SEUGI 96 Data Warehousing

Transcription

1 SEUGI 96 Data Warehousing Sean Kelly,.

2 About the presentation The Data Warehouse Network was established in 1992 to serve the needs of this market in Europe. Based in Ireland it provides seminars, consultancy and publications to users and vendors. The DRUID Survey is the annual survey of European data warehouse systems and is the source of all the statistics provided in this presentation.

3 The business promise of DW Superior business intelligence Enhanced customer service Enhanced asset management Enhanced risk management Business process innovation Mass customisation of products

4 Business intelligence systems Intuitive Fast Team-oriented Flexible What if? Integrated From macro to micro detail

5 Data Warehouse - A Definition A single integrated store of corporate data that is available to users for analysis and decision-making A data warehouse is not an information technology system: it is an information technology architecture Data warehouse is a framework, not an end in itself

6 DW Foundational Characteristics The Data Separate Integrated Cleansed Non-volatile Time-stamped Available Accessible The Architecture Logically layered Extensible Flexible Robust Scaleable Located within overall I.T. framework

7 The early implementors Retailing customer loyalty Utilities customised pricing Financial Services risk management product innovation

8 Marketing Applications Market Planning Risk Management Customer Care/Relationship Management Revenue Growth Cost Control Product Innovation Sales Automation

9 Market Segmentation Geographic Demographic Psychographic Behavioural Temporal Commercial Industrial Credit-based Market Segmentation leads to Mass Customisation

10 No alternatives to DW Resource Contention Because of the risk of runaway queries it is not possible to allow unrestricted access to operational systems for ad hoc queries Data Model Because the data in the enterprise is fragmented across many applications it cannot be exploited for strategic advantage The physical model of the data for a queryintensive database is different

11 Size of Market in Europe (DRUID) Large Systems Average Value $US Total Value $US Small Systems Total $US Total Value $US

12 Data Warehouse Architecture Legacy Extraction Database Middleware Application

13 Data Warehouse Architecture Legacy Extraction Database Middleware Application SAS TOOLS SAS TOOLS or RDBMS SAS TOOLS

14 SAS System Connectivity - SAS/Access & SAS Share Architecture - SAS/Connect Extraction - SAS/Access & SAS/Connect Data management - SAS datasets or RDBMS Administration - SAS metabase Exploitation - SAS/EIS

15 The SAS Solution - Strengths 1. Knowledge of decision-support environment 2. Comprehensive architecture support 3. Flexible deployment of component tools 4. High performance for query-intensive processing 5. Potential to develop vertical industry sector applications

16 DW as % of total IT costs Data warehousing tends to represent an ongoing commitment for a period of three years to establish the infrastructure and build applications 20% 73% 7% Less than 10% Between 10% and 20% More than 20%

17 Spread of DW investment The bulk of early human resources are directed at data cleansing and data modelling. Later the focus shifts to application development. Human Resources Hardware Software 24% 32% 44%

18 Spread of applications Overwhelmingly, the data warehouse is used as an aggressive weapon for sales, marketing, pricing and strategic planning in competitive markets. Marketing Product Others Sales Finance

19 Europe's top 500 companies ADVANCED 20% of Europe's top 500 companies have a mature data warehouse environment. MAINSTREAM 60% of Europe s top 500 companies have a data warehouse prototype or pilot project. RETARDED 20% of Europe s top 500 companies have no plans for a data warehouse in 1996

20 Essential DW components Hardware platform - Lead Contractor Data architecture Data hygiene software Data capture software Database software Application software Data mart database software Query tools

21 Data Pollution - Root Causes Legacy system deficiencies Data structure deficiencies Data entry errors Data policy errors Free form fields Data modelling errors Business rule anomalies 45% 25% 30% Cleansing Design Build

22 Data Accuracy Data Cleansing - The Benefits Data Quality & Detail Customers 10, ,000 1,000,000 99% 9,900 99, ,000 90% 9,000 90, ,000 80% 8,000 80, ,000 70% 7,000 70, ,000 Micro-segmentation requires accurate detailed data

23 Data Pre-processing DETAILED Address Date of Birth Car Date of Purchase AGGREGATE Region Age Car Class Purchase Month Revenue Amount Revenue Group Optimised queries require detailed and summary data

24 The 4 DW dilemmas Top-down design or bottom-up?? Summary data or detailed data?? Enterprise data warehouse or subjectarea warehouse?? Data driven or application driven??

25 DW Project Principles Evolutionary approach Immediate trickle benefits Business driven Scaleable solution Architectural standards Data standards End-user empowerment

26 Top 8 DW Drivers Rank Enabler % 1 User demand 71 2 Changing business markets 41 3 Available technology 39 4 Business drivers 33 5 Data as product 28 6 IT management 27 7 Organisational change 27 8 DSS applications 25

27 Top 8 DW Inhibitors Rank Inhibitor % 1 Intangibile benefits 53 2 Lack of corporate perspective 40 3 Lack of user understanding 37 4 High cost of entry 36 5 Inadequate sponsorship 33 6 Short-term demands 31 7 Poor knowledge of legacy data 29 8 Lack of suitable IT tools 28

28 Organisational Readiness Product Volatility High Low Low Important Optional Urgent & Important Urgent Market Turbulence High

29 The Intelligent Enterprise Informated/ Proactive The Cybernetic Organisation Informated/ Reactive The Information-based Organisation The Primitive Organisation Automated, Not Informated

30 How soon is the DW needed? For too many organisations, the data warehouse project is justified only when a competitor demonstrates the value with new product, pricing and service innovations. When this happens, it may already be too late It is never too soon to get to know your customers

31 Thank You Telephone Fax