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Chapter 6 Foundations of business intelligence: databases and information management

Chapter 6 Foundations of business intelligence: databases and information management. How does a ‘database’ solve the problems of traditional filing systems? What capabilities distinguish database management systems? What principles underpin DBMS design?

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Chapter 6 Foundations of business intelligence: databases and information management

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  1. Chapter 6 Foundations of business intelligence: databases and information management • How does a ‘database’ solve the problems of traditional filing systems? • What capabilities distinguish database management systems? • What principles underpin DBMS design? • What tools and technologies are used to access databases? • Why are information policy, data administration and data quality assurance essential for managing data resources?

  2. Traditional digital filing systems • “Subordinate” or “hierarchical” structure: field, record, file, database BUT: • Data redundancy? • Data inconsistency? • Program-data dependence? • Inflexibility? • Poor security? • Lack of data share and availability?

  3. And so, to “database approach” to data management! • Agglomeration of data (ALL data stored in one place) BUT how should this data “mess” be organised? • Relational databases vs. Object-oriented databases Relational data bases use “key” identifiers, that allow different groups of data to be brought together Object oriented data bases allow relationships to be formed between images and sound bytes etc, AS WELL AS text

  4. The 3 capabilities of a DBMS 1. Data definition • To ensure consistent and standardised use amongst different classes of users 2. Data dictionary • Classification of data to ensure that persware (users) can interpret all elements and not “invent” new ones 3. Data manipulation language • A “programming language” to manipulate (and access) data (SQL – Structured Query Language)

  5. 1 of 2 Finally, two more foundation concepts: • Database design Important to plan the database to avoid downstream headaches Data model: • Include all entities and their relationship • Organise data to remove redundancy • Maximise data accuracy • Make data accessible

  6. 2 of 2 • Distributed databases Distributed databases “copy” the database in multiple locations, so that data access can be improved. BUT! How reliable is the data? Or in other words, what risks are there in Computicket or SAA allowing seat booking in multiple locations off multiple databases? (Telecoms capability has removed this as a contemporary issue – but, “old fashioned” technology prohibited easy access from a common location)

  7. How does having a database improve business performance and decision making? (I don’t think the book gets it right here: it is important to emphasise that accuracy & accessibility of data are the features that improve decision making and hence performance) So, while the text book talks about ‘data warehouses and datamarts, please review the supplementary notes on ‘types’ and ‘levels’ of decisions, and be able to relate the concepts of data accuracy and data accessibility and “data innovation” (seeking out data relationships so as to improve customer intimacy: focus on product categories) to decisions that need to be made in business contexts.

  8. Related concepts for “decision making” Business intelligence • Capability to amass information • Develop intimate understanding of customer behaviour, competitors and internal operations • “fine tuned” decision making Data mining • Associations (do I treat myself to new clothes around my birthday?) • Sequences (is there a pattern to my purchases?) • Classification (am I similar to other groups/segments of customer?) • Clustering (similar to classification) • Forecasting (can my historical purchasing be used to forecast my future consumption?)

  9. Managing data resources Information policy • Data administration • Data governance Data quality • Data quality audits • Data cleansing Data security (I recommend you add this one!) • Liability in respect of breaching privacy boundaries • Managing risk in terms of data integrity (hacking, theft)

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