business intelligence architecture

Importance of a BI architecture

A business intelligence architecture articulates the technology standards and data management and analytics practices that support an organization's BI efforts, as well as the specific platforms and tools that will be deployed. It serves as a technology blueprint for collecting, organizing and managing BI data and then making the data available for analysis, data visualization and reporting. A strong BI architecture also incorporates policies to govern the use of the technology components.

Putting such a framework in place enables a BI team to work in a coordinated and disciplined way to build an enterprise BI program that meets its organization's data analytics needs. The BI architecture also helps BI and data managers create an efficient process for handling and managing the data that's pulled into the environment.

Enterprises benefit from an effective BI architecture by using the insights generated by business intelligence tools to make data-driven decisions that help increase revenue and profits. To ensure their needs are met, C-suite executives, business managers and other business users who rely on data analysis to formulate strategies and guide their decision-making should have a stake in creating the architecture.

Business intelligence architecture components and diagram

A BI architecture can be deployed in an on-premises data center or the cloud. In either case, it contains a set of core components that collectively support the different stages of the BI process, from data collection, integration, storage and analysis to data visualization, information delivery and the use of BI data in business decision-making.

As shown in the accompanying business intelligence architecture diagram, the core components include the following items.

• Source systems. These are all of the systems that capture and hold the transactional and operational data identified as essential for the enterprise BI program -- for example, ERP, CRM, finance, manufacturing and supply chain management systems. They can also include secondary sources, such as market data and customer databases from outside information providers. As a result, both internal and external data sources are often incorporated into a BI architecture.

Important criteria in the data source selection process include data relevancy, data currency, data quality and the level of detail in the available data sets. In addition, a combination of structured, semistructured and unstructured data types may be required to meet the data analysis and decision-making needs of executives and other business users.

• Data integration and cleansing tools. To effectively analyze the data collected for a BI program, an organization must integrate and consolidate different data sets to create unified views of them. The most widely used data integration technology for BI applications is extract, transform and load (ETL) software, which pulls data from source systems in batch processes. A variant of ETL is extract, load and transform (ELT), in which data is extracted and loaded as is and transformed later for specific BI uses. Other methods include real-time data integration, such as change data capture and streaming integration to support real-time analytics applications, and data virtualization, which combines data from different source systems virtually.

A BI architecture typically also includes data profiling and data cleansing tools that are used to identify and fix data quality issues. They help BI and data management teams provide clean and consistent data that's suitable for BI uses.

• Analytics data stores: This encompasses the various repositories where BI data is stored and managed. The primary one is a data warehouse, which usually stores structured data in a relational, columnar or multidimensional database and makes it available for querying and analysis. An enterprise data warehouse can also be tied to smaller data marts set up for individual departments and business units with data that's specific to their BI needs.

In addition, BI architectures often include an operational data store that's an interim repository for data before it goes into a data warehouse; an ODS can also be used to run analytical queries against recent transaction data. Depending on the size of a BI environment, a data warehouse, data marts and an ODS can be deployed on a single database server or separate systems.

A data lake running on a Hadoop cluster or other big data platform can also be incorporated into a BI architecture as a repository for raw data of various types. The data can be analyzed in the data lake itself or filtered and loaded into a data warehouse for analysis. A well-planned architecture should specify which of the different data stores is best suited for particular BI uses.

• BI and data visualization tools. The tools used to analyze data and present information to business users include a suite of technologies that can be built into a BI architecture -- for example, ad hoc query, data mining and online analytical processing, or OLAP, software. In addition, the growing adoption of self-service BI tools enables business analysts and managers to run queries themselves instead of relying on the members of a BI team to do that for them.

BI software also includes data visualization tools that can be used to create graphical representations of data, in the form of charts, graphs and other types of visualizations designed to illustrate trends, patterns and outlier elements in data sets.

• Dashboards, portals and reports. These information delivery tools give business users visibility into the results of BI and analytics applications, with built-in data visualizations and, often, self-service capabilities to do additional data analysis. For example, BI dashboards and online portals can both be designed to provide real-time data access with configurable views and the ability to drill down into data. Reports tend to present data in a more static format.

Other components that increasingly are part of a business architecture include data preparation software used to structure and organize data for analysis and a metadata repository, a business glossary and a data catalog, which can all help users find relevant data and understand its lineage and meaning.