BUS1MIS Management Information Systems Semester 1, 2012 Week 6 Lecture 1

Data, Databases, Data Warehouses, Information and Business Intelligence Learning objectives Ref. Chapter 6 (Text) Describe the level, format and granularity of organisational information. List, describe and provide an example of each of the five characteristics of high quality information. Define the relationship between a database and a database management system Describe the advantages an organisation can gain by using a database Describe the roles and purposes of data warehouses and data marts in an organisation Compare the multidimensional nature of data warehouses (and data marts) with the two- dimensional nature of databases Identify the importance of ensuring the cleanliness of information throughout an organisation Explain the relationship between business intelligence and a data warehouse

Data Warehouses and Business Intelligence The use of data warehouses to generate business intelligence has been shown to create competitive advantages. DatabaseData WarehouseBusiness Intelligence

Data Warehouses and Business Intelligence The use of data warehouses to generate business intelligence has been shown to create competitive advantages. For example: Samsung Electronics (see p. 256 of the Textbook)Samsung Electronics

Business Intelligence Examples: TV Broadcasting – predicting what programs and advertisements are best to air during prime time. Retail – predicting correct inventory levels Law enforcement – tracking crime patterns See a Business Intelligence Dashboard example

Organisational data comes in different formats and granularities, and at different levels Understanding Organisational Data (Information)

Granularity Understanding Organisational Data (Information)

Transactional Data – full detail Understanding Organisational Data (Information)

Analytical Data – summary Understanding Organisational Data (Information)

Management of Organisational Data Appropriate management of organisational data means that for a particular context the data is: Of the appropriate granularity (transactional or analytical), level and format Timely Of High Quality The effective management, access and analysis of organisational data leads to high quality information (business intelligence) and high quality decision making.

Management of Organisational Data Timeliness Timeliness is an aspect of information that depends on the situation: Real-time information – immediate, up-to-date information. For example, emergency centers, banks, Web Jet.Web Jet Batch-updated information - sometimes information that is several days or weeks old may still be relevant in decision making --- it all depends on the situation. For example, Music AustraliaMusic Australia

High Quality Characteristics of high-quality information include: Accuracy (all values correct?) Completeness (is anything missing?) Consistency (is the aggregate information consistent with the individual items?) Uniqueness (is each transaction only recorded once? ) Timeliness (real time? Batch update?) [See table 6.1 p. 258 Text] Management of Organisational Data

An example of low quality information [Fig 6.3 p. 259 Text] Management of Organisational Data

For efficient access organisational data should be stored in a database. The database needs to be designed so there is no redundant data (see Lecture 2 Week 6) A quality database management system (DBMS) should be used to manage and query a database Efficient Access to Organisational Data

The business advantages of using a database and DBMS include: Increased flexibility Increased scalability and performance Reduced information redundancy Increased information integrity (quality) Increased information security Flexibility: a user can access data in a way that suits his/her needs Scalability: how well a system can adapt to increased demands Performance: how quickly the system can perform transactions or processes Redundancy: the duplication of information Integrity: the quality of the information Security: levels of access to data through passwords and access controls Efficient Access to Organisational Data

Analysis of Organisational Data It is difficult for an organisation to efficiently and effectively analyse its data if the data is transactional and stored in multiple databases. It is better to aggregate the data (count, total, average, etc.) and store it in a data warehouse where it can be used for decision-making purposes. ETL – extract, transform and load Data Mart – a subset of a data warehouse focused on the needs of a single business unit, eg. finance.

Databases contain information in a series of two-dimensional tables (rows and columns). In a data warehouse and data mart, information is multi-dimensional, in cubes, rather than tables. Cube a represents all store information, all product information and all promotional information Analysis of Organisational Data

Cube b represents promotion II information for all stores and all product s Analysis of Organisational Data

Cube c represents promotion III information for store 2 and product B Analysis of Organisational Data

An organisation must maintain high-quality data in the data warehouse Information cleansing or scrubbing – a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information e.g. standardising customer name Analysis of Organisational Data Data warehouse

Data mining – the process of analysing data to extract information not offered by the raw data alone Data-mining tools help users uncover business intelligence (BI), eg. Cluster analysis – a supermarket chain analysed the buying behaviours of its large number of loyalty card holders. A number of clusters were identified statistically and targeted advertising campaigns were developed. Analysis of Organisational Data