Written by Matt Holzapfel
It’s no secret that enterprise data has its challenges. It’s messy, difficult to use, and can feel overwhelming to understand. Driving improved business outcomes from this data can feel like an insurmountable task. This is particularly true in procurement, which can feel like the Wild West of Data due to the near-impossible task of creating governance and standards around a process inherently filled with massive variety.
Tamr is proud to deliver transformational results to procurement teams at General Electric and Societe Generale. These successes have enabled us to develop a repeatable, turnkey process for unifying key procurement data assets. We’re excited to share this process through a new offering called, “Supplier Mastering as a Service”, which enables you to start benefiting from clean, unified supplier data in days without any IT overhead.
Challenge: Turning Operational Exhaust into Digital Fuel
Many organizations struggle to answer simple questions about their supply base, such as “who are my top 10 by spend?”. This challenge is born from the fact that analytics get layered on top of data created during operational processes. For example, while entering a new supplier into an ERP system, the user’s objective is to be able to source from this supplier, with little thought for analytics which might occur down the line.
Here, data is being treated as an operational exhaust, and so it is no surprise that subsequent analytics fail. This is not a new phenomenon, and organizations have been expending huge effort for the last 30 years to clean up this data, and make it usable for analytics, typically via lengthy master data management (MDM) projects.
You may read the acronym ‘MDM’ and shudder. Most who have been through such an experience have battle scars, and do not wish to repeat. A large part of this was due to the heavy burden on IT, whose task was to code a large set of rules to satisfy requirements specified by the business.
Solution: Agile Data Mastering Infused with Domain Expertise
At Tamr we have been turning this task on its head by employing machine learning to replace the rule generation, while soliciting feedback directly from business users, who understand the data, through the use of smart interfaces which help the algorithms to rapidly learn. This topic is explored in depth in this post on Agile Data Mastering by Tamr’s Head of Product, Mark Marinelli. This agile approach has been used by some of the world’s largest organizations to reduce the length of mastering projects by an order of magnitude (6 weeks rather than 6 months).
At Tamr, we are constantly looking for ways to further reduce the effort required to create a clean, unified master dataset. That’s why we created Supplier Mastering as a Service. We see this as a logical extension of machine learning based data unification whereby organizations can benefit from a wealth of machine-knowledge garnered from several years of solving the same problem at other organizations. This again is a step change in data unification, and provides the lowest effort way yet to create an accurate supplier mastering to fuel procurement analytics.
If you have a supplier mastering problem, and want to learn more about our new Supplier Mastering as a Service, which requires minimal effort from your IT team, zero infrastructure, and no long-term commitment, click here.