Sun Data Quality and Load Tools

By default, Sun Master Index uses the Master Index Match Engine and Master Index Standardization Engine to standardize and match incoming data. Additional tools are generated directly from the master index application and use the object structure defined for the master index. These tools include the Data Profiler, Data Cleanser, and the Initial Bulk Match and Load (IBML) tool.

Master Index Standardization EngineThe standardization engine is built on a highly configurable and extensible framework to enable standardization of multiple types of data originating in various languages and counties. It performs parsing, normalization, and phonetic encoding of the data being sent to the master index or being loaded in bulk to the master index database. Parsing is the process of separating a field into individual components, such as separating a street address into a street name, house number, street type, and street direction. Normalization changes a field value to its common form, such as changing a nickname like Bob to its standard version, Robert. Phonetic encoding allows queries to account for spelling and input errors. The standardization process cleanses the data prior to matching, providing data to the match engine in a common form to help provide a more accurate match weight.

Master Index Match EngineThe match engine provides the basis for deduplication with its record matching capabilities. The match engine compares the match fields in two records and calculates a match weight for each match field. It then totals the weights for all match fields to provide a composite match weight between records. This weight indicates how likely it is that two records represent the same entity. The Master Index Match Engine is a high-performance engine, using proven algorithms and methodologies based on research at the U.S. Census Bureau. The engine is built on an extensible and configurable framework, allowing you to customize existing comparison functions and to create and plug in custom functions.

Data ProfilerWhen gathering data from various sources, the quality of the data sets is unknown. You need a tool to analyze, or profile, legacy data in order to determine how it needs to be cleansed prior to being loaded into the master index database. It uses a subset of the Data Cleanser rules to analyze the frequency of data values and patterns in bulk data. The Data Profiler performs a variety of frequency analyses. You can profile data prior to cleansing in order to determine how to define cleansing rules, and you can profile data after cleansing in order to fine-tune query blocking definitions, standardization rules, and matching rules.

Data CleanserOnce you know the quality of the data to be loaded to the master index database, you can clean up data anomalies and errors as well as standardize and validate the data. The Data Cleanser validates, standardizes, and transforms bulk data prior to loading the initial data set into a master index database. The rules for the cleansing process are highly customizable and can easily be configured for specific data requirements. Any records that fail validation or are rejected can be fixed and put through the cleanser again. The output of the Data Cleanser is a file that can be used by the Data Profiler for analysis and by the IBML tool. Standardizing data using the Data Cleanser aids the matching process.

Initial Bulk Match and Load ToolBefore your MDM solution can begin to cleanse data in real time, you need to seed the master index database with the data that currently exists in the systems that will share information with the master index. The IBML tool can match bulk data outside of the master index environment and then load the matched data into the master index database, greatly reducing the amount of time it would normally take to match and load bulk data. This tool is highly scalable and can handle very large volumes of data when used in a distributed computing environment. The IBML loads a complete image of processed data, including potential duplicate flags, assumed matches, and transaction information.