Data Pioneers, Future Navigators
Master data management (MDM) creates a process to prepare a uniform set of data or master records for entities – persons (customers, suppliers, buyers, vendors), places (addresses), or things (products) in a business organization within internal or external data sources and applications. ESL’s MDM solutions help in improving the data quality of an organization by providing a trusted view of business-critical data that can be managed and shared across the business. This ensures accurate reporting, reduces data errors, removes redundancies, and helps employees make better-informed business decisions. Additionally, effective MDM from ESL enhances the trustworthiness of the data used in BI and analytics.
Business operations depend on transaction processing systems and BI. Data analytics increasingly drive marketing campaigns, customer engagement efforts, supply chain management, and other business processes. ESL’s MDM solutions rely heavily on the principles of data governance with the goal of creating a trusted and authoritative view of a company’s data. These solutions automate how business-critical data is governed, managed, and shared throughout the business, brands, departments, and organizations.
Data profiling & data quality
Data Empowerment: Integration, Profiling, Custom Insights!
ESL’s MDM solutions simplify data collection methods and focus on data profiling activity. They provide various tools to streamline the process, ensuring efficient data collection and accurate profiling, thereby enhancing the efficiency of data profiling activities across various data sources. There are various tools available which can:
- Connect to any type of database, data source, file to collect or retrieve the raw data.
- Assist in database design, normalization, de-normalizations, and data processing.
- Help in data mining.
- Have graphical features for database functions that simplify the tasks.
- Have options for automation for data characteristics, profiling and cleansing.
- Provide data or report outputs in multiple formats that helps for further analysis.
Our Data Management team has the experience of using these tools and even extend their functionalities to provide customized reports which help the customer’s Data/Migration teams and business team. Our team assists in:
- Data Quality report that represents the ratio of data availability and filled, based on the current system implementation. This helps to find gaps in the system and communicate with concerned teams/departments to re-check the existing data. This helps to clean the data in a safe and secure process.
- Data Characteristics report that represents the data quality that is already in use. Customers can identify the use case to implement the business rules that can maintain the data quality and can be implemented during MDM implementation process.
- Data Governance report based on the business rules provided by the customer. This represents the data validation status along with the data quality that already exists throughout all the systems.
Data Comparison reports between various systems. This is a unique report that help customers to identify the data transformation between different systems. As data transformation is an important activity, based on which data travels between multiple systems, this report helps the customer’s business integration team to understand their data fabric.
Data Model & Governance
Unlocking Insights, One Data Point at a Time!
During any MDM implementation, finalization of the Data Catalog is one of the primary and important activities, as the implementation will be carried out based on this data catalogue. During this phase, the projects are segregated under different scopes, based on their features and functionalities. The initial data model is prepared based on this which may get updated in the later phases. The initial phase, named MVP (Minimum Viable Product), where the focus is to create the initial prototype of MDM implementation and to design the data model and basic workflow execution with minimum data governance.
Full phase implementation
Turning Ideas into Code, Innovation into Reality
Post completing the MVP, planning is done for the full implementation and a target date is agreed. Based on the target, the list of sprints along with target for each sprint is decided. During implementation, the sequencing of features must be taken into consideration to ensure minimum rollback or rework. Integration related issues need to be checked as the features may conflict due to data governance rules, data model, reference libraries and workflow process. The integration process uses various types of data profiles that may be required. Workflow process is implemented for different types of user roles to enrich the master data in different steps. Examples of different types of roles, with access rights, are as follows:
- Buyer – Access for certain departments
- Vendor – Vendor specific entities
- Merchandizer – For publication related data
- Digital – Update and export digital platform data
- System Admin – To configure system
- Business Admin – To start the product data enrichment process
The workflow process allows the MDM to restrict the enrichment process for each user role and bound the user to fulfill the data requirement. This process blocks the user if he does not fulfill all the business conditions related to that workflow step. This also controls the export process and can execute some steps automatically. Business process automation ensures data quality. Data governance process provides computation & validation processes. In addition, the data governance process triggers the export profile that are the part of external integration and external system receive entity data in different step of workflow and in different data conditions.
Building relationships between multiple entity types is another complex area for any MDM implementation.
Challenges of MDM implementation:
- One of the major challenges for any MDM implementation is getting different business units and departments to agree on common master data standards.
- MDM efforts can lose momentum and get bogged down if users argue about how data is formatted in their separate systems.
- Another often-mentioned obstacle to successful MDM implementations is project scoping. The growing use of big data systems in organizations can also complicate the MDM process by adding new forms of unstructured and semi structured data stored in a variety of platforms.
MDM implementations typically are long projects that include various phases and milestones. Create a master data model that formalizes the structure of the master data records and maps them to the various source systems. Modify source systems as needed so they can access and use the master data during data processing operations. Because of its complexity and broader impact on business operations, MDM programs involve a wide range of people from different technical and business verticals.
Despite several challenges, MDM harmonizes to create a unified set of master data to be used in all applicable systems, that enables organizations to eliminate duplicate master records with mismatched data, giving operational workers, business executives, data scientists and other users access to comprehensive information without having to manually combine different data entries.