A building material company expanded rapidly by completing more than 25 mergers and acquisitions in less than five years. This had a negative impact on the quality of its master databases, creating inconsistencies in its accounts, large stock adjustments, and risks in transactions with suppliers and clients.
Context
A building materials company expanded rapidly through dozens of M&A processes. This had a negative impact on the quality of its main databases, creating inconsistencies in its accounts, large stock adjustments, and risks in transactions with suppliers and clients.
Opportunities detected
Upon analyzing the root causes of the problem, the Matrix team detected a low level of standardization in the process for creating and updating master data on clients, suppliers, and materials in SAP, limited accountability and a lack of communication between management and its subsidiaries, which not only increased data-related problems but also hindered corrective measures from being taken.
Together we defined a series of objectives:
Improve the financial performance and increase the company’s control through the optimization of data management processes.
Ensure the availability of data in the future, facilitating decision-making based on facts and information.
Clean up critical information and ensure that it is kept clean in the future, allowing the business to operate properly.
Ensure consistency and alignment between management and subsidiaries in high-level definitions for data management.
Proposed actions
The Matrix team identified the need to define actions in two areas:
The first includes measures to ensure that data is kept clean in the future:
Standardize processes for creating and modifying master data for the whole company through Kaizen workshops provided for all areas at a local and corporate level.
Create support tools that ensure compliance with the new processes and data quality control.
Agree on a new data governance structure through work sessions with management team.
Formalize roles and decision-making bodies to guarantee the successful implementation of data management.
The second refers to actions to clean up existing data:
Agree on the clean-up of master data and define a roadmap for implementation
Define a follow-up structure for clean-up measures to ensure proper interaction between all areas involved.
Support the creation of SAP tools and protocols to perform clean-up.
Results
New processes for creating and editing master data for clients, suppliers, and materials were implemented, led by a corporate processes team with presence in subsidiaries. A management data system was also activated, creating new roles and development opportunities for company staff. Finally, thirty data clean-up initiatives were defined and implemented with support from the company’s IT, auditing, controller, marketing, and operations teams, among others. Optimization of master data management facilitated the capture of US$10 million in EBITDA.
