Data integration challenges are a significant concern, as indicated by a survey of Salesforce users. Among the respondents, 54% highlighted their main hurdle as integrating applications and data sources.
Several factors contribute to the persisting difficulty of data integration and potential reasons why Salesforce data integrations fail
Complex Systems: Gathering data from numerous systems that employ various technologies and span different locations is intricate. The process is compounded by the sheer scale, volume, and intricacy, necessitating careful planning and coordination.
Data Mapping: Aligning data fields, which are often named and structured differently across data sources, presents a tough challenge. Some sources might even be outdated systems with substantial data gaps. Addressing these problems requires close collaboration between business and technical experts familiar with data intricacies.
Expertise Acquisition: Successful integration between Salesforce and a data warehouse calls for proficiency in Salesforce, CRM, data warehousing (or data lakes), and integration technologies. Building and facilitating effective communication among such a diverse team can be demanding.
Uniform Integration Strategy: While Salesforce is cloud-native, other sources may exist in the cloud or on-site. This mix requires different integration approaches, potentially leading to inconsistent data handling and jeopardizing data quality. Crafting a unified strategy that ensures data consistency and synchronization despite varying systems is a formidable task.
Ongoing Integration: Data integration is not a one-off endeavor. Beyond the initial data import, a continuous effort is essential to keep the shared repository updated with changes.
Despite these challenges, integrating Salesforce data remains pivotal for achieving a coherent data perspective within an organization. Overcoming these obstacles is attainable with a well-defined integration strategy and the utilization of appropriate data integration tools.
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