Introduction
Implementing a customer data management system can be the difference between success and failure in terms of leveraging an organization's customer relationship management (CRM) system. Since customers drive profitability, organizations need a way to provide their employees with a single view of the customer and to provide that customer with above-average customer service. Unfortunately, this is not always the case. Disparate applications such as billing and call center systems do not always feed into one another, and even when they do, lack of data cleansing and management can cause employees to see only a portion of a customer's history, interactions, or profiles. A widely used example is that of an organization sending multiple marketing brochures to one customer because of inaccuracies and lack of customer data synchronization. A more alarming example is having more than one customer record for a specific customer, with the collections department calling that customer to collect on an account that is actually current.
Why do CRM initiatives fail? Because implementing a system to manage customers does not guarantee that CRM applications will work successfully within the organization. The old adage—"garbage in, garbage out,"—definitely applies to the realm of CRM. If organizations do not have clean, reliable, centralized data, their customer view will not be complete or accurate, and their business goals will not be achieved. Consequently, customer data integration (CDI) has become an essential component of an organization's management of data, along with any CRM initiative.
This article will provide an overview of CDI within CRM, and see how it differentiates itself from the general data integration industry. Additionally, the components of CDI will be explored, to identify the important areas that should be considered when implementing master data management (MDM) for CRM within the organization. Finally, key vendors in the industry and their key product features will be identified.
Defining Customer Data Integration
Within CRM, CDI is the management and consolidation of customer information from across the organization. This includes, but is not limited to, information stored in call centers, sales and marketing departments, and accounts receivables and payables. CDI ensures that each department requiring customer contact has access to timely data, to provide employees with a complete view of customer profiles or histories. This creates a standardized view of each customer and promotes positive customer interactions.
Most enterprise organizations have built or acquired their computer applications over an extended period of time, creating a series of complex systems that work independently or that interoperate with one another. Even if these systems have high interoperability, many times the business rules and data structures of each application and business unit have not been taken into account, as they were developed independently of one another. This means that data may be captured in different ways. For example, customer address information and name may be recorded in different formats within different business units. When data is pulled from one system to another, this particular customer information may not be synchronized.
CDI and Data Integration
CDI represents a consolidated view of customer data. Aside from MDM, which looks at the whole organization, data integration generally focuses on specific initiatives, and is the type of software used to perform data transfers, consolidations, etc. Thus, when an organization is looking to implement a CDI initiative, its focus should involve identifying the data integration vendors that specialize in CRM or that focus specifically on validating and consolidating customer data.
Data integration is defined as the act of bringing together or moving data from one or multiple locations to a centralized or replicated data store. The development of a data warehouse and the consolidation of information across the organization is an example of how data integration is applied in organizations. Sub-sets of data from disparate locations within the organization are loaded into the centralized structure of a data warehouse or dedicated database. This centralized structure creates a specified view of data to measure an organization's performance, to generate reports, to provide analytics, and so forth.
Not all data integration is equal when it comes to CDI. Different forms of data integration are used within different industries and for diverse initiatives. For example, when implementing a business intelligence (BI) solution, data mapping, data cleansing, and hourly data loads are likely the most important factors to consider. Also, different vendors within the data integration space may specialize in sub-categories such as data quality, and may partner with larger industry- or solution-specific vendors to have their solutions embedded within larger software packages. This gives organizations the ability to mix and match solutions based on their needs.
Considerations for CDI
CDI requires specialized data integration solutions. Aside from the general data integration requirements such as data extraction, data standardization, data transformation, and data load functionality, CDI solutions offer additional data cleansing, data profiling, and data mapping to guarantee that a universal view of the customer exists in a centralized structure. CDI solutions also match, merge, and link records, differentiating them from other data integration or data quality vendors.
* Data cleansing and standardization is used to identify and define data definitions and standardize customer information. A common customer data standardization initiative includes creating a single view of customer name and address information across the organization. Data cleansing activities include removing duplicate records as well as fixing common spelling errors. Within CDI, the importance of cleansing and standardization activities takes center stage within integration efforts, since "a single version of the truth" is what organizations need to implement a successful CDI initiative.
* Data profiling identifies statistics about the data available in existing databases. Two main aspects of data profiling that are essential for a successful CDI implementation are interdependency and redundancy profiling. Checking for interdependency among tables within different databases across the organization will create similar database structures. For example, a customer number should be attached to the customer table in order to link each customer appropriately based on order, billing, and call center information. Data redundancy profiling identifies duplicate records or overlapping values between tables. This eliminates the possibility of sending out multiply flyers to an individual customer.
* Data mapping helps ensure the data elements are the same across disparate systems, and mapped where appropriate. For example, it is important that the customer first and last name link up the same way in each disparate system, to guarantee that the correct information is being merged. Included in data mapping are the linking and matching of customer records in order to confirm that the right data is being attached to the right customer when centralized in a hub or data store.
* Data quality is a unique area within data integration. There are select vendors that compete solely in the data quality space, or that partner with broader data integration vendors to provide data quality functionality. The activities identified above are components of developing data quality management by profiling, standardizing, and monitoring the quality of data.
Implementing an initial CDI initiative is just the first step in providing the proverbial single customer view. The ability to keep data cleansed and to monitor changes in data quality over time to improve the process is a critical success factor of CDI.
