Profile unification using a custom implementation

Introduction

Profile unification, also known as the Golden Record, provides a single, persistent, and trusted view of each individual. This document presents a comprehensive overview of a custom implementation of profile unification within the framework of Redpoint Data Management (RPDM) and its integration within the broader Redpoint Customer Data Platform (CDP) ecosystem.

In contrast to the traditional out-of-the-box (OOTB) job framework, this tailored approach harnesses the power of:

• Custom data pipelines

• Metadata-driven match logic

• Configurable survivorship rules

• Bespoke orchestration

These elements address the unique data quality, governance, and identity resolution requirements specific to enterprise environments.

Objectives

The goals of a custom profile unification process are to:

• Establish a Golden Record that aligns with the enterprise’s business definition of a “unique individual.” This involves creating a comprehensive and accurate representation of each individual within the organization, ensuring that all relevant data points are captured and maintained consistently across various systems. By establishing a Golden Record, we can enhance our understanding of our customers and stakeholders, leading to more informed decision-making and improved service delivery.

• Support dynamic, rule-driven identity resolution adaptable to evolving match policies. This means implementing a flexible system that can adjust to changes in how we define and identify unique individuals. As our business needs and match policies evolve, the identity resolution process must be capable of adapting without significant downtime or manual intervention. This adaptability not only streamlines operations but also ensures that we remain compliant with any regulatory changes regarding data management and privacy.

• Provide full lineage transparency and granular survivorship metadata to track the origin and history of data points associated with each individual. By offering full lineage transparency, we can ensure that all data is traceable back to its source, which enhances accountability and trust in our data management processes. Granular survivorship metadata allows us to understand which data points are prioritized in case of discrepancies, ensuring that the most accurate and relevant information is utilized in decision-making.

• Optimize processing through incremental delta logic and scalable orchestration. By employing incremental delta logic, we can efficiently process updates and changes to our data without needing to overhaul entire datasets. This approach minimizes resource consumption and maximizes efficiency. Additionally, scalable orchestration ensures that our systems can handle increasing volumes of data and user interactions seamlessly, allowing for growth without compromising performance.

• Integrate seamlessly with existing data lakes, MDM systems, or marketing ecosystems to connect and collaborate with our current data infrastructure, maximizing the value of our data assets. By ensuring that our solutions can easily integrate with data lakes, Master Data Management (MDM) systems, and marketing ecosystems, we can create a more cohesive and streamlined data environment. This integration not only enhances data accessibility but also fosters collaboration across departments, enabling us to leverage insights more effectively and drive strategic initiatives forward.

Architecture overview

Core layers

The custom implementation uses a layered architecture built in RPDM:

Data standardization and preparation

Custom pipelines perform advanced preprocessing before match logic:

• Parsing & tokenization: Implementing custom SQL transforms involves splitting compound fields such as “First Last” and “City/State” into distinct, manageable components. By doing so, we enhance the clarity and usability of our datasets, allowing for more precise data manipulation and analysis.

• Normalization: The normalization process includes removing unnecessary punctuation, standardizing casing for uniformity, trimming excess whitespace, and unifying common abbreviations—for instance, converting "St." to "Street." Such practices not only improve data quality but also facilitate smoother integration and interoperability between various data sources.

• Data quality scoring: Assigning a Data Quality (DQ) score to each attribute includes metrics such as the Email_Valid_Flag and Phone_Confidence_Score. By quantifying data quality, we can identify areas for improvement and ensure that our datasets meet the required standards for accuracy and reliability. This proactive approach to data quality management helps mitigate risks associated with poor data.

• Alias handling: In a multi-channel environment, maintaining alternate identifiers such as email addresses, phone numbers, or loyalty IDs enables seamless linkage across different channels, ensuring that customer interactions are cohesive and personalized. By effectively managing these aliases, we can enhance customer experiences and improve our overall service delivery.

