Deduplication and Data Stewardship Process in MDM
Data deduplication is necessary to maintain an accurate master data record. The enterprise needs a single source of truth to maintain consistency and efficacy.
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Data Deduplication in MDM
In master data management, often the same data is duplicated across several departments, which can harm the business. That’s why data deduplication is necessary to maintain an accurate master data record. It includes the removal of duplicate data from the business database. Moreover, master data needs to be a single source of truth for the whole enterprise to maintain its consistency and efficacy.
Data Duplication Strategies
Data deduplication has many advantages, such as improved cost savings. It helps improve analytics performance by providing the team with the most reliable data. It also helps the company provide a better customer experience.
Some conventional data deduplication strategies include data standardization based on external IDs, fuzzy matching with rules, persistent IDs, enrichment data, and machine learning.
- Small data volumes can be standardized by dates, phone numbers, and addresses, while ETL pipelines can normalize new data sources.
- Fuzzy matching and complicated rules help identify duplicates. But it is not convenient for multiple data systems.
- Assigning external IDs is also helpful for the deduplication of data, such as setting social security numbers to individuals and DUNS numbers to companies.
- Machine learning helps improve data management and avoids duplication by increasing automation.
- Data enrichment helps integrate internal and external data, standardizes the data, and helps identify duplicate data.
How to Identify Duplicate Data in MDM
Match merge is a process that can help identify duplicate data in master data. It takes data from different systems and looks for duplicates or exact matches (and merges them if necessary) to make a "golden copy" of the record. The match-merge process can be done in two ways: in real-time or in a batch approved by another method to verify the golden record.
- The matching process includes match columns and match rules that help recognize similar records in the database, determine customer records for automatic compliance, and determine documents that a data steward should review before consolidation.
- The matching process consists of two basic techniques (fuzzy match and exact match) that help identify duplicates. In fuzzy matching, base object matches are found through the slowest method. In it, records are matched based on misspellings, transpositions, word combinations, splits, omissions, and phonetic variances. Exact matches make it quicker to compare records whose match columns are identical.
- Consolidation is the next step after the matching phase. It is rich with queued match records and sent for the merging process. The merged data after compliance is known as the "golden record."
- Defining match rule sets, choosing match columns for comparison, and configuring the base object are required for the matching procedure. Duplicate or identical records are detected and queued for merging by match rules.
We can configure precise matching techniques using fuzzy base objects using fuzzy logic. Fuzzy logic cannot be defined with exact base objects. With exact base objects, the matching procedure can define exact criteria for finding matches, allowing it to detect only those records that are exact duplicates or identical. Fuzzy logic, on the other hand, uses imprecise criteria to find matches, allowing it to identify records that are similar but not exact duplicates.
Data Stewardship in MDM
Data stewardship ensures that a business's data is accessible, practical, useable, and trustworthy. Data stewardship takes care of data, ensuring its trustworthiness, protecting its lineage, executing data usage standards, and marketing its value.
Data Stewardship Strategies
There are different strategies for data stewardship to make business data successful.
- Data stewardship should be made an essential part of the team. Data stewards must take control full-time in the data governance of any organization and participate in active communication, briefings, and invitations.
- Senior executives must support data stewards, who can help them achieve their goals and retain credibility in monitoring the organization's data.
- Building a data-driven culture within the organization is also an essential strategy for practically using data across the organization. Data stewards help drive this culture in master data management.
- All the decisions, business rules, and data elements related to data stewardship must be written down and readily available. Using tools helps appropriately record and track every detail.
- Data policies should be practiced and accepted by all data team members.
- Communication among data stewards and building a group help promote teamwork. It initiates communication about data policies, standards, terminologies, and best practices.
Moreover, many other frameworks make data stewardship more practical and easier to implement. There is a framework established that allows data to achieve a competitive edge and increase business value. This strategy includes the following points:
- In first phase, program is built based on the problems faced by stakeholders, coworkers, the internal audit department, and privacy and compliance offices. It will help solve the data problems.
- In the second phase, a budget is made, showing the business value and value point of organization, involving business stewards and stakeholders. Working groups must be formed to create data standards, including roles and responsibilities.
- In the last step, the program is set to work. Data therapy is provided to sponsors and detractors. Moreover, the strategy is maintained by evaluating and updating.
How Data Deduplication and Data Stewardship Help in Golden Record Creation
Data stewardship and data deduplication are essential parts of master data management. Data deduplication helps remove duplicates created in the master data. The data of different customers or companies are gathered individually at each department. When that data is gathered to create a master database, many duplicate entries exist. It reduces the efficiency, consistency, and accuracy of master data. Data duplication is an effective strategy that helps remove duplicates and maintain a single source of truth. This single source of truth creates a discrete data set that establishes a "golden record" of data.
The main concern is establishing and maintaining the golden record by matching and merging the records generated from multiple data sources. Effective master data management is based upon combining similar records automatically. Moreover, an effective MDM system also enables the data stewards to function and create the best records.
Data stewardship allows the practical implementation of knowledge of a specific data set for the correctness of a record. Data stewards can also recognize the accuracy of records. To achieve the golden record, the system or data stewards need to consider the user, the value of a data system with the highest reliability, and the principles of defining great importance for each field.
Data stewardship and data deduplication must work together to resolve conflicts and inconsistencies between data sets.
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