Customer Data Integration (CDI) - Revision history

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User at 17:20, 11 October 2019

2019-10-11T17:20:22Z

User: Customer Data Integration (CDI) is the sum of procedures, automation, and skills that are required to standardize and integrate customer data that originates from different sources. CDI is an essential element of customer relationship management (CRM).

2018-12-18T19:35:55Z

Customer Data Integration (CDI) is the sum of procedures, automation, and skills that are required to standardize and integrate customer data that originates from different sources. CDI is an essential element of customer relationship management (CRM).

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 ===See Also===
-[[Application_Integration|Application Integration]]<br />
+Customer Data Integration (CDI) is a process that consolidates data about customers from various sources to provide a single, comprehensive view. This unified data can then be used across the organization to improve customer service, enhance decision-making, and effectively tailor marketing efforts. CDI combines data from disparate systems such as CRM, sales, marketing, and customer support platforms, ensuring data consistency, accuracy, and accessibility. Effective CDI strategies enable businesses better to understand customer behaviors, preferences, and trends, leading to improved customer experiences and business outcomes.
-[[Enterprise_Application_Integration_(EAI)|Enterprise Application Integration (EAI)]]<br />
-[[Enterprise_Information_Integration_(EII)|Enterprise Information Integration (EII)]]<br />
+*[[Data Warehouse]]: Discussing centralized repositories that store data from multiple sources in a single location for reporting, analysis, and data mining. Data warehousing is a foundational technology for CDI, enabling the consolidation and management of customer data.
-[[Business_Process_Integration|Business Process Integration]]<br />
+*[[Master Data Management (MDM)]]: Covering the process of defining and managing an organization's critical data (such as customer and product data) to provide, with the help of a single point of reference, an authoritative view of this data. MDM is closely related to CDI, focusing on creating a single master record for each customer.
-[[Enterprise_Integration|Enterprise Integration]]<br />
+*[[Data Quality]]: Explaining the importance of accuracy, completeness, reliability, and relevance of data. Data quality initiatives are crucial for successful CDI, ensuring that integrated customer data is accurate and actionable.
-[[Data_Cleansing|Data Cleansing]]<br />
+*[[Data Governance]]: Discussing the overall management of the availability, usability, integrity, and security of data used in an organization. Data governance frameworks support CDI by establishing policies and procedures for data management and use.
-[[Data_Mining|Data Mining]]<br />
+*[[Customer Relationship Management (CRM)]]: Covering the strategies and technologies used by companies to manage and analyze customer interactions and data throughout the customer lifecycle. CDI enhances CRM systems by providing a unified view of customer data across different touchpoints.
-[[Data_Management|Data Management]]<br />
+*[[Business Intelligence]] (BI) : Discussing the technologies, applications, strategies, and practices used to collect, integrate, analyze, and present business information. BI benefits from CDI through access to comprehensive and integrated customer data for analysis and decision-making.
-[[Data_Warehouse|Data Warehouse]]<br />
+*Data Integration Tools and Technologies: Covering the software solutions used to aggregate, cleanse, and consolidate data from disparate sources. These tools are critical for implementing CDI strategies.
-[[Customer_Relationship_Management_(CRM)|Customer Relationship Management (CRM)]]<br />
+*Big Data Analytics: Discussing the process of examining large and varied data sets to uncover hidden patterns, customer preferences, and other insights. CDI feeds into big data analytics by providing a unified data source.
-[[Analytical_CRM|Analytical CRM]]<br />
+*[[Customer Data Platform (CDP)]]: Explaining platforms that create a persistent, unified customer database accessible to other systems. CDPs are a solution for achieving CDI by aggregating and organizing customer data across channels.
-[[Collaborative CRM (CCRM)]]<br />
+*[[Real-Time Processing]]: Covering the capabilities for processing data as it becomes available, enabling immediate analysis and action. Real-time data processing is increasingly important in CDI for delivering up-to-date customer insights.
-[[Customer_Data_Management_(CDM)|Customer Data Management (CDM)]]<br />
+*[[Privacy Policy]] and Data Protection Regulations: Discussing the legal frameworks such as GDPR (General Data Protection Regulation) and CCPA (California Consumer Privacy Act) that impact the collection, storage, and use of customer data. CDI must comply with these regulations to ensure customer data is handled ethically and legally.
-[[Customer_Retention|Customer Retention]]<br />
+*Personalization and [[Customer Experience (CX)]]: Explaining how integrated customer data can be used to tailor interactions and services to individual customer needs and preferences, enhancing the overall customer experience.

