chain Data Quality and Governance Study

Having quality data is of course the first step, having the talent within the organizations to take advantage of that data and turn it into insights and valuable analytics is another.

Blog - chart: 2019 desire for all staff member to have ability to analyze data 59% yes; 41% no - Capture3

Source: 2019 Supply Chain Data Quality and Governance Study 

One executive interviewed as part of the study, on the topic of data skills, said:

“We were focused on creating a higher ‘digital IQ’ across all business functions. Because Procurement had started the earliest, they were at the eighth-grade level, while everyone else was at the third-grade level – and the goal was to get everyone to high school! Creating a data platform was key for us – we were worried that if we just put a bunch of tools out there, it would be a free-for-all – and there would be 10 people working on different apps to solve a common working capital problem. To minimize waste, we wanted to get control of the analytics development process and seek to solve problems as a standard for the whole company. Our goal was not to move to ‘Excel on steroids,’ but gain some level of control. To create a more robust BI capability, we couldn’t leave this in the hands of data scientists. Everyone brought unique perspectives and we needed to synthesize all of the different views into a common understanding. This is the vision we sold to the CFO”

 What is clear is that only organizations who take Data Governance and their data assets as seriously as they do their physical assets, and who build a data strategy for the entire organization will really see benefits.  Organizations where there is a centralized supply chain structure (59%) seem to be on the right course as do organizations with a separate data governance organization. In 2018 the study showed that, on average, 37% of the responding organizations had a separate data governance organization. That number increased to 54% in 2019. 

The report concludes with a few recommendations for CPOS to continue to conduct data audits and to create and share master data dictionaries within companies and amongst the supply chain network.  Clearly this is not the last word on Data governance but 2020 does needs to be the year for a quantum shift if we are to see the benefit of the innovations which are on every CPOs to do list.



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