A Datawarehousing/BI Session ANTHONY D NORIEGA anoriega@adnresearch.com ADN R & D

Transcription

1 Perspectives in Oracle Forecasting Analytics A Datawarehousing/BI Session ANTHONY D NORIEGA anoriega@adnresearch.com ADN R & D 1

2 Speaker Qualifications Oracle Consultant, ADN R & D Speaker at Oracle OpenWorld, Collaborate, IOUG LIVE, Quest Conference and NYOUG meetings. 25 years of IT experience 21 years of Oracle experience, 17 as a DBA (v6 thru 11g) Forecasting experience in finance, sales, and marketing. BS Systems Engineering, Universidad del Norte, MS Computer Science, NJIT, 1992 PhD CIS candidate, NJIT, 1997 MBA MIS, Montclair State University, 2006 College Math Professor and former HS Math Teacher Principal. Listed among top 10 IOUG/Collaborate papers ever. 2

3 Objectives Emphasize Oracle's SQL Modeling and Forecasting capabilities. Discuss areas of current and future applications, and relevant studies. Introduce and expand on a frame of reference to approach BI modeling and forecasting. Introduce real-time forecasting capabilities available through Coherence and rule-based custom development. Discuss Oracle forecasting analytics and its relevant business intelligence foundation. 3

4 Business Framework Traditional Methodologies Time Series Analysis Moving Average Moving Average with Seasonal Adjustments ARMA (Autoregressive Moving Average) Box, Jenkins (1976) ARIMA (Autoregressive Integrated Moving Average). 4

5 Business Framework Traditional Methodologies Statistical Analysis Linear Regression Multiple Regression Factorial Design Latin Quarters Exponential Smoothing Simple Exponential Smoothing Double Exponential Smoothing Exponential Smoothing with Trend Physics Statistics. Anthony MBA, MSCS, BSSE, OCP OCP ADNResearch AnthonyDDNoriega, Noriega, MBA, MSCS, BSSE, ADNResearch Perspectives in OracleinForecasting Analytics Analytics 5 Perspectives Oracle Forecasting

6 Business Framework Traditional Methodologies Statistical Analysis Linear Regression Multiple Regression Factorial Design Latin Quarters Exponential Smoothing Simple Exponential Smoothing Double Exponential Smoothing. 6

7 Business Framework Traditional Methodologies Operating Research Markov Chains (Stochastic Processes) Bayesian Models (Probabilistic Models) Game Theory Approaches Mathematical Models Linear Algebra Structured Matrices and Polynomials Models Martingale-based Physics Statistics. 7

8 BI Software Market Oracle in the Leader s Quadrant 8

9 Oracle s BI Approach 9

10 What to Forecast 10

11 What to Forecast 11

12 What to Model Finance Economics NASDAQ Dow 12

13 Areas of Application BI Forecasting for: Call Centers and Business Process Outsourcing Customer Credit Rating Direct and Strategic Marketing e-business and Online Sales Demographic Systems using Segmented Spatial Data Analysis Government Institutions, e.g., Unemployment and US Census Agencies Manufacturing New Products Launch Retail Chains Retail Finance Companies and Banks Sales Demands and Inventory Control Travel and Tourism 13

14 Technical Framework 14

15 Technical Framework Oracle Resources and Capabilities Oracle Exadata/Exalogic Oracle BI Foundations Suite Oracle Essbase Server Oracle Data Miner Oracle Hyperion Oracle Discoverer Oracle BI Beans Oracle Exalytics (New!) 15

16 Technical Framework Analytical Workspace Tables Dimensions Levels Hierarchies Level-Based Value-Based Measures 16

17 Technical Framework Analytical Workspace Logical Cubes Rules Attributes 17

18 Analytical Workspace A OLAP Registry Analytical Workspace Manager 18

19 Analytical SQL Syntax 19

20 OLAP DML (Finance) 20

21 OLAP DML (Finance) 21

22 OLAP DML (Statistics) 22

23 Analytical Operators Simple Forecast and Regressions REGRESS.report SMOOTH Forecast and Regression Statements using Context 23

24 SQL: Group-by Aggregation Aggregation using Grouping Sets Aggregation using Operators Aggregation with the Group Id and Grouping Id Differentiators. G 24

25 SQL: Aggregation Operators Rollup Cube 25

26 SQL: Dimensions (MOLAP) 2 26

27 SQL MODEL Clause M 27

28 MODEL-Related Clauses Partition by Dimension by Measures M Rules Rules Options Update, Upsert, Upsert all 28

29 Analytical Functions (SQL) 29

30 Model Conditions in Rules In addition to matching rules these conditions can filter the rule application: IS ANY R IS PRESENT 30

31 Unified Perspective SQL Functions and API OLAP DML API Functions U Statements J2EE XML SOA 31

32 Current Benefits Analytical Workspace Support OLAP Registry Oracle s OLAP DML J2EE/Java Integration E-Business Support Hot Plug-in Enhanced OLAP DBA, DBA Capabilities. Easy Reporting and Visualization Dashboard in Cloud 32

33 Attainable Target Benefits Robust enterprise-wide credibility on corporate expectations and planning Congruent focus on credible, logical, and proven forecasting-based planning process Consistent control and alignment of resources based on logical and proven planning for the future Outstanding competitive advantage in the market through better strategic, tactical, and operational decisions for future Solid market position with richest user's experience and highest level of customer satisfaction Increased Return on Investment (ROI). 33

34 SQL Examples 34

35 SQL Examples 35

36 SQL Examples 36

37 SQL Examples 37

38 SQL Examples 38

39 SQL Examples 39

40 Case Studies Health (HIV) Demographics (Census) Financial Markets ( Nasdaq ) Econometrics (Income/Gender) Marketing Research (4Ps) Communications Retail 40

41 Future Forecasting Models Who Where When Extent How How much? 41

42 Concluding Remarks Oracle SQL analytical framework has attained an outstanding level of maturity deserving a leadership positioning. Oracle SQL and OLAP API can integrate with custom J2EE, XML, and.net applications, and for a key reporting Data Warehousing capability with Text and related tools. Oracle Exadata/Exalogic/Exalytics lead BI resources and capabilities for cloud analytics. 42

43 Concluding Remarks SQL Model capabilities have achieved a transactional and reporting level which allows the visualization of modeling and forecasting in query-like mode, with greater flexibility than conventional statistical analysis and modeling tools. Oracle10g support through both OLAP DML and API and SQL is essentially solid for conventional forecasting methods such as Time Series (Moving Average and ARIMA) and Exponential Smoothing, among others. 43

44 Concluding Remarks Integration MODEL Forecasting 44

45 Q/A? 45

46 Thank you! Please complete your evaluation form! Speaker: Anthony Noriega Title: Visit adnresearch.com 46