Written by Bernie Kuan
Finding Answers With Mastered Supplier Analytics (Part 1)
Companies seeking to streamline supplier management across their organization require trusted views of business-critical data for analytics and operations. However, after spending months of time and money on various implementation projects, organizations still struggle to get consistent, timely answers to the question:
“Who are our suppliers, and how do we interact with them?”
Each time this question is asked, an organization may need to kick off an assessment project either internally or through third-party resources that may take weeks to months. Some organizations may even settle and make decisions based on untrustworthy data because of the effort involved.
Tamr envisions a world where enterprises can consume accurate, up-to-date, unified data, and we want to be part of the data operations that make that possible. The below dashboard is an example of what we provide customers as they undergo their DataOps journey, where clean, unified data goes hand-in-hand with business impact. The process of data mastering over time is transparent and should directly correlate with business analytics and operations.
Through our machine learning and agile mastering approach, organizations should always have within reach the most accurate, up-to-date answers to “Who are our suppliers, and how do we interact with them?”.
Mastered Supplier Analytics
The Mastered Supplier Dashboard below tracks an organization’s DataOps journey through mastering supplier data from disparate data silos of Ariba, SAP, and purchasing card systems.
The dashboard tracks data across 3 published dates:
- 2019-01-22 – State of supplier data BEFORE mastering
- 2019-02-01 – State of supplier data AFTER mastering
- 2019-02-22 – More data sources and records are added, mastered, and tracked
See both the data impact (‘Mastered Data’ Screen), as well as the domain-specific business impact (‘Supplier’ Screen) from having curated, unified data.
A walkthrough of the organization’s journey is described in the section below. Actual data in this example is derived from USA Spend vendors.
Walkthrough of an Organization’s Journey to Mastered Suppliers
Use the dashboard to follow an organization’s journey to mastering supplier data and gaining accurate analytic insights. The below timeline highlights an organization’s digital transformation to unify disparate systems containing supplier data.
As of 2019-01-22: Problems before Mastering
- The organization loads in supplier data from 3 different data sources with 54,000 records
- They know their data has problems and this doesn’t accurately reflect their number of suppliers, but can’t show exactly why
Instead of spending months on requirements gathering, rule development, code development, and testing, the organization decides to use a machine learning approach to master their suppliers (at the site-level) and are able to have results within 1 week.
As of 2019-02-0: Benefits After Mastering
- After mastering 54,000 supplier records, the organization now knows there are actually about 41,000 site-level suppliers
- Clicking through the Mastered Suppliers, the organization can see exactly where and how many duplicates exist
- They see that duplicates stem from data issues such as data entry typos, difference in data entry standards, formatting, business alias names, and null fields
- They can also see how the business is interacting with different versions of the same supplier (and take appropriate action!)
While resolving some of these issues with rule-based MDM systems is possible, having accurate, self-sustaining, mastered results within a week that can be iterated on quickly may require something more best-of-breed.
As of 2019-02-22: Ongoing Data Unification
- The organization adds more data sources and the machine learning model from previous exercises continues to be applied seamlessly on new incoming data
- Data changes can be tracked and timely action can be taken
- When data issues occur, the organization can use a feedback tool such as Tamr Steward (a Tableau extension not shown here) directly from the dashboard to resolve the issue
With data unification in place, the data-driven organization can continue to update and unify supplier data from different data silos with confidence, know exactly where individual supplier entities come from, and resolve how they interact with related suppliers. The ability to master related suppliers together further opens up a host of analytics that can improve their business.
Can You Trust Your Supplier Analytics?
Tamr has helped some of world’s largest enterprises unify and master entity data. Our customers know that their data assets are more valuable when well-curated. Otherwise, large volumes of messy, siloed data either continue to be used in siloed use cases, or become liabilities when incorrectly interpreted more broadly.
In part two of this series, “Do I Trust My Supplier Analytics?”, we will continue to walk through the Mastered Supplier Dashboard to explore the impact of Mastered Supplier Data on downstream supplier analytics consumed by business users.