BI Straight forward à The combination of applications and technology for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.
From a data analysis perspective, business intelligence is the process of gathering high-quality and meaningful information about the subject matter being researched that will help the individual(s) analyzing the information draw conclusions or make assumptions. For example, one could gather business intelligence on the precious metals industry by conducting research on who mines and processes precious metals, what public markets trade precious metals and what factors influence the valuation and volume of trading of precious metals. All of this information would provide you with an overall understanding of the industry, which you would not have had unless the analysis had been conducted. In addition, you should have sufficient information to assess the viability of investing in precious metals as well as the associated risks.
From an information systems perspective, business intelligence is the system that provides users with online analytical processing (OLAP) or data analysis to answer business questions and identify significant trends or patterns in the information that is being examined. These are information systems that facilitate the data gathering so those users can focus on the business questions they are trying to answer such as: Which products are the best selling and most profitable? Who buys our products by industry category? Who are our best customers and how much do they buy?
During the last 10 years, the names of information systems have changed from executive information systems (EIS) to decision support systems (DSS) and now to business intelligence (BI) systems. But, more has happened than just a name change. The technology has significantly evolved from internally developed graphical user interfaces (GUI) to packaged applications that provide users with easy access to data for analysis.
BI applications provide users with the capability of multidimensional analysis. For example, users can drill down on an income statement moving from net sales to sales by product to sales by product/region and, finally, to sales by product/region/customer. This capability provides users with the ability to answer questions such as: What was the sales mix of products sold? Which geographic regions did we sell the most and the least products? Who are our top customers by geographic region and by product?
The evolution of BI systems has also moved from full-client versions to Web- enabled applications that provide users with the ability to conduct their research through a Web browser and, in certain cases, to work from remote locations. Users also have the capability to create "what-if" scenarios and share them with other users who can then review and make modifications to the document. We are in an exciting time with rapid technology advances that are far extending users’ ability to conduct meaningful research to answer and support their business decisions.
What Is Business Intelligence?
Business intelligence has become a critical element of information technology. It’s an old term with general or even ambiguous meaning. It has been used synonymously with decision support, analysis, and data warehousing, but today business intelligence has a more specific definition and a better understood application. Taken literally, business intelligence is just that—intelligence or understanding of your business. You get that understanding by analyzing your business operations.
That analysis is accomplished by collecting the information that represents your marketing, sales, and service activities, the behavior of your customers in responding to these activities, and the behavior of your internal systems and your suppliers’ systems in responding to your customers’ behavior. Once you have collected this information, and its collection is a continuous process, not a one-time event, you organize and store it in a manner to facilitate its access, processing, and presentation through a broad range of techniques including, reporting, query and analysis, OLAP, and data mining. Finally, you use the results of applying these techniques to improve your business operations and start the analysis cycle all over again.
This business intelligence process can deliver significant, bottom-line results. Implementing its technologies and applying its process can help make your business more effective and more efficient, increasing revenue, decreasing costs, and improving your relationships with customers and suppliers.
The Evolution of Business Intelligence
Business intelligence technologies and business intelligence usage have also become better understood. They have been more efficiently implemented and more effectively applied, too. It wasn’t so long ago that business intelligence was implemented by a loose collection of technologies, deployed only by those companies that seem always to install the latest technologies, applied in ad hoc ways, and used by only a few individuals who were interested enough to develop the skills necessary to use and apply these technologies. We saw pockets or silos of business intelligence technologies and their applications. Benefits achieved were narrow, but potential benefits appeared quite broad.
Today, business intelligence technologies are more tightly integrated and more easily and more widely deployed and used. Business intelligence applications reach to the edges of corporations and beyond corporate boundaries to customers and suppliers.
The current economy has been major driver for these improvements in business intelligence. We are operating in an economic climate that demands more careful justification of technology investments and accelerated returns on them. Companies want to use technology tactically to make their operations more effective and more efficient. Business intelligence can be the catalyst for that efficiency and effectiveness. And, business intelligence has become so much easier to justify and demonstrate accelerated returns.
Business Intelligence Platforms
In order to deliver business intelligence to the widest audience and to maximize the benefits that it can deliver its technologies must be organized. They must be deployed within an infrastructure with the capabilities to implement the business intelligence process that we described above and to support the range of applications best suited to every user of every type. We call that infrastructure a business intelligence platform.
Business Intelligence Platform Requirements
Business intelligence platforms should include the following technologies. Each technology should implement the capabilities described below.
Data Warehouse Databases: A business intelligence platform should support both relational and multidimensional data warehousing databases. In addition, storage models should support the distribution of data across both and data models should support transparent or near-transparent access to data, wherever it’s stored.
A data warehouse is a
• subject-oriented,
• integrated,
• time-varying,
• non-volatile
collection of data that is used primarily in organizational decision making
OLAP: OLAP is a critical business intelligence platform component. It is the most widely used approach to analysis. Business intelligence platforms must provide OLAP support within their databases, OLAP functionality, interfaces to OLAP functionality, and OLAP build and manage capabilities. On-Line Analytical Processing (OLAP) is an element of decision support systems (DSS). It is a decision support database that is maintained separately from the organization’s operational databases.
Data Mining: Data mining has reached the mainstream. It is a critical business intelligence platform capability. Platforms should include data mining functionality that offers a range of algorithms that can operate on data warehouse data.
