Introduction Data Vault Historical development Business Intelligence 1950

Introduction Data Vault

Historical development Business Intelligence • • • 1950 1960 1970 • 1980 • • • 1990 2000 2010 Turing : First computers Codd : 3 NF Management Information Systems (MIS) Executive Information Systems (EIS) Kimball : Dimensional Modeling Kimball Inmon : Corporate Information Factory Enterprise Datawarehousing Linstedt : Datavault Ronstad : Anchor Modeling

Challenges Classical Datawarehouse • Time-to-Build – Complexity, High Failure Rates • Lack of Agility – Expensive and Extensive re-engineering required to adapt • No auditability – Lack of tracebility, accountability and compliance • Departmental scope – No enterprise view, inability to effectively integrate disparate systems • Duplicate efforts and Spread Marts

Dan Lindstedt - Founder

Data Vault • ”The Data Vault is a detail oriented, historical tracking and uniquely linked set of normalized tables that support one or more functional areas of business. It is a hybrid approach encompassing the best of breed between 3 rd normal form (3 NF) and star schema. The design is flexible, scalable, consistent and adaptable to the needs of the enterprise. . ” Dan Linstedt • “The Data Vault is a data modelling approach and methodology that is specifically tuned to optimize your Enterprise Datawarehouse initiatives. ” Hans Hultgren

The DWH Guru’s opinion “The Data Vault is the optimal choice for modeling the Enterprise Datawaerehouse in the DW 2. 0 framework. ” Bill Inmon, June 2007

Datavault application in Netherlands

Datavault – 3 Building blocks • Hub : Identification – Unique collection of Business Keys – Business Key: identity of an enterprise entity • Link : Relationship – Unique collection of associations between two or more Business Keys – Unit Of Work (grain), transactions and events • Satellite : Description – Adds context to a Hub or. Link – Timebound (System Date/Time !)

Sample model • Identification Relation Description

Datavault modelled Datawarehouse Architecture Source 1 Star 6 Star 2 Star 7 Star 3 Star 8 Source. . Star 4 Star 9 Source x Star 5 Star x Source 2 Data Vault EDW Source 3 Error Marts (Operational) Reports Business Data Vault

Example

Data Vault model Airline #IATA Code Airline Aircraft #Reg. Nr Aircraft Airline Product Hierarchy Product Sale Aircraft Sale Flight Product #Product. Code Product Sale #Trx. ID Product Shop Flight #Flight. Nr #Scheduled. Date #Arr. Dep. Indicator Flight Gate Ramp Sale Product Shop Sale Shop #Shop. No Flight Aircraft Flight Sale Shop Gate / Ramp #Gate. Ramp. Code Lounge #Lounge. Code Gate / Ramp Shop Lounge Gate Lounge

Added value of Datavault modelling • Build Incrementally – Think Big Start Small • Scale to Infinity – Insert only • Auditability – Tracebility of the data and it’s history, versioning of data • Absorb all data all of the time – Store RAW data, Independent Loads, Lazy Updates, Single Point of Facts • Adapts to new sources easily – No need for re-engineering when new sources need to be integrated • Need for Operational Business Intelligence – Real Time Loading (SOA), Real Time Reporting and data maintenance

Build a flexible foundation with us! DWH / Datavault implementation Semantic layer Implementation Develop dashboards & reports Data Quality Management Data Integration Build an Analytics capability Self-service BI Implementation Big data POC’s 14

Datavault implementation services & tools • Datavault Education – By certified Datavault engineers • Datavault Implementation consultancy – Assist you in creating your Datavault model • Datavault Implementation Tools – Model driven Datawarehouse ETL code generation