SOURCE:
http://www.technologyevaluation.com/research/articles/customer-data-integration-a-primer-19942/
Implementing a customer data management system can be the difference between success and failure in terms of leveraging an organization's customer relationship management (CRM) system. Since customers drive profitability, organizations need a way to provide their employees with a single view of the customer and to provide that customer with above-average customer service. Unfortunately, this is not always the case. Disparate applications such as billing and call center systems do not always feed into one another, and even when they do, lack of data cleansing and management can cause employees to see only a portion of a customer's history, interactions, or profiles. A widely used example is that of an organization sending multiple marketing brochures to one customer because of inaccuracies and lack of customer data synchronization. A more alarming example is having more than one customer record for a specific customer, with the collections department calling that customer to collect on an account that is actually current.
Why do CRM initiatives fail? Because implementing a system to manage customers does not guarantee that CRM applications will work successfully within the organization. The old adage—"garbage in, garbage out,"—definitely applies to the realm of CRM. If organizations do not have clean, reliable, centralized data, their customer view will not be complete or accurate, and their business goals will not be achieved. Consequently, customer data integration (CDI) has become an essential component of an organization's management of data, along with any CRM initiative.
This article will provide an overview of CDI within CRM, and see how it differentiates itself from the general data integration industry. Additionally, the components of CDI will be explored, to identify the important areas that should be considered when implementing master data management (MDM) for CRM within the organization. Finally, key vendors in the industry and their key product features will be identified.
Defining Customer Data Integration
Within CRM, CDI is the management and consolidation of customer information from across the organization. This includes, but is not limited to, information stored in call centers, sales and marketing departments, and accounts receivables and payables. CDI ensures that each department requiring customer contact has access to timely data, to provide employees with a complete view of customer profiles or histories. This creates a standardized view of each customer and promotes positive customer interactions.
Most enterprise organizations have built or acquired their computer applications over an extended period of time, creating a series of complex systems that work independently or that interoperate with one another. Even if these systems have high interoperability, many times the business rules and data structures of each application and business unit have not been taken into account, as they were developed independently of one another. This means that data may be captured in different ways. For example, customer address information and name may be recorded in different formats within different business units. When data is pulled from one system to another, this particular customer information may not be synchronized.
CDI and Data Integration
CDI represents a consolidated view of customer data. Aside from MDM, which looks at the whole organization, data integration generally focuses on specific initiatives, and is the type of software used to perform data transfers, consolidations, etc. Thus, when an organization is looking to implement a CDI initiative, its focus should involve identifying the data integration vendors that specialize in CRM or that focus specifically on validating and consolidating customer data.
Data integration is defined as the act of bringing together or moving data from one or multiple locations to a centralized or replicated data store. The development of a data warehouse and the consolidation of information across the organization is an example of how data integration is applied in organizations. Sub-sets of data from disparate locations within the organization are loaded into the centralized structure of a data warehouse or dedicated database. This centralized structure creates a specified view of data to measure an organization's performance, to generate reports, to provide analytics, and so forth.
Not all data integration is equal when it comes to CDI. Different forms of data integration are used within different industries and for diverse initiatives. For example, when implementing a business intelligence (BI) solution, data mapping, data cleansing, and hourly data loads are likely the most important factors to consider. Also, different vendors within the data integration space may specialize in sub-categories such as data quality, and may partner with larger industry- or solution-specific vendors to have their solutions embedded within larger software packages. This gives organizations the ability to mix and match solutions based on their needs.
Considerations for CDI
CDI requires specialized data integration solutions. Aside from the general data integration requirements such as data extraction, data standardization, data transformation, and data load functionality, CDI solutions offer additional data cleansing, data profiling, and data mapping to guarantee that a universal view of the customer exists in a centralized structure. CDI solutions also match, merge, and link records, differentiating them from other data integration or data quality vendors.
* Data cleansing and standardization is used to identify and define data definitions and standardize customer information. A common customer data standardization initiative includes creating a single view of customer name and address information across the organization. Data cleansing activities include removing duplicate records as well as fixing common spelling errors. Within CDI, the importance of cleansing and standardization activities takes center stage within integration efforts, since "a single version of the truth" is what organizations need to implement a successful CDI initiative.
* Data profiling identifies statistics about the data available in existing databases. Two main aspects of data profiling that are essential for a successful CDI implementation are interdependency and redundancy profiling. Checking for interdependency among tables within different databases across the organization will create similar database structures. For example, a customer number should be attached to the customer table in order to link each customer appropriately based on order, billing, and call center information. Data redundancy profiling identifies duplicate records or overlapping values between tables. This eliminates the possibility of sending out multiply flyers to an individual customer.
* Data mapping helps ensure the data elements are the same across disparate systems, and mapped where appropriate. For example, it is important that the customer first and last name link up the same way in each disparate system, to guarantee that the correct information is being merged. Included in data mapping are the linking and matching of customer records in order to confirm that the right data is being attached to the right customer when centralized in a hub or data store.
* Data quality is a unique area within data integration. There are select vendors that compete solely in the data quality space, or that partner with broader data integration vendors to provide data quality functionality. The activities identified above are components of developing data quality management by profiling, standardizing, and monitoring the quality of data.
Implementing an initial CDI initiative is just the first step in providing the proverbial single customer view. The ability to keep data cleansed and to monitor changes in data quality over time to improve the process is a critical success factor of CDI.
SOURCE:
http://www.technologyevaluation.com/research/articles/customer-data-integration-a-primer-19942/
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