• Audit stamping: To ensure traceability and accountability in our data processes, each record must include essential metadata such as Load_ID, Source_System, and Processing_Timestamp. This audit stamping not only supports compliance with regulatory requirements but also facilitates troubleshooting and data lineage tracking. By implementing robust audit trails, we can enhance our data governance practices and build trust in our data management systems.

Custom matching logic

Deterministic matching

Deterministic matching is defined through a metadata-driven match rule table (MATCH_RULES), enabling rule management outside code.

Example rule definitions:

Each rule’s output is written to a MATCH_RESULTS table with:

• Source record keys (Record_A, Record_B)

• Match Rule_ID

• Match score

• Confidence level

• Match source (deterministic / probabilistic)

Probabilistic / fuzzy matching

Probabilistic, or “fuzzy,” matching is implemented using Redpoint’s MatchKey and Comparison transforms, enhanced with custom expressions:

• Jaro-Winkler similarity on names: The Jaro-Winkler similarity algorithm is particularly effective for comparing strings, especially names, as it accounts for typographical errors and transpositions. This method provides a score that reflects how similar two names are, making it a valuable tool for deduplication and matching tasks within our organization. By implementing this algorithm, we can enhance our ability to identify similar entries in our databases, ensuring more accurate data management and improved user experiences.

• Phonetic encoding (Soundex / Metaphone) for loose phonetic matching: Phonetic encoding techniques like Soundex and Metaphone allow us to match names and words that sound alike but may be spelled differently. This capability is crucial in our organization, where diverse naming conventions and spelling variations can lead to mismatches in data retrieval. By utilizing these phonetic algorithms, we can significantly reduce the risk of overlooking relevant records, thereby improving our search functionalities and ensuring that users can find the information they need more efficiently.

• Geo-distance scoring for address proximity: Geo-distance scoring is a powerful method for assessing the proximity of addresses based on geographic coordinates. By integrating this scoring system into our processes, we can evaluate how closely related two addresses are, which is particularly useful in logistics, service delivery, and customer relationship management. This approach enables us to optimize route planning, enhance service efficiency, and ultimately improve customer satisfaction by ensuring timely and accurate deliveries.

• Configurable thresholding for match acceptance: Implementing configurable thresholding allows our organization to set specific criteria for what constitutes an acceptable match. This flexibility is essential for tailoring our matching processes to meet varying business needs and contexts. By adjusting the thresholds, we can fine-tune our algorithms to balance between precision and recall, ensuring that we capture all relevant matches without overwhelming our systems with false positives. This adaptability enhances our overall data accuracy and reliability, fostering a more robust decision-making environment.

Clustering

Records are iteratively linked through a transitive closure process:

• If A=B and B=C, then A, B, and C belong to one cluster. This logical relationship highlights the interconnectedness of elements within a dataset, emphasizing the importance of understanding how different components relate to one another. By establishing that if two elements are equal to a third, they can be grouped together, we can identify patterns and relationships that may not be immediately obvious. This clustering approach is essential in various fields such as data analysis, machine learning, and network theory, where recognizing these connections can lead to more effective insights and decision-making.

• Each cluster receives a Cluster_ID, which is a unique identifier written to MATCH_CLUSTERS. The assignment of a Cluster_ID is crucial for tracking and managing clusters effectively. By labeling each cluster, we can easily reference and manipulate them in subsequent analyses or updates. This systematic approach not only enhances organization but also facilitates collaboration among team members who may need to access or work with specific clusters.

• Clustering is persisted for incremental updates to avoid full re-computation. This means that once a cluster is established, any future changes or additions to the dataset can be integrated without the need to start from scratch. This incremental approach saves time and computational resources, allowing for more efficient processing of data. It also ensures that our analyses remain current and relevant, as we can continuously refine our clusters based on new information while maintaining the integrity of previous computations. This method is particularly beneficial in dynamic environments where data is constantly evolving, enabling us to stay agile and responsive to changes.

Golden Record construction

Survivorship rules

Configurable survivorship rules allow organizations to define how conflicting data should be resolved, ensuring that the most reliable information is retained.

