Focusing on “data-driven processes” is a top priority for procurement teams. However, when data-driven decisions are made without quality data, they result in missed opportunities and financial risks. And, when the lack of data quality goes unnoticed, there’s a snowball effect: operational efficiency is halted, risk and vulnerabilities with suppliers are heightened, and critical decisions are made with outdated information.
But before organizations can solve their procurement data problem, they need to understand the root cause.
Understanding procurement data
Procurement data is primarily divided into two categories: internal procurement data and third-party information.
Internal Procurement Data:
| This encompasses all the information generated within an organization, like: | |
| Transactional data | This could range from invoice data, purchase orders, receipts, customer purchase history |
| Contract details | Project management requirements, terms and conditions, delivery timeframe, quantities. |
| Supplier performance metrics | Could include the supplier’s lead time, order accuracy, responsiveness, competitiveness, compliance rate, price variances, and more. |
While this data forms the backbone of procurement decisions, it’s often:
- Unstructured (e.g., videos, emails, images)
- Scattered across multiple systems
- Inconsistent
- Challenging to manage and analyze effectively
Third-Party Information:
| This includes external data about: | |
| Suppliers | Can include anything from company name, to NAICS code, certifications, company size, address and other firmographic data. |
| Market trends | Price volatility, market fluctuations, and other changes impacting procurement decisions are factors to consider. For instance, sudden increases in raw material prices can significantly affect your production costs. |
| Industry benchmarks | Such as: cost effectiveness, process efficiency, cycle time, quality, and staff productivity. |
Integrating this data with internal sources is crucial for organizations to gain a holistic view of their suppliers and make well-informed decisions. However, managing this data is often daunting due to its volume and variability.
Three steps to take to improve procurement data
To make sense of all the data, organizations should focus on these critical steps:
Standardize the data:
For most procurement teams today, data is gathered through various sources and, depending on the operational need, added to the appropriate systems. To ensure data standardization, teams establish rules to ensure everyone is following the same process.
For example, establish proper naming conventions for suppliers and maintain a schedule to remind your team to update supplier data. By implementing a common taxonomy, data can be reused effectively and redundant analysis can be eliminated. Standard processes and categorizations promote consistency across multiple data sources, encourage data sharing, and ultimately contribute to the delivery of more accurate insights.
Improve data access:
A database that offers readily accessible and high-quality data fosters a culture where information drives decisions and spurs change, like increased operational efficiency. By creating a central data repository, decision-makers can focus on critical data to swiftly make better decisions and implement necessary changes.
And, it’s not just a procurement problem. Everyone in the organization should be on board to maintain accessible, quality data. Fostering a data-driven culture is fundamental for staying competitive.
Establish data requirements:
Many organizations think that more data means more information, but it’s crucial to know the goals of the organization in order to decide what data is needed. Companies should also be flexible in the data they collect to match the changing objectives of their procurement strategy, but also establish requirements to better understand what data is necessary and what isn’t.
The power of a supplier data foundation
When it comes to data management, accomplishing all of these steps is easier said than done. They’re time consuming, resource-intensive and sometimes impossible. What if there was a solution that could help automate these tasks and tackle all of them at once? Enter a supplier data foundation.
A supplier data foundation serves as the source of truth for supplier data in an organization. Its AI-powered technology helps to improve data quality, eliminate duplicate data, and expedite data-driven processes.
TealBook’s Supplier Data Platform is a supplier data foundation that continuously expands its supplier network and creates universal supplier profiles (USP). The USP serves as a connection between the organization’s supplier ecosystem and the TealBook environment. Here’s how it works:
- TealBook integrates the list of suppliers that an organization currently has or wants to enrich.
- TealBook matches and unifies supplier records in the organization’s systems with the USPs in TealBook.
- This process identifies unintentional supplier duplication and provides early insights so organizations can identify potential contract consolidation opportunities, or instances where multiple divisions pay for the same data set from the same provider.
How our data is different
Tealbook’s unique identifier, known as a ‘TealBook ID’, helps identify suppliers across different systems and provides a consistent and reliable reference point for each individual supplier. Every supplier record that TealBook ingests connects to an organization’s environment and is accompanied by a TealBook ID. This allows for seamless data sharing across different solutions, creating a clean link between systems that may have had very different data before.
For example, an organization has tax IDs in various formats—around 18 different ones, to be exact. They often come across vendors and suppliers with both two-digit Tax IDs in their ERP and six-digit Tax IDs because they receive Tax IDs from various sources. By unifying these Tax IDs with a TealBook ID, organizations can quickly understand which Tax ID is mapped to which supplier profile.
Data you can have confidence in
Many leaders in large organizations are hesitant to adopt technology that uses AI and ML1, which is why TealBook’s SDP offers the TrustScores and Business Rules Engine features. These features identify an attribute-level confidence score ranging from “Low” to “Very High”, and give users control over which attributes they want to bring into their organization’s systems, depending on their confidence threshold. This ensures that only relevant and trusted data is used, saving time and effort spent on data cleansing.
The impact of a supplier data foundation
With a supplier data foundation, the critical steps to improving data quality are expedited, without relying on additional resources.
| How TealBook improves data quality: | |
| Enrich | The time it takes to fill in missing data is erased with automatic supplier data enrichment. And, instead of spending hours, weeks, or months cleansing data and ensuring everyone’s using the proper naming convention, automatically get enriched data that is mapped to the right entity in your vendor master. |
| Verify & Match | The TealBook ID links suppliers together who might have different information on file, so everyone in the organization can identify and remediate the inconsistencies and ensure your supplier’s information is accurate. |
| Validate | With enriched data across all systems and tools, no matter where in the world your team is working from, they’re all using the same quality data. And, with TealBook’s Business Rules Engine, the whole organization is working with data that follows the same quality requirements, ensuring consistency across the business. |
A strong foundation of supplier data has been a constant struggle for organizations due to the sheer volume of data they deal with and the manual processes that have been tied to master data management. However, with a supplier data foundation, organizations can automate the process of maintaining accurate and reliable supplier data. By prioritizing data quality and integrating innovative solutions such as TealBook’s SDP, businesses can navigate the complexities of the procurement landscape, make data-driven decisions, and ultimately drive efficiency and growth. The transition from manual data processing to streamlined operations is not just a change, but a necessity in today’s procurement landscape.
Sources
1Business Leaders Optimistic but Hesitant Over AI Adoption, Digit News, September 2023.
