Management Information Systems Chapter 6

Management Information Systems: Chapter 6

• Management Information Systems: Managing the Digital Firm, Seventeenth Edition, Global Edition, by Kenneth C. Laudon and Jane P. Laudon

• Chapter 6 covers Foundations of Business Intelligence: Databases and Information Management.

• Learning Objectives include: the challenges of managing data resources in a traditional file environment, the capabilities of Database Management Systems (DBMS), tools and technologies for accessing information from databases to improve business performance and decision-making, and the importance of data governance and data quality assurance.

• A traditional file environment suffers from issues such as data redundancy, inconsistency, program-data dependence, poor flexibility, poor security, and lack of data sharing and availability.

• Database Management Systems (DBMS) serve multiple applications by centralizing data and controlling redundancy.

• DBMS functions include: interfaces between applications and physical data files, separation of logical and physical views of data, solving the problems of traditional file environments, and controlling redundancy/inconsistency, uncoupling programs and data, enabling organizations to centrally manage data and data security.

• Data is organized in hierarchical structures with a database at the top, followed by files, records, fields, bytes, and bits.

• Relational DBMS is a powerful tool that represents data as tables, with rows (records) and columns (fields).

• Key fields uniquely identify records, primary keys are in tables and point to records, while foreign keys link tables.

• Capabilities of DBMS include data definition, data dictionaries, querying and reporting (using Structured Query Language (SQL), and various report generation tools, such as Microsoft Access.

• Designing databases requires conceptual design vs. physical design, normalization to reduce redundancy, and referential integrity.

• Big data consists of massive amounts of unstructured/semi-structured data from web traffic, social media, sensors, etc., and requires new technologies for management and analysis.

• Business intelligence infrastructure includes tools for obtaining information from multiple systems and big data, such as data warehouses, data marts, Hadoop, in-memory computing, and analytical platforms.

• Data warehouses store current and historical data, while data marts are subsets of data warehouses focused on single subjects or business lines.

• Hadoop enables big data processing across inexpensive computers, using Hadoop Distributed File System (HDFS), MapReduce, and NoSQL databases like Hbase.

• In-memory computing uses computer memory (RAM) for data storage, reducing processing delays. Analytical platforms use both relational and non-relational tools optimized for large datasets.

• Contemporary BI infrastructure consists of various data sources, including operational data, historical data, IoT data, and external data. Data is transformed and loaded into a Data Warehouse, providing data for Data Marts, Casual Users, and Power Users. Data mining tools can be used to get important insights such as customer buying behavior, predicting future events, etc.

• Online Analytical Processing (OLAP) enables rapid, online answers needed for ad-hoc queries, typically using multidimensional analysis. Data mining finds patterns and relationships in data, with types such as associations, sequencing, classification, clustering, and forecasting. Text mining extracts key elements from large text sets, while web mining discovers patterns from web content, structure, and usage.

• Databases and the web allow organizations to make internal databases accessible to customers and partners, with advantages including ease of use, minimal changes to database systems and low cost.