Interfaces: Business intelligence platforms should provide open interfaces to data warehouse databases, OLAP, and data mining. Where appropriate, interfaces should comply with standards. Open, standards-based interfaces make it easier both to buy and to build applications that use the facilities of a business intelligence platform.
Build and Manage Capabilities: Business intelligence platforms should provide the capabilities to build and manage data warehouses in their data warehouse databases. Build capabilities should include the implementation of data warehouse models, the extraction, movement, transformation, and cleansing of data from operational sources, and the initial loading and incremental updating of data warehouses according to their models. A wide range of data sources should be supported including databases, files, and the data of popular packaged software. Transformation capabilities should be powerful and flexible. Predefined transformations should be packaged. They should be extensible through programming languages. Manage capabilities should cover all platform resources—users, data, and processes. Strong and flexible prepackaged capabilities are essential. Good use should be made of visual tools
The business intelligence platform should provide good integration across these technologies. It should be a coherent platform, not a set of diverse and heterogeneous technologies. For example, a single toolset should provide build and manage capabilities across both relational and multidimensional data warehouses.
Differences between BI (OLAP) and OLTP Systems
OLTP OLAP
User Clerk, IT Professional Knowledge worker
Function Day to day operations Decision support
DB Design Application-oriented Subject-oriented (Star, snowflake)
(E-R based)
Data Current, Isolated Historical, Consolidated
View Detailed, Flat relational Summarized, Multidimensional
Usage Structured, Repetitive Ad hoc
Unit of work Short, Simple transaction Complex query
Access Read/write Read Mostly
Operations Index/hash on prim. Key Lots of Scans
Records accessed Tens Millions
Users Thousands Hundreds
Db size 100 MB-GB 100GB-TB
Metric Trans. throughput Query throughput, response
BI and Decision Support Systems
Decision Support Systems (DSS) are a specific class of computerized information system that supports business and organizational decision-making activities. A properly designed DSS is an interactive software-based system intended to help decision makers compile useful information from raw data, documents, personal knowledge, and/or business models to identify and solve problems and make decisions.
DSS is used to help the knowledge worker (executive, manager, analyst) make faster and better decisions.
• What were the sales volumes by region and product category for the last year?
• How did the share price of computer manufacturers correlate with quarterly profits over the past 10 years?
• Which orders should we fill to maximize revenues?
• Will a 10% discount increase sales volume sufficiently?
• Which of two new medications will result in the best outcome: higher recovery rate & shorter hospital stay?
Business Intelligence (BI) gives you the ability to gain insight into your business or organization by understanding your company's information assets. These assets can include customer databases, supply chain information, personnel data, manufacturing, and sales and marketing activity as well as any other source of information critical to your operation. Business intelligence software allows you to integrate these disparate data sources into a single coherent framework for real-time reporting and detailed analysis by anyone in your extended enterprise – customers, partners, employees, managers, and executives.
Differences b/w Business Intelligence and Decision Support Systems
The main difference is that besides having powerful querying tools the Business Intelligence Systems have also packed modules that are domain or industry dependant, and these modules can be used to provide extra information derived from the data in the data ware house. E.g. Indicators, Market Intelligence, Preemptive Alerting for Sales Intelligence. Although the data ware house systems only have powerful querying tools.
Business intelligence systems are not stand-alone systems, but are integrated into the business process, and support automated closed-loop decision making.
Business intelligence systems focus not only on integrating business information in a data warehouse, but also on providing access to this business information to a wide audience of internal and external users.
One weakness of many data warehouse vendors is that they often focus on technology, rather than business solutions. One distinguishing factor of business intelligence systems is that they focus on providing packaged application solutions in addition to improved technology.
Business intelligence systems focus on improving the access and delivery of business information to a wide audience of both information providers and information consumers. They achieve this by providing online analytical processing (OLAP) and information mining technologies, and packaged applications that exploit the power of those technologies. These applications often need to process and analyze large volumes of information using a variety of different tools. A business intelligence system must, therefore, provide scalability, and must be able to support and integrate products from many different vendors.
Business intelligence applications are evolving to support a closed-loop decision-making process whereby the output of business intelligence applications is routed to operational system users in the form of suggested actions that could be taken to remedy specific business problems.
In summary, business intelligence is the process of gathering meaningful information about the subject matter being researched. Software applications have been developed that provide users with the capability to conduct business intelligence to answer questions and identify significant trends or patterns in the information that is being examined.
Birds Eye Architecture for a BI Platform
Very basic questions that arise in our minds:
What is Legacy or source System?
What is dimensional Modeling?
What is a Data Warehouse?
What is Data Mart?
What is Star Schema?
What is snow flake schema?
What are Dimensions, Level, Fact, Measures?
What is ETL? What are common ETL tools available in the market?
What is Full refresh and incremental Update?
What are incremental Update Strategies?
What is OLAP?
What is a multi-dimensional database?
What OLAP servers are used in the market?
What is aggregation?
What are MOLAP, ROLAP and HOLAP?
What is a cube?
What is dimensions, level, measure, metric, member in an OLAP server?
How can we create a simple Cube?
How can we create a metric?
What is a shared Dimension?
What is a virtual Dimension?
What is a virtual Cube?
What is multi-dimensional Analysis?
What is Drilling?
What is MDX?
How MDX works?
How can I be a part of enterprise BI strategy? Where should I fit in?
Thanks
Syed Saulat
