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Enterprise architecture (EA) is a business function concerned with the structures and behaviours of a business, especially business roles and processes that create and use business data. The international definition according to the Federation of Enterprise Architecture Professional Organizations is "a well-defined practice for conducting ...
Another example of possible implied criticism of some EA practitioners: Many novice EA practitioners comment that they find the DODAF too complex for a starting point to build an enterprise architecture. Other practitioners find the DODAF a good source of product description information to get them started. —
Business analytics (BA) refers to the skills, technologies, and practices for iterative exploration and investigation of past business performance to gain insight and drive business planning. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods .
Enterprise architecture regards the enterprise as a large and complex system or system of systems. [3] To manage the scale and complexity of this system, an architectural framework provides tools and approaches that help architects abstract from the level of detail at which builders work, to bring enterprise design tasks into focus and produce valuable architecture description documentation.
For example, some EA artifacts can describe the current state of an organization as well as the planned changes to this state in both the short-term and mid-term future, while other EA artifacts can describe some timeless imperatives for an organization which were relevant in the past, are relevant now and will be relevant in the future
Writing for the Harvard Business Review, [10] Davenport provides a summary of the three analytics maturity levels of any organization. Analytics 1.0 organizations are those where management has acquired the ability to rely on internal data for decision making, rather than mere intuition.
Then is called a pivotal quantity (or simply a pivot). Pivotal quantities are commonly used for normalization to allow data from different data sets to be compared. It is relatively easy to construct pivots for location and scale parameters: for the former we form differences so that location cancels, for the latter ratios so that scale cancels.
The maturity levels for business intelligence are: operational reporting; analytic reporting; business dashboards; analytic applications; It may extend further to predictive analytics, or predictive analysis may form part of the analytic application - depending on both the subject matter under analysis, and the nature of the analysis required.