Business Intelligence and Analytics

Business intelligence systems focus on improving the access and delivery of business information to both information providers and information consumers. This section addresses the architecture patterns relevant to implementing a

business intelligence infrastructure to support reporting and analysis of information combined from multiple organizations as well as individual reporting requirements that are shared among organizations.

Business intelligence systems are typically designed to process and analyze large volumes of information using a variety of different tools. A business intelligence system must, therefore, provide scalability to support growing information volumes and be able to support and integrate products from multiple vendors.

A business intelligence system may also provide access to business information through the use an information catalog that documents decision support objects that can be employed by information consumers to answer the main business questions that arise in everyday business operations. These objects may be

combined and presented via a user interface called a dashboard. Some systems also provide information consumers with the ability to subscribe to the information they require, and the system delivers it to them at predefined intervals.

To support reporting of information combined from multiple organizations, the Business Intelligence and Analytics pattern presupposes the existence of a common data warehouse to store the required information. The information stored in a data warehouse will be sourced from operational databases most likely via the

Population pattern and will be structured in such as way as to facilitate inquiry, reporting and analysis. This will thereby reduce the impact on an organization’s operational transaction systems.

Figure 5–13 Business Analytics Patterns

5.5.2 Extract, Transform, Load

The process of moving data from its location in an operational system involves transforming it into the required format for the target data warehouse, and loading it into the target data warehouse. Even though the three steps may be performed on different systems or in different locations, there are often referred to together as extract, transform and load, or ETL.

Individual organizations will most like assume responsibility for providing data feeds to the central data warehouse. These may be created as a part of the

organization’s own ETL process or by some other means that will depend highly on the application software and database technology of the organization. For example, many commercial hospital systems provide the capability to generate extracts of HL-7 data that could be used as input to the central warehouse. The format of the data and the communications means will again be up to the individual organization. The transform process takes the incoming data feed and processes that data

according to predetermined criteria. For example, name and address data may be normalized before being loaded. In addition, data may be aggregated or analyzed prior to loading. Finally, the load phase writes the data to the local data

warehouse.

5.5.3 Reporting

Business intelligence reports will draw on the data loaded into the common data warehouse and will be presented to the information consumer via a web browser. Reporting will mostly likely consist of predefined reports and answer specific business questions although an online query capability may be employed as well.

In addition, reports may be available via subscription and will most likely be delivered via e-mail.

5.5.4 Analytics

Analytics typically refers to the processing, summarization, and categorization of data necessary to produce actionable insights from that data. With source data available in a data warehouse, apart from operational systems, sophisticated routines can be created using COTS business intelligence and analytics tools. The output of these routines can be presented to users in a way such that additional analysis can be performed in real-time using online analytical processing tools (OLAP).

5.5.5 Implementation Considerations

The intent of this pattern is to provide a common infrastructure to support business intelligence and analytics that draw upon data from multiple organizations or are common across multiple organizations. It is not intended to replace an

organization’s existing business intelligence and analytics infrastructure or functionality.

Figure 5–14 Business Intelligence and Analytics Implementation

The primary assumption is that the infrastructure will include some form of shared storage, shown above as a common data warehouse. Data will be provided by

source applications or local data warehouses via a variety of means. For example, organizations that are able can invoke common web services to transmit files or XML documents to the shared infrastructure. This is depicted in the notional

infrastructure as a local system invoking a Web service via its own Enterprise Service Bus and transmitting data in this fashion.

Other organizations may provide data via file transfer. This is depicted in the notional infrastructure as an organization providing an extract of its operational data and sending it to the shared infrastructure by some means. This may include use of file transfer protocol (ftp), e-mail or some means of manually sending the data to the shared infrastructure (e.g., FedEx).

All data will be copied initially to a staging are in the common infrastructure. From there data will be extracted, transformed and loaded (ETL) into the common data warehouse. COTS tools are available that provide a facility to map incoming data against its data targets, perform necessary transformations, such as harmonization or aggregation, and load the data into storage.

It is also likely that COTS business intelligence and analytics tools will be hosted in the shared infrastructure. Such tools will be the primary means for display, inquiry and reporting of data from the common data warehouse. Most COTS tools provide the capability to present data via a web browser. In addition, COTS tools typically provide the ability to query and analyze data online via a web browser. Finally, the capability should exist to generate structured extracts or aggregations of the data that can be sent to other organizations for additional processing and reporting. For example, data may be aggregated across a state and extracted for reporting to the Federal government.

5.5.6 Infrastructure Components Used by the Business Intelligence and Analytics Pattern

• Enterprise Service Bus • Federated Single Sign-on

• Data Warehouse/ETL COTS Software • Business Intelligence/Analytics COTS Tools • Web Servers