The topic of data is like a boomerang. It always seems to come back into every discussion when we get into the nuts and bolts of implementing a digital transformation in procurement or even just implementing a software solution to get more visibility into spend analytics.
Problem is, investing in it often doesn’t yield a visible payback that can be directly tracked to a future P&L statement. So, how can data management ever become top of the agenda?
My guest today argues that a significant part of the solution is being able to tell the right story to the right people, to help them better understand the implications of NOT doing this.
The message is clear. At the point of putting together a business case and a budget appropriation request for implementing procurement tech, getting your data ducks in a row needs to be an integral part of the calculation.
Scott Taylor joins me on this week’s podcast to help explain why.
Why Implementing a Digital Procurement Solution Must Include A Data Strategy: Scott Taylor is The Data Whisperer
What problems does Scott see when it comes to the causes and the effects of poor or inconsistent data?
Master data – what are the different types of data in the procurement space and where do the common pitfalls tend to come from? And how can feeding garbage into a procurement tech solution impact your implementation of a digital transformation?
Why is data management an important component in the various different procurement initiatives that are buzzwords at the moment?
Scott explains his “4C” concept and how it helps businesses to understand their potential flaws in master data management.
If we assume that cleaning your data is something that’s a non-negotiable, I ask Scott how to approach the discussion with key decision makers. Specifically, how to pitch to CFOs to get buy-in to make the investment in something that doesn’t have an immediate, demonstrable payback.
Scott explains why if digital transformation is part the journey of where a company wants to go, it requires highly structured data to successfully reach this destination.
Why selling data cleaning as a stand-alone project is likely to fail, and how to make the case for including data management and structure in the budget of any large-scale procuretech investment.
When it comes to data management, do smaller businesses have the advantage over larger corporations because they have fewer legacy systems and less data to manage, or do the larger businesses have the upper hand because they have the resources and expertise to stay one step ahead?
As a final question, I ask Scott about whether he thinks data scientists will be an integral part of procurement organisations going forward.
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