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Understanding Databases: Challenges, Solutions, and Management Strategies

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This chapter explores the complexities of managing data in traditional systems and how a Database Management System (DBMS) addresses these challenges. It discusses key features of relational DBMS, database design principles, and tools for effective data access and performance improvement. We highlight the importance of data governance, quality assurance, and information policies to ensure data integrity and availability. You will also learn about data marts, business intelligence techniques, and the role of data administration in enhancing decision-making processes.

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Understanding Databases: Challenges, Solutions, and Management Strategies

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  1. BUS 290: Chapter 6 Databases and Information Management

  2. Objectives • Problems managing data in “traditional” environments and how a DBMS solves them • Major capabilities of a DBMS and why relationship DBMS are so powerful • Some important principles of database design • Principal tools & techniques for accessing database information to improve performance & decision making • Why are information policies, data administration & data quality assurance essential?

  3. Terms • Bit • Byte • Field • Record • Entity • Attribute • File • Database

  4. Traditionally … • Each department had independent data and way of handling/processing it This led to .. • Redundant data • Inconsistent data • Inconsistent field names or values • Program data dependence • Field sizes, formats

  5. And more Issues • Lack of flexibility • Not designed for ad hoc queries • Poor Security • Lack of Data Sharing • Lack of Data Availability

  6. Relational Databases Access, Oracle database, etc • 2 dimensional tables (AKA files) • Each table has information on an ENTITY • Primary Key links tables • 3 basic operations • Select • Join – combines data from several tables • Project – creates a new table with the Joined data

  7. DBMS Tools Tools to organize, manage & access data • Data definition • creates tables and defines fields • Data dictionary • from size, type, format on Access to Usage ownership, security on large databases • Queries and reports • Structured Query Language (SQL)

  8. Database Design Need to understand: • Data relationships • Type of data (and potentially size) • How it will be used • How it will be manipulated New databases must be NORMALIZED • Sub tables created • Every table will include one common field – the key

  9. Data Marts Instead of one centralized database … • Smaller decentralized data marts • Highly focused data for market segments • Easier, faster & cheaper access

  10. Business Intelligence • Online Analytical Processing • Just like the data analysis in Chapter 1 • Data Mining • Discovery driven • Association … buy chips … buy coke • Sequences … buy a house - Buy new fridge 2 weeks later • Classification … characteristics – segmentation • Clustering • forecasting • Predictive Analysis • Text mining & Web mining (e.g. what we google)

  11. Managing Data • Data Administration (CIO’s office) • Develops policy, plans for data, oversees the database design & data dictionary • Data Governance • PnP for availability, integrity, security & compliance with government regulations • Information Policy • Rules: how to share, disseminate, acquire, standardize, classify, modify and inventory information • Data quality Audits

  12. Chapter 6 Homework • Management decision Problems • Discussion question 3

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