Good data is at the heart of every informed procurement and sourcing decision; however, many professionals often face the daunting task of making choices based on inadequate or unreliable supplier data. This poor data quality hinders their ability to be strategic and resilient in times of disruption. But bad data can be defeated. How, you may ask? By acknowledging the potential weaknesses in your procurement processes, evaluating your current data and implementing necessary improvements.
Identifying bad data and what to do about it
The first step to fixing bad data is determining if your current data is up to snuff. To establish the integrity of your data, evaluate the prevalence of the following. These signs may indicate bad data is thwarting operational efficiency and success in your operations:
- Project delays.
- Exceeding budgets.
- Failure to meet demand.
If sourcing delays or data errors have led to any of these consequences in your organization, it may be time to tidy it up. Don’t be ashamed if this is the case — according to TealBook research, the average procurement team needs five weeks to identify a new supplier, and 100% of leaders say this prolonged timeline has created project delays in their organization.
There are two options for tackling this problem. The first, more traditional method involves manual data synthesis, including data cleanup and continuous updates. Alternatively, leaders can leverage a supplier data platform powered by machine learning (ML) and AI to efficiently harvest, validate and integrate accurate supplier data into their systems. To best decide which course of action will be the most beneficial for your organization, let’s take a closer look at both solutions.
Which way to the best data?
Better supplier data and a stronger data foundation are paramount for procurement professionals looking to remain agile in the face of disruptions and delays. But what approach will best serve your organization?
Manual methods
A traditional data solution requires a rigorous process of data synthesis as well as regular check-ups and cleanup. This includes spending considerable time and resources to ensure the accuracy and completeness of supplier records, as well as working with suppliers to establish procedures for continuous data updates. While this approach can be effective, it is time-consuming and prone to human error. Plus, every time more information is entered into your system, the whole process has to start all over again.
Utilizing a supplier data platform
Leveraging an AI and ML-backed supplier data platform is an innovative solution to bad data. These platforms automate the gathering, validation, enrichment and integration of data from numerous sources. It offers a centralized and up-to-date source of supplier data, seamlessly integrating with existing procurement technologies and enabling efficient search, filtering and shortlisting of suppliers based on specific criteria. By leveraging advanced technologies, a supplier data platform streamlines data management processes and ensures a more accurate, diverse and reliable supplier database.
Furthermore, supplier data platforms improve visibility into data provenance by providing complete transparency into the quality and completeness of a supplier’s claims. For example, if a supplier is classified as a minority- and women-owned business enterprise (MWBE), supplier data platforms will trace the lineage of that claim with relevant ownership information, validation information and certifications.
An ML- and AI-powered supplier data platform has several advantages when used in the procurement process. These platforms save valuable time and effort for the entire company by automating
- Data gathering.
- Validation.
- Integration.
- Visibility.
- Reliability.
And the benefits don’t stop there. ML algorithms enable the platform to continually update and enrich supplier data from a vast array of sources, ensuring accuracy and completeness. By leveraging AI technology, the platform offers advanced search and filtering capabilities, allowing procurement professionals to quickly identify and create a list of potential suppliers based on particular requirements.
Additionally, the seamless integration with existing procurement technologies optimizes workflows and enhances overall efficiency. A supplier data platform powered by ML and AI not only streamlines data management processes but also provides procurement teams with a reliable and comprehensive source of supplier intelligence, enabling them to make more strategic decisions and respond effectively to disruptions in the fast-paced business environment.
When it comes to sourcing and procurement, bad data can lead to bad choices that cause problems from product delays to business failure. Ultimately, a supplier data platform provides procurement professionals with the good data they need to make good decisions that drive growth and optimize operations.
About the Author:
Brian Tarble serves as TealBook’s Senior Vice President of Product.
Having spent the last 13 years as an accomplished, results-oriented product and technology leader in the SaaS space with a specific focus in data & analytics, travel, and procurement, he brings an exciting drive and determination to TealBook.
Most recently, Brian was the VP of Product & Strategy for the Intelligent Spend Management Group, responsible for data and analytics across SAP Concur, SAP Ariba, SAP FIeldglass, and SAP S/4. Prior to his role at SAP, he held leadership positions at Concur, TRX, and Oracle.
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