Business Intelligence Roadmap: The Complete Project Lifecycle for Decision-Support Applications

Things to Consider

Hardware

What hardware platforms do we already have or use?

On which platform should we implement the BI application?

Do we need new hardware? What will it cost?

Will we need more staff to maintain the new hardware?

Will the new hardware integrate with our existing platforms?

How will the new hardware scale to accommodate ever-increasing loads of processing and volumes of data?

Network

What type of local area network (LAN) are we using?

What type of wide area network (WAN) are we using?

Is the bandwidth of our WAN sufficient to grow?

Middleware

What type of middleware do we already have or use?

Do we have the necessary middleware to retrieve the source data from heterogeneous platforms and transfer it to the BI decision-support environment?

What is the operational source architecture? (e.g., enterprise resource planning [ERP], legacy files)

Do we need new middleware? What will it cost?

Will the connection be permanent between the source files (or source databases) and the BI target databases?

Which of our hardware, software, and middleware is proprietary? Have we purchased it? Or are we leasing it?

Database Management Systems

What DBMSs do we already have?

Will we need to buy a new DBMS? What will it cost?

Will the new DBMS be compatible with our operating system(s)?

What software tools can run with it?

Does our staff have the skills to use and administer the new DBMS?

Will we have to hire more database administrators?

Tools and Standards

How are the business analysts currently analyzing the data? What reporting and querying tools do they use?

What additional tools and utilities do we need?

What other software do these tools need to interact with?

Do we know of any major problems with our technical infrastructure?

What are our technical standards for compatibility and access?

The development efforts of early BI applications, such as the early data warehouses, were relatively slow, labor- intensive , risky, and expensive. Extraction and transformation of operational data into a data warehouse frequently involved creating new, custom-written application code. The target databases were either based on proprietary DBMSs or were using proprietary hardware platforms. There was also a shortage of tools to administer, control, and expand the new decision-support environment. The lesson learned from the early BI days was that in order to reach the best performance results for data access and retrieval, a comprehensive application platform must be chosen . Therefore, it is important to select the appropriate hardware, middleware, and DBMS and to ensure that these components are implemented properly.

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