- List Reporting – The most common
usage of Enterprise Reporting is the formatted displays or presentations
of organizational data lists through list, text, graphics or other
rendering formats for periodic business operation. Various levels of
itemized rows and aggregated summaries are typically used in List
Reporting. Data rows and summaries might be assembled from one or more
than one functional discipline areas within the enterprises.
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- Interactive Analysis
– Enterprise
users need to perform analyses upon large sets of data to understand or
find presentations of the data. These analysis typically are interactive
and allow users to directly select dimensions (location, department, time,
etc) to compare measurements (sales growth, cost distributions, amounts
etc). The interactive analysis requires data to be readily available when
different dimensions or measurements are chosen so typically data is
pre-calculated or aggregated using specific data models like OLAP.
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- Metric Management - In many organizations, business performance is managed and measured through
outcome-oriented metrics. These metrics are agreed measurements to track
and compare the business performance over a period of time. Within the
organization, these are mostly called Key Performance Indicators (KPIs). For
external organizations, they are Service Level Agreement (SLAs).
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- Dashboard - Another way for the enterprise to consume their reporting data is publishing
them into customized dashboard views, mostly hosted within the enterprise’s
intranet portal. These dashboards might use graphic to mimic color-coded
auto dashboard indicators for easy but grand overviews of the enterprise’s key
performance.
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- Balance Scorecards - This method attempts to present an
integrated view of success in an organization. In addition to financial
performance, they also include customer, business process, learning and
growth perspectives.
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- Data Mining - When the enterprise holds large
amounts of data, many of them start to analysis this data using Data Mining
techniques that employ neural networks and machine learning to study and find potential
common patterns in the data. Most of the data mining takes time and
dimensions into account to try to predict or forecast the enterprises potential for future growth based on past patterns of performance.
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