Unlike the static OOTB survivorship table, survivorship logic here is dynamically driven by rule metadata stored in SURVIVOR_RULES:

The survivorship job merges all contributing records within a cluster and assigns:

• Golden Record ID (GR_ID)

• Source_Count / Source_List

• Field-level metadata (Rule_Applied, Source_Chosen, Confidence)

Data stewardship layer

Optional manual review layer using Redpoint or an external data stewardship interface:

• Allows business users to have the capability to override matches or survivorship results, providing them with greater control over data accuracy and integrity.

• Records all manual merges and splits in a DATA_STEWARD_ACTIONS table, ensuring thorough audit tracking and accountability for any changes made to the data. This facilitates transparency and allows for easier identification of modifications over time, which is crucial for maintaining high standards of data governance within the organization.

Lineage, audit, and governance

Lineage tracking

Each GR record includes full provenance:

• GR_ID

• Source_Record_Key

• Source_System

• Match_Rule_ID

• Survivorship_Rule_ID

• Timestamp

This allows complete reconstruction of how any Golden Record was formed.

Audit metrics

Custom audit reports track:

• Match rate (% of total records clustered): This metric indicates the effectiveness of our data clustering efforts. A higher match rate signifies that a larger proportion of records have been successfully grouped together, which can lead to more accurate insights and decision-making. Continuous monitoring of this rate allows us to identify trends and areas for improvement in our data management processes.

• Survivorship consistency by attribute: Evaluating survivorship consistency by attribute helps us understand how well our data maintains its integrity over time. This analysis focuses on the reliability of specific attributes across different records, ensuring that the most accurate and relevant information is preserved. By enhancing our data governance practices, we can improve this consistency, leading to better quality records and ultimately more reliable outcomes.

• Delta vs. prior run (new merges/splits): Tracking the delta compared to previous runs provides valuable insights into the dynamics of our data. This metric highlights the new merges and splits that have occurred, allowing us to assess changes in our data landscape. Understanding these fluctuations is essential for adapting our strategies and ensuring that we remain aligned with our organizational goals.

• Golden Record churn rate (records changed since last run): The churn rate of our Golden Records reflects the number of records that have undergone changes since the last processing run, which can impact our operational efficiency and data reliability. By analyzing this churn rate, we can identify patterns of data volatility and implement measures to minimize unnecessary changes, thereby enhancing the overall quality of our data assets.

By focusing on these key metrics, we can drive improvements in our data management practices, leading to more effective decision-making and a stronger foundation for our organizational objectives.

Data governance

Integration with enterprise metadata catalogs (e.g., Collibra, Alation) ensures:

• Clear data definitions for Golden Record attributes are essential to ensure that all stakeholders have a shared understanding of the data being utilized. This clarity helps in maintaining data integrity and consistency across various systems and processes. By establishing precise definitions, we can minimize ambiguity and enhance communication among teams, ultimately leading to more effective data management practices.

• Ownership by domain and data steward plays a crucial role in the governance of our data assets. Assigning clear ownership ensures accountability and facilitates better decision-making regarding data usage and maintenance. Data stewards are responsible for overseeing the quality and compliance of the data within their domain, providing a point of contact for any data-related inquiries. This structure not only empowers individuals but also fosters a culture of responsibility and care for our data resources.

• Compliance with GDPR and CCPA is paramount in today’s data-driven environment. By implementing lineage transparency, we can track the flow of data throughout its lifecycle, ensuring that we adhere to regulatory requirements. This transparency allows us to demonstrate our commitment to data privacy and protection, building trust with our customers and stakeholders. Furthermore, it equips us with the necessary insights to manage data risks effectively and respond promptly to any compliance challenges that may arise.

Incremental refresh and orchestration

Incremental logic

Custom delta handling ensures scalability:

• Process only new or updated records based on the Last_Updated_Timestamp to ensure that our system remains efficient by focusing on the most relevant data, reducing unnecessary processing time and resources.

