1. Business Intelligence in Detail What is a Data Warehouse?

2. Data Sources Data Warehouse Analysis Results Data visualisation Analytical tools OLAP Data Mining Overview of Business Intelligence Data visualisation

3. Example BI Questions (Microsoft) How does our profitability to date compare with the same time period during the past five years? How much money did customers over the age of 35 spend last year, and how has that behavior changed over time? How many products were sold in two specific country/regions this month as opposed to the same month last year? For each customer age group, what is the breakdown of profitability (both margin percentage and total) by product category? Find top and bottom salespeople, distributors, vendors, clients, partners, or customers.

4. To answer these...Data is 1.Gathered from relevant sources 2.Filtered,standardised and stored 3.Analysed and arranged into meaningful patterns using different tools. 4.Business intelligence is the knowledge gained from that data analysis.

5. We need to Understand Data issues – data quality Where data comes from How data is stored: data warehouses How data is analysed Example Business contexts Limitations of the computer Our own blind spots (if this is possible)!

6. Where does the data come from? Data can be collected manually or automatically. –Transaction data –Time studies, questionnaire, observation notes –Physical sensors e.g. temperature of a rooms in a house –Sensors, scanners, bar codes It may be stored in different systems e.g. ERP database, operational databases etc. and in different formats

7. We need to decide what data is important... Depends what our goals are, the functional area(e.g. Sales, HR, marketing..) and what processes we are looking at.. e.g. Balanced scorecard uses Critical success factors Key performance indicators These are derived from e.g. transaction data collected

8. Sales and marketing products customers demographics promotions sales force order type Human resources Employee data e.g. Pay Operations management assembly speed warehouse stock manufacturer and supplier cost shift productivity Finance currency standards account information industry trends Example data in Functional Areas

9. Data Quality is also important Contextual – relevance, value, timeliness completeness, amount Intrinsic – accuracy, objectivity, believability, reputation Accessibility DQ – ease of access,security Representation DQ – interpretability, ease of understanding, concise, consistent representation.

10. 10Data 3 What is a Data Warehouse? A data repository that makes operational and other data accessible in a form that is readily acceptable for decision support and other user applications. Note: A data warehouse is not another word for a database. The specific purpose of a data warehouse is to support decisions not operations.

11. .... From Turban, Aronson and Liang

12. Data warehouse vs operational database Operational database – optimised for speed of update Data is normalised. Each data item is only held once. very fast insert/update performance Older data periodically purged to improve performance. Data warehouse - optimized for speed of data retrieval. –Data may be stored using a dimension-based model e.g. like OLAP –data warehouse data are often stored multiple times. Historic data are held to enable comparison

13. E.g. Microsoft: How can data from different sources be joined together (consolidated and integrated) securely? SQL Server provides a comprehensive and scalable data warehouse platform organizations build large-scale enterprise data warehouses that can consolidate data from multiple disparate systems into a single, secure, manageable solution.

14. 14Data 3 Parts of a Data Warehouse System Data Warehouse itself Data acquisition (back end) software Client (front-end) software

15. 15Data 3 Data Warehouse Itself A large physical database which contains the data in the data warehouse. A logical data warehouse which contains all the meta data, business rules and processing logic used to organise and preprocess the data.

16. 16Data 3 Data acquisition (back end) software Extracts data from legacy systems and external sources, consolidates and summarizes the data and loads it into the data warehouse. The size of the update window – is how long it takes for the operational data to be transformed and loaded into the warehouse.

17. 17Data 3 Data Extraction Importing files Summarising data standardising data Filtering and condensing data.

18. 18Data 3 What do we mean by transforming data? Comparing data from different systems to improve data quality –Data may be missing from one data base but present in another. Standardising data and codes E.g. Gender may be classified as Male/Female, M/F, 0/1 Integrating data from different systems e.g. if one system keeps orders and another stores customers, these data elements need to be linked. Performing other system housekeeping functions. –Change files to reduce data load times. –Finding keys for data.

19. 19Data 3 Meta Data A data warehouse must allows for the storage of metadata –information about the content of the warehouse, –Sources of the data – guides for moving data, –summarisation rules, – business and technical terms, –rules for data extraction.

20. 20Data 3 Characteristics of Data in the Warehouse Subject oriented –data are organised by detailed subject, containing only information relevant for decision support. Integrated –all data is standardised and consistent Time-variant –data kept for 5-10 years & used for trends, forecasting and comparison Non-volatile –once entered into the data warehouse data can’t be changed. –Obsolete data are discarded. Summarised –into different levels of detail Not normalised

21. 21Data 3 Front End Tools Business intelligence (BI) tools and analytic applications may be used to access the warehouse/marts to support querying, reporting, and analysis of the data. –General-purpose report writers and managed query tools –OLAP –Data mining tools –Performance management systems –Data visualization tools

22. 22Data 3 Related Technologies : Data Mart A subset of a data warehouse, typically consisting of a single subject area. Lower cost, scaled down version of a data warehouse designed for a strategic business unit or a department. Advantages: –lower cost –shorter lead time for implementation –local control –more rapid response –easier to understand and navigate –allows a business unit to build its own systems without relying on a centralised IS department.

23. What is OLAP? OLAP enables you to look at and access data in different ways (3-d data cubes), drill down, view summarised data, make calculations on the fly etc. http://www.census.gov

24. How can data be analysed? Microsoft Online Analytical Processing (OLAP) makes it quick and easy to perform ad-hoc queries and analysis of large amounts of complex data across all aspects of your business.

25. Microsoft OLAP is used to report on... sales marketing management issues business process management budgeting and forecasting, financial issues etc..

26. 26Data 3 Why use a Data Warehouse? –Data is stored in different systems. –Management use information to make decisions. –The customer base is large and diverse. –The data in the different systems is represented differently. –Data is stored in highly technical, difficult to decipher formats.