Founder and CEO of Bid Ops — a provider of predictive sourcing software to help procurement teams predict and win faster savings.
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A new survey by The Economist Intelligence Unit estimates that supply chain disruptions have cost companies over $4 trillion in lost revenue. Simply put, when it comes to supply chains, having high-quality data is a business imperative.
That lesson has been driven home by wave after wave of shortages and price increases since the beginning of the pandemic, where bottlenecks, backlogs and surges in demand have created a perfect storm of cascading supply disruptions. The 2021 Deloitte Global Chief Procurement Officer Survey showed that CPOs today are prioritizing supply chain data quality like never before, with some even ranking supply chain data as a higher priority than cost savings. That’s a significant shift from pre-Covid times.
The problem is getting high-quality data about a large group of geographically distributed suppliers is no easy task.
The traditional approach to collecting data from suppliers is to send them a survey. This survey is often called a request for information (RFI) if the purpose is to identify and qualify new suppliers. If it’s for a company’s existing suppliers, then the survey often falls under supplier life cycle management. Measured in hours, using surveys is a very expensive and error-prone approach to data collection. And that’s only if the surveys are ever completed in the first place.
For suppliers struggling with uncertainty, completing the same survey questions for many customers is a laborious and repetitive manual data entry task. During times of disruption, completing these surveys can become a lower priority for suppliers. Fatigued, understaffed and overwhelmed, suppliers may let survey requests go unanswered. But what if it were possible to eliminate surveys entirely?
This is where third-party supplier data has emerged as a new category of technology solution. This is a massive shift in the industry, and it could soon spell the end of supplier surveys as the main source of supply chain data.
The End Of Supplier Surveys: Why Companies Are Buying Data
Relying on manual data entry from supplier surveys assumes that human labor within any supply chain is both plentiful and inexpensive. Following the Great Resignation, it’s clear that this is no longer the case.
As human labor has become scarcer and more expensive, data and compute power have only gotten cheaper. Twenty years ago, if you wanted to build a list of relevant business contacts, the cheapest way was to hire a temp worker or an outsourced service provider. Today, the same task is performed at a fraction of the cost by a cloud-based database with a simple user interface for any subscriber to query and filter records. This has long been the case for sales and marketing following the rise of platforms like Salesforce, HubSpot, ZoomInfo and now Seamless.ai and Clearbit. But increasingly, it’s becoming the “new normal” in procurement and supply chain.
The reason is simple: When it comes to data collection, supplier surveys just don’t get the job done. Manual data errors abound. Incomplete surveys are often just as problematic as unreturned surveys. Surveys containing even a single obvious error can call into question the integrity and fidelity of all the data. Why perform expensive analysis on bad data at all? As the saying goes: garbage in, garbage out.
That’s why many companies are now choosing to skip the survey altogether and buy supplier data from third-party supplier databases. Popular data providers include industry standards like Duns & Bradstreet, Thompson Reuters and Dow Jones, as well as startups like Tealbook, Supplier.io, Risk Methods, EcoVadis and others. But the proliferation of these supplier databases requires companies to develop a predictable, repeatable process for buying third-party supplier data. For many companies, developing this process can create a new challenge.
Buying Data From Third Parties Requires A Repeatable Process
Simply buying supplier data instead of emailing suppliers with a long survey sounds like a quick and easy win. The reality, as IT procurement professionals know, is that buying data — any data — requires a repeatable process.
There are many different data providers, and not all of them perform to the same standards of quality, reliability and turnaround time. Certain databases are fully automated, while others may be checked by a human to validate quality and consistency. This is often called buying “data enrichment services” or “data validation services” rather than just buying “data.” And once you have the third-party data, you may now face the daunting task of reconciling it with the internal supplier and spend data that you already have, including data you’ve purchased previously.
While certainly less expensive than human labor, the costs of data can mount quickly. For example, if you spend $2 per supplier record to enrich an existing database of 75,000 suppliers, you may need a data budget in excess of $150,000. If you enrich before cleansing your data of duplicate records, you could easily be overspending by tens of thousands of dollars (e.g., by buying data for suppliers’ records that aren’t relevant to your reporting goals).
The three best practices for making a great process for buying third-party supplier data include:
1. Cleanse supplier master data before buying supplier attribute data (e.g., contact data, certification data, etc.).
2. Have a structured internal process of tying enriched reports together (e.g., spend reports with supplier reports).
3. Create a buying calendar to make sure that reports involving multiple datasets remain updated and accurate.
This process will ensure that you aren’t buying data you don’t need or buying data more often than you need it. However, you can also automate these processes by purchasing data programmatically using a data provider’s API (application programming interface), which will ensure that your data is auto-refreshed on regular cycles. Procurement teams often refer to these programs that link multiple APIs as “data exchanges,” which often marry internal spend datasets with external supplier attribute datasets to create holistic and “always-on” supplier relationship profiles.
By taking the guesswork out of third-party data purchases, API-driven supplier data exchanges are helping procurement teams deliver better and faster supply chain reporting to the C-suite.
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