• Re-evaluate affected clusters only to maintain accuracy and relevance in our data analysis, ensuring that we are not wasting time on static information.

• Preserve GR_IDs for unchanged clusters to maintain the integrity of our data and ensure that we can track and reference clusters effectively, even when they have not undergone any modifications. It allows for seamless integration and continuity in our data management processes.

By adhering to these principles, we can enhance our operational efficiency and ensure that our data management practices align with the overall goals of our organization. This strategic approach not only streamlines our workflows but also enhances the reliability of our data-driven decisions.

Job orchestration

Implemented in RPDM or external orchestration tools (Airflow, Control-M):

1. Load_SourceData

2. Standardize_Data

3. Run_MatchRules

4. Generate_Clusters

5. Apply_Survivorship

6. Build_GoldenRecords

7. Export_IdentityGraph

8. Generate_AuditReports

Downstream publishing

Golden Records are published to target systems:

• Redpoint Interaction (RPI): This system enhances our segmentation and personalization strategies. By leveraging advanced algorithms and data analytics, we can create tailored experiences for our users, ensuring that they receive relevant content and recommendations that resonate with their specific interests and behaviors. The integration of RPI with our existing platforms allows us to refine our marketing efforts and improve customer engagement, ultimately driving higher conversion rates and fostering brand loyalty.

• Data warehouse (Snowflake, Databricks, BigQuery): Our data warehouse solutions, the backbone for our analytics and reporting capabilities, enable us to store vast amounts of data securely while providing the flexibility to analyze it efficiently. By utilizing these tools, we can generate insightful reports that inform strategic decision-making across the organization. Additionally, the ability to perform real-time analytics empowers our teams to respond swiftly to market trends and customer needs, ensuring we remain competitive in a rapidly evolving landscape.

• APIs / Real-time services: The implementation of APIs and real-time services is pivotal for our operations, particularly in areas such as profile lookup, identity graph visualization, and personalization endpoints. These services allow for seamless integration with various applications, facilitating quick access to user profiles and data insights. By visualizing identity graphs, we can better understand customer journeys and interactions, leading to more informed marketing strategies. Furthermore, our personalization endpoints enable us to deliver customized experiences in real-time, enhancing user satisfaction and engagement across all touchpoints.

Exports include both Golden Record and lineage views to preserve traceability.

Advantages of a custom implementation

Summary

A fully custom implementation of Redpoint’s profile unification framework offers maximum control and transparency, making it an exceptional choice for organizations looking to enhance their data management strategies. By externalizing match and survivorship logic into metadata-driven configurations, organizations can achieve a tailored Golden Record architecture that not only reflects their unique business identity model but also maintains the integrity and quality of their data.

This approach allows organizations to define their own rules and processes for data matching and survivorship, ensuring that the Golden Record is not just a generic solution but a customized representation of their operational needs and strategic goals. With Redpoint’s high-performance processing capabilities, organizations can efficiently handle large volumes of data while ensuring high standards of data quality. This is critical in today’s data-driven landscape, where accurate and reliable data is paramount for informed decision-making and effective customer engagement.

Moreover, the flexibility of this design empowers organizations to adapt to changing business requirements and evolving market conditions. As businesses grow and their data needs change, the ability to modify the match and survivorship logic without extensive reconfiguration is invaluable. This adaptability not only saves time and resources but also enhances the overall agility of the organization.

Additionally, the auditable nature of this framework ensures that organizations can track changes and understand the rationale behind data decisions. This transparency is essential for compliance with data governance policies and regulations, providing stakeholders with confidence in the integrity of their data management processes.

In summary, the custom implementation of Redpoint’s profile unification framework represents the most flexible and auditable approach to building the Golden Record within Redpoint CDP. By leveraging this powerful tool, organizations can create a data environment that is not only efficient and reliable but also aligned with their specific business objectives, ultimately driving better outcomes and fostering growth.