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
>>Chapter 12 : Business Intelligence User Model
>>Chapter 13 : Business Intelligence Issues and Challenges<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
- Creating Cost Effective Enterprise friendly BI solution
Creating Cost Effective Enterprise friendly BI solution
You already know that selecting the right BI solution is important. The BI tools give a wide variety of functionality varying from the simple reports to drill-down analytical solutions aimed at particular industries and operational environments. While selecting a Business Intelligence solution, firms will have to answer two main questions and they are the following:
1. What kind of data has to be analyzed and from where does it have to come from? There are many packaged application and database vendors which include some BI functionality in their core product. If there is plan to source all the data from the same application or database then there may not be a need to buy these additional products. Yet this strategy may limit the analytical range.
2. Who will do the analysis and how do they have to obtain the results?
Previously, the report or analysis requests used to be sent to the IT department, who would then program codes and then create the report. Today, the BI is on the front lines of business and the tools would then be used by executives or sales and marketing professionals. As a result, firms have to know the technical capabilities of the end user upfront.
In order to construct a Business Intelligence solution, the enterprises will have to consider new investments and upgrades to the present technology to set out the BI technology stack. The technology stack is planned to highlight the different layers of the technology that can be affected by a BI project, from the hardware hosting the data at the bottom of the stack to the portal product used to provide data to the users at the top. Starting from the bottom, this seven-layer stack consists of the following:
1. Storage and Computation of Hardware: To apply BI, organizations have to invest or upgrade their data storage infrastructure. This includes Storage Area Networks (SAN), Network Attached Storage (NAS), Hierarchical Storage Management (HSM), and the silo-style tape libraries. The trend over the next few years is for the storage resources to be combined into a single, policy-managed and enterprise-wide storage pool.
2. Applications and Data Sources: To develop a useful BI solution, source data will have to be cleansed and organized. The challenge is that source data may come from any number of applications, mostly using proprietary data formats and application-specific data structures. Customer Relationship Management (CRM), Supply Chain Management (SCM), Enterprise Resource Planning (ERP) systems, and other applications are the some of the common sources of data. The trend for the next few years is for the applications to standardize the data format using extensible Markup Language (XML) schema and influence the BI specific standards like XML for Analysis.
3. Data Integration: Middleware allows different systems supporting different communication protocols, interfaces, object models, and data formats to communicate. The firms will have to invest in the ‘connectors’ to allow the data from the source applications to be integrated with the BI repository. Extraction, Transformation and Loading (ETL) tools will pull the data from the multiple sources, and then load the data into a data warehouse. Again, the trend in data integration and Enterprise Application Integration is towards the standardization through XML and web services.
4. Relational Databases and Data Warehouses: Firms will require a data warehouse to store and organize the tactical or historical information in a relational database. Organizing the data in this way allows the user to extract and assemble specific data elements from a complete dataset to perform a range of analyses.
5. OLAP Applications and Analytic Engines: Online Analytic Processing (OLAP) applications give a layer of separation between the storage repository and the end user’s analytic application of choice. Its role is to carry out special analytical functions which will require high-performance processing power and more specialized analytical skills.
6. Analytic Applications: Analytic applications are the programs which are used to run queries against the data to perform either ‘slide-and- dice’ analysis of historical data or the more predictive analyses, often referred to as ‘drill-down’ analysis. For example, a customer intelligence application can allow a historical analysis of all customer orders and the payment history. Otherwise, the users could drill down to understand how changing a price may affect the future sales in a specific region.
7. Information Presentation and Delivery Products: Query results can be returned to the user in a number of ways. Many tools provide presentation through the analytic application itself and offer the dashboard formats to combine multiple queries. Also, enterprises can purchase packaged or custom reporting products, such as Crystal Reports. An important trend in the BI presentation is leveraging XML to deliver analyses through a portal or any other Internet-enabled interface, such as a personal digital assistant (PDA).
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