Business Intelligence Architecture – What, Why, and How

This week we have a guest post by Manjunath Hegde, who has over a decade’s experience in Business Intelligence and working with analytics related technologies. He is currently enrolled into the Executive Program in Business Analytics by Jigsaw Academy and MISB Bocconi.

Rome wasn’t built in a day, and neither is a business intelligence system of any organization. It requires lot of strategies and efforts to build such a solution.

To start with the effort of building business intelligence systems, one needs a framework comprising of best practices, policies, and standards. Business intelligence architecture, by providing this framework, ensures that the development efforts of multiple projects fit neatly together as a cohesive whole to achieve the desired BI system.

Let us briefly explore the architecture of a business intelligence system.

Why do we need Business Intelligence Architecture?

Much before an organization starts adopting a business intelligence architecture, there are series of indicators which accelerate the case for building a BI system. There are many important factors, but the key ones include:

• Backlog of business requests: IT department is under a lot of pressure to fulfil the report requests from various business users.
• Need for self-service BI: Business users are stuck as they need to depend on IT for even minor pieces of information. This hinders their decision-making process and forms a bottleneck for smooth operation.
• Messed up IT system: Silos of data, different data formats, disparate data and applications – these will form a complex IT system, building a justified case for a stronger BI infrastructure.
• Cost: Cost of maintaining information silos and feeding to huge number of IT resources for even small sets of data is detrimental to an organization.

These factors push the organizations to build a business intelligence architecture that will seek to help them make better decisions. A solid architecture will help in structuring the process of improving business intelligence and helps implement the Business Intelligence strategy in a very cost effective way.

BI architecture, among other elements, often includes both structured and unstructured data. This data comes from both internal and external sources and are transformed from raw transaction data into logical information.

Components of Business Intelligence Architecture

One mistake that top leaders of many organization make is think of their BI system as equivalent to front-end BI tools being used. Then there is another set of technical geeks who make lot of discussion about a business intelligence architecture around some fancy jargons without giving due importance to what exactly comprises BI architecture.

The key elements of a business intelligence architecture are:

• Source systems
• ETL process
• Data modelling
• Data warehouse
• Enterprise information management (EIM)
• Appliance systems
• Tools and technologies

Source Systems – Transaction Processing Systems

This is the starting point for any BI initiative. Organization data is first created in these databases. Point to note: if you do not capture the data in the operational system, you can’t analyse it.

Operational systems (OLTP) form the bulk of the data needed for the data warehousing. In addition to that, source systems may also include data from secondary sources such as market data, benchmarking data etc. Business Intelligence architecture should address all these various data sources which are of different formats and standards.

ETL Process

In an ETL process data is extracted from the operational systems and loaded into a data warehouse. ETL, which stands for Extract Transform Load, is usually done using custom solutions available in the market. IBM Websphere Data Stage, Oracle Data Integrator, Ab Initio, and Microsoft Integration Services are examples of such tools.

Data Modeling

Data modeling will help to address what exactly is needed from data sources, the format of the data, and how it will be related to other data elements. It is not feasible to extract everything from a source system as that comes with cost issues. Data modeling will help to organize the data and therefore will minimize cost of storage replication, and effort needed to build a data warehouse.

Data Warehouse

Warehouse will have data extracted from various operational systems, transformed to make the data consistent, and loaded for analysis. A data warehouse will help in achieving cross-functional analysis, summarized data, and maintaining one version of the truth across the enterprise.

Enterprise Information Management (EIM)

EMI is another BI jargon which may stump some beginners. The term usually refers to ETL tools, data modeling tools, data quality, data profiling, metadata management, and master data management.

BI Hardware

It is important to make decisions on the hardware requirements to maintain a high performance and scalable BI system. Apart from server configurations, we have data warehouse appliances to combine the server, the database, and the data storage into one system. Netezza and DATAllegro are some well-known appliances in the market.

Tools and Technologies

Another important component of business intelligence architecture is what tools and technologies to implement. It is not just the front-end UI tools, but the tools used for EIM as well. There are cloud solutions, SaaS model, many full-fledged BI solutions (such as MSBI, Oracle BI suites, Microstrategy and more) to choose from. BI framework should have guidelines to make decisions on what is required for the organization.

This article has been adapted (with permission) from the original article posted on BusiTelCe.