Business Intelligence Reporting

Business Intelligence and Analytics

• Reports can be categorized into two types: routine reports and ad hoc (or on-demand) reports.
• Multilingual support is available for reports.
• Scorecards and dashboards are used to present data.
• Report delivery and alerting can be done through various touchpoints, including self-subscription and administrator-based distribution.

Ad Hoc Query

• An ad hoc query is a query that cannot be determined prior to the moment the query is issued.
• Structured Query Language (SQL) is a data definition and management language for relational databases.

Multidimensionality

• Multidimensionality is the ability to organize, present, and analyze data by several dimensions.
• Examples of dimensions include sales by region, by product, by salesperson, and by time.
• Multidimensional presentation involves dimensions, measures, and time.

Multidimensional Database

• A multidimensional database is a database that is organized specifically to support easy and quick multidimensional analysis.
• A data cube is a two-dimensional, three-dimensional, or higher-dimensional object that represents a measure of interest.

Cube

• A cube is a subset of highly interrelated data that is organized to allow users to combine any attributes in a cube with any metrics in the cube.

Limitations of Dimensionality

• Multidimensional databases can take up significantly more computer storage room than summarized relational databases.
• Multidimensional products cost significantly more than standard relational products.
• Database loading consumes significant system resources and time, depending on data volume and the number of dimensions.
• Interfaces and maintenance are more complex in multidimensional databases than in relational databases.

Data Mining and Predictive Analysis

• Data mining is the process of analyzing data to identify patterns and relationships.
• Predictive analysis is the use of tools to determine the probable future outcome for an event or the likelihood of a situation occurring.

Data Visualization

• Data visualization is the graphical, animation, or video presentation of data and the results of data analysis.
• Data visualization can provide competitive advantage by quickly identifying important trends in corporate and market data.

New Directions in Data Visualization

• Data visualization has moved into mainstream computing, where it is integrated with decision support tools and applications.
• Intelligent visualization includes data interpretation.

Dashboards and Scorecards

• Dashboards are used daily in organizations to provide a more operational view of success than scorecards' focus on strategic goals.
• Dashboards provide a foundation for better decision making and more efficient day-to-day management of teams, resources, and expenses.

Geographical Information System (GIS)

• A GIS is an information system that uses spatial data, such as digitized maps.
• GIS applications are used to improve decision making in the public and private sectors, including dispatch of emergency vehicles, transit management, facility site selection, and drought risk management.

GIS and Decision Making

• GIS applications are used to improve decision making in the public and private sectors, including local governments.
• GIS can help managers determine where to locate retail outlets.

GIS and the Internet/Intranets

• Most major GIS software vendors provide Web access that hooks directly to their software.
• GIS can be deployed on the Internet for internal use or for use by customers.

Real-time BI

• Real-time BI involves the trend toward BI software producing real-time data updates for real-time analysis and real-time decision making.
• Concerns about real-time systems include data update frequency, data consistency, and real-time data necessity.

Automated Decision Support (ADS)

• ADS is a rule-based system that provides a solution to a repetitive managerial problem.
• ADS is also known as enterprise decision management (EDM).

Characteristics and Benefits of ADS

• ADS is suitable for decisions that must be made frequently and/or rapidly, using information that is available electronically.
• ADS provides rapid decision-making capabilities.

Capabilities of ADSs

• ADS can rapidly build rules-based applications and deploy them into almost any operating environment.
• ADS can inject predictive analytics into rule-based applications.

ADS Applications

• ADS applications include product or service configuration, yield (price) optimization, routing or segmentation decisions, corporate and regulatory compliance, fraud detection, dynamic forecasting, and operational control.

Implementing ADS

• Software companies provide components to ADS, including rule engines, mathematical and statistical algorithms, industry-specific packages, enterprise systems, and workflow applications.

Competitive Intelligence

• Many companies continuously monitor the activities of their competitors to acquire competitive intelligence.
• Competitive intelligence drives business performance by increasing market knowledge, improving knowledge management, and raising the quality of strategic planning.

Using the Web in BA

• Web analytics involves the analysis of data that occurs in the Web environment.
• Clickstream data provides a trail of the user's activities and show the user's browsing patterns.

Usage of BA

• Almost all managers and executives can use some BA systems, but some find the tools too complicated to use or are not trained properly.
• Most businesses want a greater percentage of the enterprise to leverage analytics.

Success and Usability of BA

• Performance management systems (PMS) are BI tools that provide scorecards and other relevant information that decision makers use to determine their level of success in reaching their goals.

Why BI/BA Projects Fail

• Reasons for failure include:
  • Failure to recognize BI projects as cross-organizational business initiatives.
  • Unengaged or weak business sponsors.
  • Unavailable or unwilling business representatives.
  • Lack of skilled or available staff.
  • Suboptimal staff utilization.
  • No software release concept.
  • No work breakdown structure.
  • No business analysis or standardization activities.
  • No appreciation of the negative impact of "dirty data" on business profitability.
  • No understanding of the necessity for and the use of metadata.