Chapter 4: Databases and Business Intelligence

Chapter 4: Databases and Business Intelligence

1. A database is a structured collection of related information stored in an organised way so that specific items can be retrieved quickly.

2. Types of databases and database management systems

   • File processing databases: data stored in separate files; difficult to integrate and analyse across the organisation.
   • Database management systems (DBMS): software systems that manage data storage, retrieval, and maintenance.
   • Relational DBMS (RDBMS): the most widely used type; data is stored in tables and linked using keys.
   • Object-oriented databases: store data as objects; useful for complex application structures.
   • Network and hierarchical databases: older approaches; less common today.

3. Using database software in practice

   • Create and edit tables/record structures.
   • Enter, modify, delete and sort records.
   • Generate reports and outputs in different formats.

4. Data warehouses and data warehousing

   • Data warehouses : large database systems containing current and historical data that can be analysed to produce information to support organisational decision making
     • Subject-oriented
     • Integrated
     • Time-variant
     • Non-volatile
     • Data marts are smaller, department-focused subsets of a data warehouse (e.g., marketing, finance).
   • Data warehousing The process of creating and maintaining a data warehouse.
   • Data warehouse architectures
     • Data mart centric. Data are linked to users through independent data marts. This provides a relatively easy implementation in technical and organisational terms
     • Virtual, distributed, federated. This consists of linking users directly to data sources through the use of middleware
     • Hub-and-spoke data warehouse. This links users to dependent data marts, which are then linked to an enterprise data warehouse which in turn is linked to organisational data sources.
     • Enterprise data warehouse. This links users directly to a data warehouse which is linked in turn to data sources. This provides a single and thus consistent view of the data across the enterprise. I
   • Real-time data warehousing (RDW): The capability to load and process data as events happen into the data warehouse.

5. Data mining- In its broadest sense is a process that uses statistical, mathematical, artificial intelligence and other techniques to extract useful information from large databases.

   • Identifying associations: establishing relationships about items that occur at a particular point in time
   • Identifying sequences: This involves showing the sequence in which actions occur
   • Classification: analysing historical data into patterns to predict future behaviour
   • Clustering: finding groups of facts that were previously unknown
   • Modelling: This involves using forecasting and regression analysis to predict sales

6. Visualisation tools

   • Spreadsheets: In conjunction with their statistical and forecasting capabilities spreadsheets are particularly useful are providing graphical displays of trends
   • Dashboards: A graphical interface to assist people to understand performance measurement information.
   • Scorecards:A graphical interface to assist people to understand a summary of performance over time. Associated with the balanced scorecard strategy tool.
   • Business activity monitoring (BAM): Software that is designed to monitor, capture and analyse business performance data in real time and present them visually in order that rapid and effective decisions can be taken.

7. Business intelligence A general term that describes a range of techniques and technologies used to gather, store and analyse information from a variety of sources in order to improve decision making within the organisation.

   • Business Intelligence (BI): purpose and value
     • Supporting strategic and managerial decisions.
     • Combining data from different sources into a usable form.
     • Identifying patterns, trends, and performance issues.
   • Business intelligence system overview

8. Text mining is the application of data mining to text files. Text mining aims to find previously hidden patterns in text within and between documents

9. Web mining: Data mining applications that are being developed to analyse information from the web.

   • Clickstream analysis: Analysis of user behaviour gathered as they visit web pages.

10. Business analytics (BA) is a term that is used to describe various approaches to data driven analysis including reporting tools, covers the following areas:

    • Statistics – use statistical data analysis to drive fact-based decisions.
    • Data and text mining – build descriptive and predictive models and deploy results throughout the enterprise.
    • Data visualisation – allows users to interact with graphs to clarify results and take action
    • Content categorisation – categorises content, which is then used to create metadata and trigger business processes.
    • Forecasting and econometrics – analyse and predict outcomes based on historical patterns and apply statistical methods to economic data, problems and trends.
    • Operations research – applies techniques such as optimisation, scheduling and simulation to achieve the best result.
    • Model management and deployment – streamline the process of creating, managing and deploying analytical models.
    • Quality improvement – identifies, monitors and measures quality processes over time.

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