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 *Custom-Built Data Hub: This style reflects the historical way of building customer hubs using [[software]] tools and custom-coded rules. Building such a hub requires the use of an [[Extract,_Transform,_Load_(ETL)|Extract,Transform,Load (ETL)]] tool to bring data into the hub's data model from multiple sources and [[Data_Quality|Data Quality (DQ)]] tools to cleanse and match similar records within the hub. These matched records are then merged based on simple, custom-coded rules, which generally results in the [[system]] choosing one record over another. Typically, ongoing code development is needed to integrate this custom hub to downstream systems, to make changes to match and merge rules and to add new data sources.
 This tools-based approach is the least adaptable to business changes and has severely limited extensibility to new data sources.
-*Fixed Transaction Hub: Several [[application]] vendors like Siebel and Oracle offer this style of CDI hub which is built on a comprehensive but fixed data model and developed to support a specific set of applications. Despite its richness, this data model may not encapsulate all the relevant attributes required for unified customer views and needed by disparate downstream systems. To build the hub, all data from contributing sources is conformed to this data model, which may require extending the existing model and creating extensive ETL scripts to bring in data sources outside of the [[vendor]]'s model. This makes it difficult to upgrade the hub to the next version of the software product and may require specific tools to systematically update their normalized data model - which results in longer times for data loads. Once data is cleansed, matched and merged, it becomes an [[Operational_Data_Store_(ODS)|operational data store (ODS)]] that serves up-to-date customer data, to multiple applications. The entire customer data - reference, relationship and transaction - required for a unified view is persisted in advance and tied to the hub's fixed data model. As a result, this style is best suited when there are only a few operational applications - identified in advance - supported by the hub's fixed data model and when the business does not anticipate significant changes (i.e. additions of new applications/data sources).
+*Fixed Transaction Hub: Several [[application]] vendors like Siebel and Oracle offer this style of CDI hub which is built on a comprehensive but fixed data model and developed to support a specific set of applications. Despite its richness, this data model may not encapsulate all the relevant attributes required for unified customer views and needed by disparate downstream systems. To build the hub, all data from contributing sources is conformed to this data model, which may require extending the existing model and creating extensive ETL scripts to bring in data sources outside of the [[vendor]]'s model. This makes it difficult to upgrade the hub to the next version of the software product and may require specific tools to systematically update their normalized data model - which results in longer times for data loads. Once data is cleansed, matched and merged, it becomes an operational data store (ODS) that serves up-to-date customer data, to multiple applications. The entire customer data - reference, relationship and transaction - required for a unified view is persisted in advance and tied to the hub's fixed data model. As a result, this style is best suited when there are only a few operational applications - identified in advance - supported by the hub's fixed data model and when the business does not anticipate significant changes (i.e. additions of new applications/data sources).
 *Match and Link Cross-Reference Hub: Also referred to as the "registry" style, this approach is offered by certain best-of-breed CDI vendors that historically provided matching tools. As a result, the data model of the customer hub contains only the selective attributes needed to match similar records across multiple data sources. The hub matches against these attributes and links the matched records to create a customer identity master store. This hub physically stores only the global ID, the cross-references ("links") back to source systems and any mappings/transformations necessary to achieve semantic reconciliation. With this style, [[standard]] DQ and ETL tools may be used to cleanse the source data and transfer the data from the sources to the system area for matching. Since there is neither any resolution of matched yet conflicting records, nor any history of past data states, this style cannot offer the best, resolved view of customer master data. While this style offers beguilingly fast performance with low product "footprint," it does so at the expense of critical functionality - primarily because little data is being stored or managed. Similarly, this style is highly scalable as long as functionality remains limited to persisting cross-reference links and no other data is stored/accessed (i.e. transaction or meta-data). Data stewardship is also limited since there are no merged master records to manage. Net, this style doesn't create a full transactional hub for serving complete customer views, or an IT platform for developing and delivering data services to downstream systems.
 *Adaptive Transaction Hub: This style emerged most recently to address the limitations of the above approaches. With the adaptive style, the hub is built as a platform for consolidating data from disparate third party and internal sources and for serving unified customer views to operational applications, analytical systems or both. This hub is data-model-neutral and uses templates, tailored to each [[industry]], which allows enterprise-specific data models to be implemented quickly. With the data model defined, data is loaded using standard ETL tools and cleansed via integrated third party DQ tools. Beyond just matching similar records, it merges matched records to build a "best-of-breed" master record that reflects the best-version-of-truth - at the cell-level - across multiple source systems. In effect, becoming the customer master or "system-of-record" for all systems. This approach delivers a real-time hub that has a reliable, persistent foundation of master reference and relationship data, along with all the history and lineage of data changes needed for audit and [[compliance]] tracking. On top of this persistent master data foundation, the hub can dynamically aggregate transaction data - on [[demand]] - from different source systems to compose/deliver unified customer views to downstream systems. The scalability and performance characteristics of an adaptive transaction hub can also be altered - at multiple levels - to fit business requirements. Once built, it delivers unified views to portals or embeds them within applications. Data can also be accessed through batch interfaces, published to a message bus or served through a real-time services layer. As a platform, new data sources can be readily added in this approach by extending the data model and by configuring the new source mappings, meaning that all legacy data hubs - CIF systems, identity and data cleansing hubs - can be leveraged to contribute their records/rules into the new transaction hub. Finally, through rich-user-interfaces for data stewardship, it allows exception handling by business analysts to keep it current with business rules/practices while maintaining the reliability of best-of-breed master records. Designed as a manageable, extensible and scaleable platform, the adaptive transaction hub serves as the most reliable foundation for delivering trusted unified customer views to all systems.
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 [[Analytical_CRM|Analytical CRM]]<br />
 [[Collaborative CRM (CCRM)]]<br />
 [[Customer_Data_Management_(CDM)|Customer Data Management (CDM)]]<br />
 [[Customer_Retention|Customer Retention]]<br />

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 *An “application”
 *An analysis tool
-*An [[Operational Data Store (ODS)]]
+*An Operational Data Store (ODS)
 *The automation of a customer data [[model]]

@@ Line 89: / Line 89: @@
 [[Customer_Relationship_Management_(CRM)|Customer Relationship Management (CRM)]]<br />
 [[Analytical_CRM|Analytical CRM]]<br />
-[[Collaborative_CRM|Collaborative CRM (CCRM)]]<br />
+[[Collaborative CRM (CCRM)]]<br />
 [[Operational_CRM|Operational CRM]]<br />
 [[Customer_Data_Management_(CDM)|Customer Data Management (CDM)]]<br />