1. Business and IS Performance (IS 6010) MBS BIS 2010 / 2011 25 th November 2010 Fergal Carton (f.carton@ucc.ie) Accounting Finance and Information Systems

2. Last week Decisions mean comparing plan to actual DW architecture and ETL Real time information Response times and refresh rates Bill Ramsay –MIS talking the same language

3. This week Thoughts on Bill’s presentation Data quality and examples of transformation Response times and refresh rates

4. MIS speaking different languages Types of flows in a system –InformationBytes –Physical Boxes –Value Bucks Information not just about things, but information that drives processes –Mailing list vs CRM data on preferences –Warehouse system contains information about product Information about location of the product SAP ~ 2 million fields in the Materials Master file!

5. Business and IS performance System and process performance affects success of business Production planning manager with spreadsheet Real-timeness requirement varies with business –Volatile or contestable products versus agri-business Businesses hate inventory Manufacturing want to understand as close as possible what’s going on in real time –GSK bypass distributor to get to pharmacy example in Australia –Systems integration required particular skills

6. Product nomenclatures Tower of Babel A physical product lives in many different systems But all you can see is data eg. table from SAP Barcodes and EN codes on gift cards Frequency of renewing barcodes Does manufacturing pass gift code on to sales? –Interface between MES and ERP Careers for people capable of interpreting such data sets –Sales order in JDE being visible in DHL warehouse system

7. Apple case thoughts Think of businesses in different cycles –Order to cash, Make, Buy, Deliver Supply and demand sides of business driven by different things Customer service responsibility to put in sales orders –How can progress of order be visualised? Marketing rebates, applied at a different rhythm than sales More intermediaries in a system the more “noise” Noise is caused by the granularity of the information the closer I get to sales Forecasting gift card production relies on good sales data

8. Apple case thoughts Warehouse concerns (security, batch tracking) Retail (getting the cards in front of customers) HMV store looking for incentives to prioritise sales Stores might go around system to get inventory in a hurry

9. 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

10. 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.

11. 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.

12. 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

13. 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

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

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

16. Refresh Optimization

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