APQC recently spoke with Genpact’s Susmita Kanjilal, assistant vice president, master data management practice, and Sandeep Singh, vice president, sourcing and procurement practice, about how organizations can implement an effective master data management program. This post presents the first half of the interview, in which Kanjilal and Singh discuss the benefits of a master data management program and potential obstacles to implementation.
Susmita Kanjilal and Sandeep Singh gave an in-depth presentation on this topic during a recent APQC webinar.
Why did Genpact prioritize its master data management practice?
Genpact manages finance and accounting, order to cash, and source to pay processes for large organizations. Since we manage the processes and operations for the client we see a huge gap in data quality, which is predominantly due to lack of governance, lack of standardized processes, and disparate technology platforms. For example, due to gaps in its customer master, an organization may have extended days sales outstanding below the industry benchmark. The key reason for this is incomplete and inaccurate customer master attributes. Similarly, procurement efficiency in many organizations is below the industry benchmark because of large issues in vendor and material master. For example, with suboptimal vendor data management, accurate spend categorization cannot be completed. Another common observation is the lack of material master data, which leads to the proliferation of free-text purchases within the organization.
With a view on how to run these processes more efficiently for its clients, Genpact believes that fixing upstream master data processes can lead to efficiencies in the downstream processes. Thus, across all processes master data efficiency is critical to achieve best-in-class status. Genpact recently commissioned research with senior-level executives on the potential impact of new operating models. It reveals that organizations understand the material impact master data management can make on their most important enterprise challenges, which include:
- ensuring compliance with regulations,
- reducing costs,
- increasing customer satisfaction,
- increasing growth and scalability, and
- managing risk.
However, master data management maturity in many organizations is low, which demonstrates the need for a holistic and focused approach. Based on our insights and experience, Genpact has prioritized its master data management practice.
How does Genpact measure the effectiveness of an organization’s master data management program/processes?
Simply put, the effectiveness of an organization’s master data management program/processes can be measured by the quality of data available for its reporting and transactional requirements.
For a more holistic analysis, we use proprietary frameworks and granular benchmarks to measure the effectiveness of a customer’s master data management program and processes. It is a proven framework that has been used for multiple organizations. The key dimensions of this framework used for measuring the maturity of master data management are:
- data governance for master data, including the organization, roles and responsibilities, processes, and policies;
- the organization’s operating model, including who does what from where;
- the processes for creating and maintaining master data with key controls embedded in them;
- data quality management for cleansing master data, both manual and automated; and
- tools and technology to support the master data organization and increase productivity.
Organizations across industries are at various stages of the maturity cycle, and there is no one solution that fits all organizations. For example, in a recent engagement with a telecommunications organization, the data governance framework had to be highly customized to meet its existing governance framework of a “demand and supply model.”
Do organizations experience any obstacles or push back during implementation?
Master data management initiatives can face considerable push back due to the challenge of establishing the business case to prioritize this initiative and the heavy change management required.
Organizations tackle the problem in multiple ways—to tackle change management issues, organizations design the data governance organization and operating model to bring minimal disruptions to their existing operations. While this may work for some organizations, one has to be careful that it does not undermine the very reason for the change. A master data management initiative should always be tightly coupled with a change manager to ensure people consider data as an asset.
Creating a business case for master data management with hard quantification is a challenge due to fragmentation of the departments consuming master data. Departments typically operate in silos and the priority of one department is different from another, resulting in challenges to establishing a business case at the enterprise level. For example, a large petrochemical organization conducted multiple assessments to build a business case, but due to multiple stakeholders or consumers of master data, the organization faced challenges obtaining buy-in of the business case, without which it could not start mobilizing resources.
Genpact has created a business case framework that we use to create quantified benefits that is continually enhanced with new proof points from our engagements.