1. IS Consulting Process (IS 6005) Masters in Business Information Systems 2009 / 2010 Fergal Carton Business Information Systems

2. Last week UCC integration framework PCB exercise –Analysis method, importance for budgeting –Structure of requirements spec Dashboard design –Data cubes –Types of data –Data recording, A framework for transition of data to decisions Dashboards: Good Food Limited

3. This week Dashboard design –Data cubes –Types of data –Data recording, DW design A framework for transition of data to decisions ETL and data quality Real time information requirement Refresh rates

4. Example of data cubes

5. Types of data 1 Volume data (production) consumption data (raw material, packaging…) personnel data maintenance data time related measurements productivity data … All form the basis of the calculations used to monitor manufacturing activities

6. Type of data 2 Primary data: –taken straight from the floor (input and output) –e.g. production, consumption, labour, maintenance –ad-hoc reports - e.g. accidents, defects Secondary data or calculated data: –allocated costs –productivity –pay bonuses –variances High level data: –investigations of variances –soft information about staff morale etc...

7. Type of data: soft information Data collection - –Grapevine –factory tours (talking and observing) Data storage - –managers’ minds –special reports Data usage: –ad-hoc basis –decision making

8. Extraction Cleaning Transformation Loading Relational Database on a dedicated Server De normalised, data Static Reporting Scrutinising Multidimensional Data Cubes OLAP tools Data Warehouse Source Systems Discovering Data Mining ……. Data Staging Area Exploiting the DW data

9. Dashboards: from data to decisions

10. ETL Tools Extraction, Transformation, and Loading Specification based Eliminate custom coding Third party and DBMS based tools

11. Data extraction and transformation Getting data out of legacy applications Cleaning up the data Enriching it with new data Converting it to a form suitable for upload Staging areas

12. Data Quality Problems Multiple identifiers: –some data sources may use different primary keys for the same entity such as different customer numbers. Multiple names: –the same field may be represented using different field names. Different units: –measures and dimensions may have different units and granularities. Missing values: –data may not exist in some databases. To compensate for missing values, different default values may be used across data sources.

13. Data Quality Problems Orphaned transactions: –some transactions may be missing important parts such as an order without a customer. Multipurpose fields: –some databases may combine data into one field such as different components of an address. Conflicting data: –some data sources may have conflicting data such as different customer addresses. Different update times: –some data sources may perform updates at different intervals.

14. Example 1 – the supplier file Sup codeSup nameSup addressCityPhone 4 digits Sup codeSup nameSup address…PhoneCat 3 letters +1,2,3 depending 4 digitson total purchases last year OLD NEW New supplier code to include city where firm is based Assignation of category based on amounts purchased

15. Example 2: merging files Complete customer file based on Accounts and Sales and Shipping OLD (finance) CustIDnameaddresscityaccount numbercredit limitbalance OLD (sales) OLD (Shipping) CustID*nameaddresscitydiscount ratessales_to_daterep_name CustID**nameaddresscityPreferred haulier

16. Life cycle of the DW Operational Databases Warehouse Database First time load Refresh Refresh Refresh Purge or Archive

17. Real time information Up to date On-line Actual data Live feed Decisions made on what basis?

18. Real time requirement? Historical sales or accounting data, not real- time Sales as quarter end approaches Inventory levels for MRP Exchange rates, when is Visa rate calculated? Real-time processing: card transactions down

19. Real time requirement UCC?

20. Response times Response times are a function of : – response time, –Infrastructure elements, –Database sizing –Transaction processing –Interfaces –Reporting –Other processing demands –Peak times –…

21. Example Revenue reports from EMC Data warehouse Report can grow to >1million lines at quarter end Should not be run on ERP server Poorly designed?

22. Manager’s view Volume has been increasing at a huge pace compared to … like, you go talk to Jonathan, … my answer to it will be, get used to it, it’s not going to go away, I don’t care what you do, it’s not my problem, I want the reports, you deal with the volume of records, it’s not going to go away, you deal with it.

23. Refreshing databases Timing Criticality of information Volume of data Response time Real-time requirement Level of aggregation / granularity

24. Refresh Optimization

25. Determining the Refresh Frequency Maximize net refresh benefit Value of data timeliness Cost of refresh Satisfy data warehouse and source system constraints