Master Your Data with Comprehensive Master Data Management Solutions
With Match & Merge, you can unify and streamline data from multiple sources into a single, accurate, and reliable master record. This empowers better decision-making, boosts operational efficiency, and strengthens data governance across your organization. Our advanced matching engines and conflict resolution protocols ensure a seamless, precise unification process, supporting data accuracy and consistency essential for scaling insights and business impact.
Deterministic Matching Engine
Our solution allows you to create and apply deterministic rules that are key to maintaining consistent and accurate master data throughout the organization. This ensures that your data remains reliable and uniform, supporting better decision-making and smoother operations.
Deterministic rules are based on the exact matching of specific data attributes, such as unique identifiers (e.g., customer IDs, product SKUs), names, or addresses. These rules ensure that identical records from different sources are recognized and merged with high precision.
Users can define precise conditions that must be met for records to be considered the same. For example, a rule might state that two customer records should be unified only if their names, birthdates, and addresses match exactly. This helps prevent false positives and ensures only truly identical records are merged.
The tool allows users to prioritize specific data attributes when applying deterministic rules. For instance, when merging records, a rule might prioritize the most recent update or the most trusted data source. It ensures the best data is retained in the master record.
Deterministic rules are automatically applied during the data unification, ensuring consistent and repeatable results. This automation reduces the need for manual intervention and speeds up the MDM process.
In cases where deterministic rules identify conflicts between data sources, the tool provides options for resolving these conflicts based on predefined rules or user-defined preferences. This ensures that the final master record is both accurate and reliable.
The tool tracks the application of deterministic rules, providing a clear audit trail. Users can review how and why specific records were merged, split, or modified, ensuring transparency and accountability in the MDM process.
Probabilistic Matching Engine
Our system uses advanced rules to tackle the challenges of managing master data, especially when dealing with incomplete, inconsistent, or messy information. Instead of relying on exact matches, it identifies and unifies records based on likelihood, ensuring a more accurate and cohesive view of your data.
Probabilistic rules use fuzzy matching techniques to compare data attributes that may not exactly match but are likely to represent the same entity. For example, slight variations in names (“John Doe” vs. “Jon Doe”) or address formats (“123 Main St.” vs. “123 Main Street”) are recognized as potential matches.
The tool calculates similarity scores for data attributes, assigning a probability that two records refer to the same entity. Users can define thresholds for these scores to determine when records should be merged or flagged for review, balancing accuracy with coverage.
Probabilistic rules allow users to assign different weights to various data attributes based on their importance in identifying a match. For example, an exact match on a social security number might be weighted more heavily than a partial match on a last name, increasing the likelihood of correct unification.
The self-healing tool uses machine learning models to continually improve its probabilistic matching over time. It recognizes patterns by analyzing historical data and learning from data steward actions. It gets smarter at deciding when to merge or separate records, making the process more accurate and efficient with each use.
In cases where the probability of a match falls within a specific range, the tool can be configured to either automatically merge records or flag them for manual review. This flexibility helps manage the uncertainty inherent in probabilistic matching while ensuring data quality.
The tool tracks the application of deterministic rules, providing a clear audit trail. Users can review how and why specific records were merged, split, or modified, ensuring transparency and accountability in the MDM process.
The tool tracks all decisions by probabilistic rules, providing a clear audit trail. Users can review how probabilities were calculated, why certain records were merged, and adjust rules or thresholds to improve outcomes.
Identity Resolution
Identity resolution involves identifying and merging records that refer to the same entity across various data sources to create a holistic and accurate view of that entity. This process involves several key stages working together to ensure reliability and clarity.
Create this as a step by step process arhestation
Records are grouped into blocks based on shared characteristics using standard blocking techniques. This initial step narrows down the comparison space, and token-based blocking further refines the process by focusing on specific tokens or attributes.
Records within each block are prepared for detailed comparisons, which may involve additional data cleansing or deduplication to enhance data quality.
Various algorithms, such as Jaro Winkler, Edit Distance, and Soundex, are employed to compare records within blocks. Each attribute of the records is scored based on similarity to help identify potential matches.
Tools like itertools and network graph techniques compare records pairwise, detecting clusters that likely represent the same entity.
After clustering, the system uses predefined match-merge rules to determine each record’s status: auto-merge, possible-merge, or no-match. Records identified as auto-merge are further processed based on trust score configurations to create or update the golden record, ensuring it accurately represents the entity.
Data Stewardship
Records categorized under possible merge are queued for manual review through the Data Stewardship Portal, allowing data stewards to make final determinations on merges based on a more nuanced understanding of the data.
Survivorship Rules
Users can configure survivorship rules to determine which data attributes should be retained when conflicting information is found across different sources. This ensures that the most accurate, complete, and reliable data is preserved in the master record.
Golden Record Creation
The data unification process results in the creation of a “golden record”—a single source of truth that represents the most accurate and comprehensive view of each entity (e.g., customer, product, supplier) within the MDM system.