The Importance of High Quality Spend Data

Published on
September 21, 2021 at 9:00:00 AM PDT September 21, 2021 at 9:00:00 AM PDTst, September 21, 2021 at 9:00:00 AM PDT

More than ever before, we are in an age where #Procurement demands the following:

·      High Quality Data              ·      Savings via Cost Reduction or Avoidance

·      Digital Transformation       ·      Agility


Without clean high-quality data, it would be hard-pressed to say that any #ProcurementOrganization could effectively and successfully achieve anything except small wins. Clean data is the driving force for all high-performing decision makers – and yet this seemingly simple idea is continuously brought up year after year. It’s nearly impossible to have anything but a birds-eye view of your spend if the quality of your spend data is poor....so wave goodbye to #savings and #DigitalTransformation, and if you think your procurement organization is agile without verifying first that your spend data is clean....your head is either in the clouds or in the sand.



In April 2021, Deloitte released its “2021 Chief Procurement Officer Survey” where the key components were driving and delivering value and defining and how to build an agile procurement team. To understand enterprise value, CPO’s reported over a 12-month period their top list of priorities, and almost all of them increased over the previous survey including two completely new ideas about driving operational efficiency and innovation.


“Despite all the talk about resiliency, cost continues to be the central focus for #CPOs and #CFOs at the end of the day, organizations need to be cost-competitive to thrive in the next normal. CPOs are now wrestling with the increased value delivery challenge while also having to become more efficient......clearly recognizing that with efficiency improvements and digitization comes the capacity and capability to up their game.”


But how are high-level procurement teams expecting to meet these priorities head-on if the quality of the data they are starting out with is unverified and poor? Susan Walsh, The Classification Guru and the #FixerOfDirtyData, resolves issues with #garbagedata regularly with her clients.



“Right now, without any clean data, you could be making the costliest mistakes of your business life, based on incorrect information. And you don’t even know, because your data is not clean. Time and time again, I hear “there’s no appetite from the business” that is, until something has gone disastrously wrong and there’s no option but to pay to fix it.



There IS a solution to get some quick results…. simply by normalizing your suppliers or SKU’s. Using something as basic as Excel and then a pivot table to check could save your organization a lot. How many times have you seen IBM or PWC in your data in many different variations? You have no helicopter view of what’s going on because it’s all disjointed. By normalizing your data, the picture suddenly becomes a lot clearer….


A lot of the time, data quality can seem like a menial, low level task. I can tell you right now it is not, if you have skilled professionals helping with your data, you could shave weeks, if not months from data cleansing projects. You’re not just saving costs through your suppliers, you’re saving time through your employees.”



Over a year ago, even the CIO Magazine had the foresight to publish an op-ed on how #ResiliencyInProcurement begins with #QualityData.


“Building a strong and resilient procurement system has never been more important, given the current economic downturn. And starting with quality data can give companies a strong foundation for weathering uncertain times like these, now and in the future.


Today, procurement data sources can include internal, external, structured, and unstructured data obtained from automated processes. That data might provide insights that can be acted upon within hours or even in real time.


If these varying data types can be normalized and analyzed, the payoff is potentially tremendous. According to one report, for example, data analytics can help healthcare industry procurement departments realize cost savings of up to 18%, compared to traditional pricing models.



The catch is that many enterprises struggle with poor-quality data that is insufficient or stale—or both. And without the right data, success will remain elusive.”


The old saying “The first step starts with you” comes to mind when it comes to poor quality spend data. Starting with identifying all of the spend categories, all suppliers within those categories, and all items and services purchased from said suppliers is a good start, followed by normalizing all of the names of the categories, suppliers and items/services so they are all cohesive. The latter portion involves a harmonizing process to eliminate redundancies and grouping like-data together. Incoming spend reports from suppliers need to also be standardized to this new format so that moving forward your future data remains clean and doesn’t get muddled all over again.


If the pandemic taught us anything, it’s that our #supplychain is vulnerable but it starts at home with our own quality of data. In a recent article by McKinsey, “by better using a company’s own data, analytics can help organizations spend more intelligently and efficiently, improving their liquidity and cost position. It can increase transparency and speed, giving decision-makers crucial insights for determining when, where, and how to act. And in so doing, it can help organizations become more resilient.” So if we all realize how monumental having accurate clean data is, why did only 50% of those in their poll report that they performed better in 2020 than they had during the 2008-2009 market recession? Are the other 50% being lazy about their data, or hiding their heads in the sand?



At the end of the day without cleaning up your dirty spend data, your decisions are made on incorrect or incomplete information. CIO Magazine said it best: “Done right, spend analytics is an important tool for proactively identifying savings opportunities, managing procurement risks, and optimizing your buying power. But [its] success involves having the right data to automate for analytics. That means identifying, and then unifying, relevant, current, and accurate data in a format that’s both accessible and actionable. The healthier your data, the healthier your company, during lean and prosperous economic times alike.”