SAS Business Intelligence gives you ... and delivering self-service reporting and analysis, IT spends less time responding to requests...


Microsoft Business Intelligence


Open Source Business Intelligence Solutions


Business intelligence (BI) is a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions. BI applications include the activities of decision support systems, query and reporting, online analytical processing (OLAP), statistical analysis, forecasting, and data mining. Business intelligence applications can be


The term business intelligence (BI) refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business information and also sometimes to the information itself. The purpose of business intelligence is to support better business decision making. more


Business intelligence is the technology and practice of applying information to make decisions. In this way, business intelligence is different than data warehousing, which is more about storing information. It is also more comprehensive than data mining. Information shows its real value when many people can use and share it. This is the goal of business intelligence..contd

Business Intel

Definition: Business Intelligence (BI) is an umbrella term applied to methods, applications and technologies used to gather, integrate, present and analyse business information to improve decision making.


Business Intelligence

Business Intelligence Solutions (BIS) lead to better business decision making through providing access to enterprise data for easy analysis against Key Performance Indicators (KPIs). This is achieved through having more information available at all levels of an enterprise and enabling each management level to be more responsive to current market trends. Every aspect of the business can be co-ordinated efficiently and dealt with at various levels of management.

As technology has improved, the volume of information available for analysis has increased significantly and more efficient systems have been designed to handle the data collection process. The data collection services and tools available ensure even microscopic pieces of information are included for analysis whereas they would have been ignored previously due to not being cost-effective to collect.

BIS plays a strong role for an enterprise of any size. The development of automated collection tools has helped reduce the time cost and monetary cost of intelligence gathering. A smart business will look at evaluating every piece of data individually and collectively to help make more informed decisions. BIS enables collective data to be analysed for trends and then for every subset of data to be drilled down and analysed individually.

Business Intelligence comprises the following main elements:

1. Analytics
2. Customer Relationship Management (CRM)
3. Dashboards
4. Data Warehouse
5. Data Integration
6. Data Management
7. Data Mining
8. Extract, Transform and Load (ETL)
9. Online Analytical Processing (OLAP)
10. Business Performance Management (BPM)
11. Reporting
12. Scorecard

Implementing BIS in Enterprise

There will be a significant cost of implementing any BIS if existing applications exist for any part of the overall process. The ideal scenario would be to use solutions for each aspect of BIS from the same vendor or where they are proven to be able to integrate with other vendors. In some cases, it may be more cost-effective migrating existing processes into a bespoke system to facilitate better control and understanding. This will reduce the training costs associated with training new personnel as they will only be required to learn one system as opposed to multiple existing systems.

After the decision has been taken to implement business intelligence solutions, a set of criteria must be addressed from the outset in order to gain the most benefit. The most important areas for consideration are:

1. Response Time
Considerations will need to account for data capture time, ETL processing time, caching and reporting time in addition to user expectations. For example, if the service is offered to clients and they are informed statistics are updated in "real-time", they do not expect to be waiting for a few minutes every time they login to check stats.

It would be unwise to have every piece of data update in "real-time" as it may cause too much load on the server and result in reliability issues. Instead, only the absolute necessary pieces of information should be updated in "real-time". This area needs to be clearly defined in the design process - what constitutes essential information.

2. Data Refresh Rates
Automated queries and database dumps can be routinely scheduled to take snapshots of actual statistics to reduce potential problems with the live data capture. Providing only the vital information is extracted, it can be set to retrieve a database dump every minute if necessary. The data integration tools should be designed to have minimal impact on a server when importing new data sets to facilitate smooth data exchanges. If designed correctly, management would be querying backup data from the latest data refresh rather than causing additional load on the live server.

3. Visual vs. Analytical Dashboards
Statistical information is essential to represent the current state of affairs and should be available for drilldown analysis. However, a visual dashboard should suffice to present a quick overview of affairs with any changes highlighted. It is especially useful for  CEOs to see actual performance against KPIs instantly.

4. Data Delivery
It isn't necessary for every managerial level to have access to all the data collected, but it is necessary for them to have access to all data relevant to their decision making. In this instance, BIS must be designed from the outset to have flexibility in assigning different roles. A bespoke solution that enables new roles to be created where specific sets of data can be extracted and delivered for analysis without needing to cross reference with other departments is essential.

5. Scalability
After an initial assessment of enterprise requirements it is still important to consider scalability issues and possible future requirements. Any BIS implementation should adequately provide the capability for future modification and expansion without posing any significant risk to current procedures and management requirements.

The criteria above is not extensive but does cover the most common considerations that can be overlooked when designing bespoke BIS applications. The more thorough the planning undertaken before designing a bespoke solution, the more useful and cost-effective the end solution will be.

The future of Business Intelligence

With the advent of new technology enabling more efficient data capture and processing, traditional business intelligence has started to shift from reactive to proactive in the sense artificial intelligence (AI) can aid decision making when certain conditions exist. This helps to free up more time for managers to focus on other areas like staff motivation and training.

There will continue to be a need for managers to analyse data against KPIs, but the majority of decisions can be automated through use of AI and alerts sent out for manual intervention if the data produces any anomalies. The role of artificial intelligence should be defined from the outset in order to be able to manually disable or adjust it according to enterprise goals. Failure to adequately define the AI role can lead to a loss of control in the decision making process.

Overall, business intelligence and BIS form an integral part of every enterprise and if used correctly will help improve efficiency and help meet both short-term and long-term objectives. (See open source solutions to Business Intelligence)