Enterprise data has two broad contexts: operations and analytics. The operations context supports organizational operational activities such as sales, marketing, operations, finance, and customer service. The analytic context supports organizational analytic and data-science requirements—within and outside operational business processes—regardless of where the data comes from. This delineation leads to distinct, fit-for-purpose data infrastructures: Analytical Data Infrastructure (ADI) and Operational Data Infrastructure (ODI).
Since their advent, enterprise applications were positioned and assumed to be the preeminent organizing principle for IT. As a result, shared enterprise data and analytics evolved into a “no-man’s land” from which competing organizational points of view arise that cannot be resolved below the enterprise level. Organizations seeking to become more data driven will find that this ad-hoc, application-centric approach proves progressively inadequate over time.
The start of this journey to no-man’s land differs, depending on the age and nature of the enterprise. For example, older product- and manufacturing-centric enterprises started with enterprise resource planning (ERP). Newer, smaller customer-centric organizations started with one or more options, including customer relationship management, salesforce automation, marketing automation, and e-commerce.You do not have permission to access this document. Make sure you are logged in and/or please contact Danielle with further questions.