This post is part of Series Business Intelligence – Tools & Theory
Currently running topic for this series is listed as below :
Series Business Intelligence – Tools & Theory
>>Chapter 1 : Business Intelligence an Introduction
>>Chapter 2 : Business Intelligence Essentials
>>Chapter 3 : Business Intelligence Types
>>Chapter 4 : Architecting the Data
>>Chapter 5 : Introduction of Data Mining
>>Chapter 6 : Data Mining Techniques
>>Chapter 7 : Introduction to Data Warehousing
>>Chapter 8 : Different Ways of Data Warehousing
>>Chapter 9 : Knowledge Management
>>Chapter 10 : Data Extraction
>>Chapter 11 : Business Intelligence Life Cycle<You are here>
Continuing from my previous post on this series, If you have missed any link please visit link below
We are going to Cover the Following Points in this article
- BI Development Stages and Steps
BI Development Stages and Steps
BI lifecycle has six stages for any project. Within every stage, various methods are carried out to check the completion of the project.
Following are the important stages:
· Justification Stage: This stage is also called as the Business Case Assessment. Here the business issues or business prospect is described and a BI solution is planned. Every BI application release must be economical and must precisely identify the profits of either resolving a business issue or taking benefit of a business prospect.
· Planning Stage: This stage is also called as the Enterprise Infrastructure. Because BI is a cross-organizational assessment support solution, a project infrastructure should be present or be designed as the BI applications are produced.
A project infrastructure has two constituents:
o Technical infrastructure which consists of hardware, software, middleware, database management systems, operating systems, network models, meta data storehouse and functions.
o Non-technical infrastructure which consists of meta data standards, data naming standards, project data structural design (evolving), procedures, guiding principles, testing methods, change control process, problem managing methods and dispute resolution processes.
· Project Planning: BI projects are tremendously active and have changes to scope, workforce, finances, equipment, customers and supporters can rigorously affect the achievement of the project. As a result, project scheduling must be comprehensive, and actual improvement must be strictly monitored and reported.
· Business Analysis Stage: This stage is also called as the Project Delivery needs. Examining is one of the most complicated tasks for BI applications. The need to have everything immediately is hard to expect. On the other hand, keeping the scope small is one of the significant factors to describe the needs for every deliverable. These needs must be accepted to change all through the development cycle as more is learned regarding the probabilities and the restrictions of the technology.
· Data Analysis: The major challenge to every BI project is the quality of the resource information. The bad practices developed from years are hard to eradicate, and it is very complicated and takes a long time to discover and fix the problem developed from the bad practices. In spite of it, data evaluation which was done previously was restricted to one line-of-business customer‟s vision and was never prepared to accept with other vision in the organization. This step will take an important proportion of time in the whole project plan.
· Application Prototyping: Analysis for the efficient deliverables, previously called system analysis, is best done during prototyping. Currently there are equipment’s and various programming languages that facilitate the designers and developers to show or negate a conception or proposal comparatively quickly. Prototyping also permits the users to observe the potential and the restrictions of the technology.
This provides them a chance to modify their delivery needs and their predictions.
· Meta Data Repository Analysis: Using more equipment means having supplementary technological meta data along with the business meta data, which is generally captured in a modeling CASE (computer- aided software engineering) equipment. This meta data requires to be arranged with other meta data and saved in a repository. Meta data repositories can be obtained or developed. In both the cases, the needs for what sort of meta data to collected and saved should to be documented in a meta model. Along with that, the needs for sending meta data to the customers have to be examined.
· Design Stage: This stage is also called as the Meta Data Repository Design. If a meta data repository is obtained, it will be most possible to be widened with characteristics that are necessary for BI applications. If a meta data repository is developed, the database has to be planned with regards to the meta model designed during the earlier step.
· Database Design: Here more than one database will be accumulated in the form of business data in a very comprehensive form, depends on the reporting needs of the customers. All the reporting needs are not planned, and all of them are not multidimensional. The database development plan must go with the access needs of the business.
· ETL Design: This method is the one of the difficult processes of the whole BI project. Extraction, Transformation, and Load processing time frames are usually minute. However, the bad quality of the resource information generally takes a long time to carry out the alteration and cleaning processes. It is a challenge for the majority of the organizations to complete the ETL method in the obtainable timelines.
· Construction Stage: This stage is also called as the ETL Development stage. Various types of equipment’s are used for this method, few are complicated, and few are effortless. Depends on the data cleaning and data transformation needs designed through the data analysis process, an ETL tool may or may not be the top solution. In both the cases, pre- processing the data and defining extensions to the tool abilities are regularly essential.
· Application Development: When the prototyping attempt has concluded the functional delivery specifications, actual development will start on either the same customer access and investigation tools, such as OLAP tools, or on other tools. This task is generally carried out in corresponding to the meta data repository and ETL performances.
· Data Mining: Various organizations will not utilize their BI databases to their maximum potential. Actually utilization is often restricted to prewritten information. Here few of them not even new variety of reports, but substitution of old reports. The actual benefits for BI applications arrive from the business intelligence unseen in the organization’s data, which could only be found out with data mining tools.
· Meta Data Repository Development: If the conclusion is made to develop a meta data repository than purchasing a new one, a different team is generally charged with the development procedure. It becomes a huge subproject of the whole BI project.
· Deployment Stage: This stage is also called as the Implementation.
When all features of the BI application are systematically tested, users must be well trained and the support functions started. These functions involve customer support, safeguarding of the BI target databases, planning and performing ETL batch jobs, performance evaluation, and database tuning.
· Release Evaluation: By means of an application release conception, it is significant to gain knowledge from lessons studied on the earlier projects. Any equipment’s, procedures, guiding principles, and developments that were not supportive should be re-evaluated and modified, if necessary even trashed. Any missed targets, cost issues difference of opinions and their resolutions should be evaluated. Modifications to the procedures have to be done prior to the next release.
The above mentioned steps not necessarily carried out in an order. Though, there is a usual order of development from one stage to other, certain dependences occur between some of the development stages.
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