Business Intelligence Decision Support Systems – Decision making is often routine and can be done easily, however, when in a position to make a new decision, managers often need information to support the reasoning behind their choices. Here are some of the data problems management faces when making decisions:
Information overload – This is defined as “too much irrelevant information”. Accessing/storing data is now easy in n’ days; data is everywhere. While this can be very valuable, it can also be a failure because too much information is available to many managers that is not relevant to decision making. This can cloud the decision-making process.
Business Intelligence Decision Support Systems
Data Quality – Poor data quality can also negatively impact management decisions, some examples include:
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Certain factors should be considered when choosing a processing method, such as the nature of the transaction, costs and organizational needs. For example, WestJet processes transactions immediately because one seat cannot be sold to two different people, which is inefficient for the organization.
OLAP “provides the ability to add, count, average, and perform other simple arithmetic operations on groups of data.” The report is dynamic, allowing viewers to change the structure of the report online.
“A system that informs improved decision making.” BI systems differ in their characteristics and capabilities, as well as in the ways in which they foster competitive advantage.” Here is a tabular summary of “Characteristics and Competitive Advantage of BI Systems” from the MIS Experience textbook:
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The purpose of a data warehouse “is to extract and clean data from operating systems and other sources, and to store and catalog data for processing by BI tools,” while a data mart is “a collection of data created to fulfill a specific job. function, problem, or opportunity.”
Data mining is “the application of statistical techniques to discover patterns and relationships among data, and for classification and prediction”. These include: A decision support system (DSS) is a computer-based information system that supports decision-making activities in a business or organization; often this results in ranking, ranking, or choosing between alternatives. DSS serves the management, operations, and planning levels of an organization (typically middle and senior management) and helps people make decisions about issues that can change rapidly and that cannot be easily determined in advance.
The data warehouse incorporates best business practices and information systems technologies and requires the cooperation of the business and IT departments, with constant coordination to coordinate all needs, requirements, tasks and results for a successful implementation.
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A successful data warehouse implementation is required when reports from business databases and individual reports require joining multiple tables to obtain related data, so retrieval is slow, naming conventions are often not enforced, and it can be difficult to know when your organization may have Where many business applications run against one or more databases that store the data you need, so the data quality is low and not tracked over time.
Creating a central repository for consolidated and historical data has several advantages over the normalized relational schema presented in the scenarios above, including: simplifying business reporting logic, improving performance, speeding up aggregation, and using Star to provide data capabilities or flake schemas that span multiple business domains.
A star schema divides business process data into facts and dimensions. Fact tables record measurements of specific events and typically consist of numeric and foreign keys for dimensional data that contain descriptive information. Fact tables are usually assigned a surrogate key to ensure that each row can be uniquely identified. This key is a simple primary key, not derived from the source data. A dimension table is a descriptive attribute related to factual data and usually has fewer records than a fact table, but each record can have many attributes to describe the fact data.
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Dimensions can define different characteristics. Dimension tables are typically assigned a surrogate primary key, usually an integer data type in a single column, that maps to the combination of dimension attributes that make up the natural key.
Snowflake schemas, on the other hand, are extensions and extensions of star schemas with additional tables of secondary dimensions. In a star schema, each dimension is typically stored in a table; the snowflake design principle extends the dimension and creates tables for each level of the dimension hierarchy.
As well as advantages, data warehouses also have disadvantages, including not enforcing data integrity as in highly normalized databases. One-time inserts and updates can lead to data anomalies, and normalization patterns are designed to avoid these anomalies. In general, star schemas are loaded in a highly controlled manner via batch processing or near-real-time “flow-feeding” to compensate for the lack of protection provided by normalization.
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Star schemas are also less flexible than normalized data models in terms of analysis requirements. A normalized model allows any type of analytical query to be executed as long as it follows the business logic defined in the model. Star schemas tend to be more structured for specific views of the data, so they don’t really allow for more complex analytics Star schemas don’t support many-to-many relationships between business entities – at least not naturally. Often these relationships are simplified in a star schema to fit a simple dimensional model.
When a data warehouse is included as a component in a data-driven DSS, a DSS analyst or data modeler should develop a schema or structure for the database and identify analysis software and end-user presentation software to complete the DSS architecture and design. DSS components must be connected in an architecture that provides adequate performance and scalability. In some data-driven DSS designs, a second multidimensional database management system (MDBMS) is included and populated with a data warehouse built using a relational database management system (RDBMS). The MDBMS will provide data for online analytical processing (OLAP). Data warehouses are typically built using Microsoft’s RDBMS, and then query, reporting, and analysis software from vendors such as Tableau or Business Objects are used as part of the overall data-driven DSS design. What some vendors call “business intelligence software” provides analysis and user interface capabilities for a data-driven DSS built using data warehouse components.
AnalyticAnalyticsbanking Bibanking BI and DSSBig ResionsBi v ExcelbusinessBusiness AnalytingDerBent AnalylicsDasacdenata AnalylicsDatasDantata StoryTellingData CurcereDecesion StorytellingData CurcereDecesision StoryTeringData Actualing InformationSing Insight OLTP Partners Patchways International Report Report Insurant Analytics Self-service BI Software Star and Snowflake Schema Table Trend Visualization Digital Age. The early days included decision support systems (DDS) with data warehouse systems and online analytical processing (OLAP) systems. However, data warehouse systems and OLAP systems have significant differences in applicable applications, system architects, and, of course, system capabilities.
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Data warehouses provide data for decision making. Online Analytical Processing or OLAP facilitates data analysis and visualization. Data warehouse systems are designed to support OLAP. A DBMS that runs decision queries is a decision support system.
Data warehouses are used for detailed historical data, while OLAP servers are used for analytics. Despite their differences, both can work together to achieve business goals.
In terms of access, the data warehouse has read-only access and a list-oriented query and report. The OLAP server has read and write access for iterative and comparative analysis to explore access patterns. Also, the data warehouse may have slower responses to queries, and the OLAP server is faster with more consistent responses to queries.
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In terms of data storage, data warehouses have multi-subject data, single-subject data areas, and historical data storage. For an OLAP server, there are many cubes, and each cube is one subject area.
In the OLAP server, the data is dimensional and hierarchical. Data structures in a data warehouse are designed for list-oriented queries, while OLAP servers are designed for analysis. However, data warehouses can store terabytes of data, and OLAP servers typically handle many more gigabytes. In contrast, investments in hardware for data warehouses are not cheap, and OLAP servers vary in price.
Data warehouses are also slow to implement, taking months or years, while OLAP implementations can take days or weeks. Also, data warehouses have low adaptability, while OLAP servers are easily modified.
Business Intelligence And Decision Support Systems
A Decision Support System (DSS) analyzes business data and presents it so that users can make business decisions. A decision support data warehouse takes data from various sources and then uses advanced tools and techniques to support the development of decision support systems.
A decision support system starts from the information source and moves through the data warehouse to the OLAP server and then to the client for querying, reporting and data mining. OLAP
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