Reference Data Management
Reference Data Management (RDM) provides the processes and technologies for recognizing, harmonizing, and sharing relatively static data sets for “reference” by multiple constituencies (people, systems, and other master data domains).
Inconsistent or non-existent Reference data management (RDM) can be debilitating for a company because all systems in a company rely on the reference data as a standard. Without it, business intelligence reports can be inaccurate, and systems integrations may fail.
FEATURES
Reference Entity Profile
Helps to create reference data to describe the entities and each attribute in it by using metadata.
Attribute Analysis
This requires metadata of the reference data and generates even more reference metadata of mapping attributes that need to be captured.
Data Analysis Repository
Data Analysis Repository
This includes facts about the reference dataset or individual codes. This helps users of the reference data to understand how to interpret and use it.
Import Reference Data
The import includes the capability of updating external or internal reference data metadata as per business needs.
Assign Accountabilities
Assign Accountabilities
Assigning the accountabilities for all aspects of reference data management as per the reference dataset, particularly for internal reference datasets, this requires a rich set of reference data metadata elements.
Distribute Reference Data
A variety of distribution mechanisms such as export, connecting to internal Data Quality systems for reference.
APPROACH
01
Establish a Central Reference Data Unit
Establishing a Reference Data Unit will help oversee data management across the organization. It is important to use this for data standardization, data quality, and operational goals to increase efficiency.
02
Manage External Data
Use the previously established standard practices to discover, profile, and understand reference data. This data should be kept up to date.
03
Govern Reference Data
The Reference data should be governed by SMEs as standards are more likely to have been created by them, and the team should be aware of any changes to the reference data. Departments should also be held accountable for their internal reference data.
04
Collaboration
Since reference could be used throughout the enterprise, it is essential that all applications and all users have current, synchronized data. This will ensure operational efficiency.
BENEFITS
Establish a Central
Reference Data Unit
Establishing a Reference Data Unit will help oversee data management across the organization. It is important to use this for data standardization, data quality, and operational goals to increase efficiency.
Manage
External Data
Use the previously established standard practices to discover, profile, and understand reference data. This data should be kept up to date.
Govern Reference
Data
The Reference data should be governed by SMEs as standards are more likely to have been created by them, and the team should be aware of any changes to the reference data. Departments should also be held accountable for their internal reference data.
Faster Turn
Around
RDM solutions not only automate the process of obtaining error-free data but also saves an enormous amount of time.
Reduced
Operational Risk
By defining and placing reference data in one central location and applying to enrich business data, users can speed up processes by reducing the operational risk and increase efficiency.
Improved Regulatory
Compliance
RDM application simplifies the challenges related to security breaches and regulatory policy enforcement thereby increasing Regulatory compliance.
Collaboration
Since reference could be used throughout the enterprise, it is essential that all applications and all users have current, synchronized data. This will ensure operational efficiency.
CASE STUDY
Customer
- A leading Retail Customer.
Use Case
- Improve the Data Quality and omni channel experience for their consumers .
Business Challenge
- Automate multiple source data processing using a single platform.
- Need to maintain a single source of truth by matching and merging the duplicate records.
- Managing future data enrichment in a robust and faster way.
Project Requirements
- Create an MDM implementation to maintain Consumer (individual) and Customer (Business) records.
- Integration of different source data.
- Cleansing the data with given business validation data quality rules.
- Defining the data management strategy to master current and future consumers and customer data.
Solution Highlights
- Created an automated process to integrate for the data collected from multiple sources.
- Managing the Data Quality requirements for the selected attributes in Data Asset through the standard business validation rules.
- Assessing the quality of the data source by generating operational reports.
- Data model to support the feeding source system with require changes as per the business requirement so that further transactions can be initiated without any confusion.
- Easy edit for Master data through MDM data Browser.
- Different data models for various specifications as per business (e.g. Customer, Partner, Sales, etc.)
AMURTA Value
- Supported in managing the operational metrics as per business validation rules.
- Reduced the time taken to view all merged consumers' information as per their corresponding roles with the compilation of multiple source data.
Results
- Key metric information was provided in near real-time to business executives.
- Related operational Dashboard updated.
- Business found the number of consumers with specific consumer role to improve the performance in Sale.
- Improved business resource optimization.
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If you have queries we are ready to discuss how our Data Insights Platform can help you in improving your organization governance process.
