Blog
April 4, 2023

Why is This So Hard? Identifying and Solving Procurement’s Spend Data Challenges

Share
TABLE OF CONTENTS

Manual Processes are Unsustainable

Manual data collection and data analysis are among the most common spend analysis mistakes to avoid. It’s neither reasonable nor sustainable to manage spend data at scale this way. These manual processes are time-intensive, error-prone, and limit scalability across procurement teams and finance teams.

For starters, companies may need to pull raw spend and supplier data from 10, 20, 30 or even more internal systems and sources. Even when companies do automate their data collection and management processes, they may become overly reliant on automated systems or lack sufficient domain expertise to properly code or recode data.

In-house data models, such as spend cubes built with tribal knowledge, may not be easily repeatable. Without knowledge transfers, departing staff would deprive their teams of the knowledge or ability to run or refresh models for new business cycles. And analyses can carry high degrees of uncertainty (i.e., Did we collect everything? Where else do we have spend data?).

Why It’s Hard

  • Disconnected systems across accounting, procurement operations, and financial services platforms
  • Manual expense tracking and inconsistent expense management processes
  • Limited automation across the procurement process

Impact

  • Slower data analytics and delayed insights
  • Increased procurement costs
  • Missed cost saving opportunities and reduced cash flow visibility

Key Spend Data Challenges

Consolidating data is tricky

Spend data can reside across dozens of business units, including accounting services, finance teams, and ERP systems. Spend data can also reside within the company’s vendor master sheets, with the vendors themselves, and with third-party sources, such as financial institutions.

Consolidating spend data across incompatible formats and systems is a major barrier to spend visibility. Without APIs and data connectors, and common or compatible data formats, procurement teams will have to manually pull the data from each of these sources and clean it, a process that can take months. While in reality, CPOs often need to make decisions in minutes.

Impact

  • Incomplete procurement analysis
  • Poor visibility into spending patterns
  • Increased missed opportunities for savings

Normalizing data at scale is hard

Normalizing data is a critical step in effective spend analysis but remains a common procurement miss. There can be a hundred variations of the same supplier, service, product, part, or commodity name across an organization – depending on data coding and preferred taxonomies. Multiply that by thousands of suppliers, tens of thousands of categories, and hundreds of thousands of parts and products.

Without automated spend management solutions that employ machine learning algorithms to intelligently recode disparate category names into standardized taxonomies, procurement teams will be in the weeds cleaning up their spend data when they could be at the surface level steering the ship.

Impact

  • Reduced data quality
  • Poor visibility into indirect spend and tail spend
  • Missed savings opportunity areas

Uncategorized spend 

Uncategorized spend is one of the most common procurement mistakes. They can still leave more than 70% of their spend uncategorized, even using 200-1,000 sub-categories. With so many spend categories, it can be difficult to align on a workable naming taxonomy with other stakeholders across the organization.

Impact

  • Poor spend visibility
  • Increased maverick spend and maverick spending
  • Lost cost savings and unmanaged expenses

Data enrichment is hard, too

Even after data collection and categorization, many organizations struggle to turn data into actionable insights through enrichment. This can involve integrating transactional data from AP/finance databases (e.g., payment terms) and supplier information management systems (e.g., supplier addresses, diversity, and sustainability data). There may not be established processes, which could complicate matters further.

Procurement teams must also incorporate supplier performance, risk, and sustainability data as they focus more on ESG initiatives , laws, and regulations. Many of these laws, such as the German Supply Chain Due Diligence Act and the US Uyghur Forced Labor Prevention Act , require companies to gain visibility into their suppliers’ environmental impact, human rights records, and corporate governance practices. But procurement teams are struggling to solve their data problems in time to match their enterprise spend behaviors with their suppliers’ ESG impact, which will ultimately hinder their compliance to ESG laws and regulations. Additional drivers for data enrichment could be corporate compliance related to corporate goals, or goals related to investors and employee targets.

Impact

  • Weak risk management
  • Limited strategic value from data
  • Reduced the effectiveness of financial planning

Distillation depends on the audience

Providing the right level of data analysis to stakeholders is critical for informed decisions. High-level data may underwhelm stakeholders, while granular data may overwhelm. Procurement teams need flexible spend intelligence solutions that provide header-level views of critical spend data with drill-down capability for more detailed views. They provide the best of both worlds for procurement teams that have multiple audiences and data priorities.

Best Practices

  • Provide summary insights for executives
  • Enable drill-down for procurement teams
  • Align reporting with the finance team needs

The Solutions to Your Spend Data Challenges

Procurement teams can avoid these common mistakes by adopting best practices and modern spend management solutions. They don’t need to rely on manual processes to gain current, actionable spend intelligence to make the most informed sourcing and purchasing decisions. Modern spend analytics platforms combine automation, analytics, and predictive analytics to transform procurement operations.And SpendHQ has been perfecting these solutions for more than a decade.

We provide an automated, AI-driven spend intelligence solution that lets you overcome all these common procurement data management challenges. We match our deep domain expertise with our industry-leading spend intelligence solution to help you wrangle 100% of your enterprise spend data, extract critical intelligence, and use that intelligence to identify and realize cost-savings and value creation opportunities. We also support risk management, compliance, and long-term strategic value through better procurement decisions.

Final Takeaway

Avoiding these spend analysis mistakes to avoid is essential for improving spend management and unlocking cost savings.

With the right combination of automation, data analytics, and best practices, procurement teams can turn raw data into actionable insights, reduce expenses, and drive smarter, more strategic spending decisions.