How Data Warehouse Automation complies with the Speed of Business. Freek Kamst

Transcription

1 How Data Warehouse Automation complies with the Speed of Business. Freek Kamst Information Management 2014, Brussels April 29th, 2014

2 What s it all about? Speed of Business Increasing Demand for Decision Supported Information Data Warehouse Automation (Benefits & Pitfalls) Customer Case Q&(A) End 2

3 Speed of Business The World is changing very rapidly More Self Service, More Transactions Business must adapt to the Market Faster Decisions make a Difference Source: Dr. Richard Hackathorn, Bolder Technology Inc. Faster Decisions increase Business Value

4 Demand for Agility, Analytics and Adaptivity Requirements Analysis Data Profiling Design logical model Physical Design ETL Deploy RFC Barriers of Change

5 Value Realization Increasing Demand for Analytics Applications Automation Business Process App Integration Descriptive Reporting Diagnostic Analysis Predictive Analytics What Should Happen? Prescriptive Analytics What Will Happen! Most BI projects Why Did It Happen? Data What Happened? Transactions Competitive Business Performance Curve

6 Data Warehouse Automation Connect to Source Applications Connect Securely Extract Data Full Incremental Denormalize Data Produce aggregatable data Create/flatten hierarchies for rollups Consolidate sources Cleanse data Create Dimensional Model Identify things that are to be aggregated Identify business entities that Manage changes and history Snapshots Slowly changing attributes Create Business Model Semantic layer Allows business users to create queries without knowing SQL or underlying physical structure Distribute Publish Heavily Predigested reports AdHoc/Visualization Create interactive analysis (dashboards) Embed in other applications Requirements Analysis Data Profiling Design logical model Create Dimensional Model Physical Design Extract Transform Load Deploy Only partial support by Visualization, Dashboard-only, and other SaaS Tools Conventional Analytical ETL tools, but without Data Warehouse Automation BI/Data Discovery Tools 6

7 Data Warehouse Automation BIRST AUTOMATES THE DATA WAREHOUSE CREATION IT/business design logical model IT/business implement logical Birst Generates the ETL Birst DWA builds the DB Business builds initial model Schema reports Beschikbaar in de Cloud of lokaal. Measured in 4-6 Week Sprints BUSINESS DRIVES ITERATIONS IT/business modifies logical model Business validates new reports Measured In Days

8 Functionality & Value Data Warehouse Automation Requirements Third Phase Deployed Second Phase Deployed Agile BI Business Value Second Phase Deployed First Phase Goes Live First Phase Goes Live Discovery Tools Legacy BI Value Q1 Q2 Q3 Q4 Q5 Q6 Traditional Deployment 8

9 Benefits & Pitfalls Pitfalls Requirements Analysis can t be automated This also applies for Data Quality & setting up Database Connections Don t try to create the perfect data warehouse (think out of the box) Don t let a Data Discovery tool deceive you Benefits TCO (Total Cost of Ownership) TTV (Time to Value) or Time to Report Reduction in human errors Reduced Risk in data warehouse development The ability to prototype the data warehouse Automation Technology makes the data warehouse agile A huge reduction in ETL Development Automatic generation of various Dimensions & Aggregates

10 Customer Case Executive Sales Analytics Nailed quota: 6 consecutive quarters Sales Mgmt: 10% less admin time TTV: 83 days Earlier at D&B, we tried to build exec level dashboards and we never achieved this success. - Ted Mastalski, Leader-Global Solutions Reporting Business need Outlook: forecast, quota, actual, predicated performance on 800+ sales reps Sales and product combined reporting and analytics Team performance on constantly changing territories Technology Snapshots, conforming dimensions, automated warehousing Warehouse, multi-data source joins, Pixel perfect reporting Slowly changing dimensions DATA SOURCES

11 Finally our Conclusions The demand for information increases is infinite Business has to adapt faster and faster Traditional BI has limitations Business Analytics requires Data Warehouse Automation

12 Thank you! Freek Kamst