Selling Data Vault

Dutch Data Vault MastersA Gathering of Creative Minds

Presenter: Date:

Note:

Company:eMail:

Twitter:

Elwyn Lloyd Jones5 June 2014

Sevenoaks Systems Ltd.eljones@ieee.org

Types of Argument

1. ad Verecundiam / ad HominemNSA, US Army, JP Morgan, ABN AMRO, KPN, Bank of England, Volvo, Mercedes, Volkswagen, Bill Inmon

2. ad Populum800 organizations

3. ad BaculumAudit-able system of records

4. Reducto ad Misericordiam Throw away and start from scratch Source integration => Enterprise crash

5. Up and UnderWhile client watches the BI - we’ll watch the DWH

6. Scallability (cost) – busting Moore’s LawNo columnar Netezza, Exadata, etc.

Data Vault as leverage for Data Migration

Exploring the benefits

Dutch Data Vault MastersA Gathering of Creative Minds

Presenter: Date:

Note:

Company:eMail:

Twitter:

Antoine StelmaJune 5 2014Presentation

Centennium a.stelma@centennium.nl@antoinestelma

Challenges

Client: • wants new ERP• 10 old source systems• own plant with intensive processes• MD/MT data is spread• unify & simplify architecture• business process changed

Quest

A data warehouse that:• helps speeding up and unifying data migration• contains current data and processes• is a solid base for a new ERP • unifies master & meta data

Two scenario’s

Source Source

Source SourceE D W

Source Source

Source Source

E D W

ERP

Module 1

Module 2 Module 3

Module 4

Module 5

Migrated data ETL

Source/MD/MT

Classic approach

Suggested approach

ERP

Module 1

Module 2 Module 3

Module 4

Module 5

Migrated data

ETL

Organization

How?

• scope of data for migration• type DV • collect master & metadata in H+S• unify & data cleaning • relations/links/etc. • alignment between old source and new ERP

Lessons (to be) Learned

#lesson1: An EDW can help you with data migration by using DV#lesson 2: EDW does not have to wait for full ERP implementation#lesson 3: It’s all about Data! # be creative

in Banking

Enabling market risk data analysis

Dutch Data Vault MastersA Gathering of Creative Minds

Presenter: Date:

Note:

Company:eMail:

Twitter:

Juan-José van der LindenJune 5, 2014

QOSQOjuan-jose.vanderlinden@QOSQO.nl@OS_Quipu

Bank Example

- Prudential Regulation Authority (PRA) - Prevent new financial crisis- Regular collection and storage data of firms’

trading book positions- Simulate risk scenarios

High-level requirements

Deliver an effective stress testing capability:

– Report stress results with material accuracy and on a timely basis

Requires an industrial-strength infrastructure for data collection, risk analysis and reporting

Challenges:Need for a flexible data repository:

– Effective data management (model changes)

– Facilitate historical data analysis – Facilitate data provenance

management

Architecture

Metadata

Data

Legend

Oracle data modeler

SQL Server

SSIS/SSAS/SSRS

Quipu

Tableau

Source data files

Matadata flow

Data flow

Based on

Stg DV 3NFETL

Src

SrcBM

xls Stg DV 3NFSSISSSIS

SSAS

SSIS

SSRS

Tableau

Proof of Concept and Project

Three-week proof of concept:- 1 week business data model support- 1 week data warehouse development - 1 week demonstrating changes and developing

Tableau reporting

Project support:- Model validation- Quipu training- Architecture support and validation

Ongoing:- Quipu product support as part of software

license

Takeaways

# 1 – Invest in getting the input 3NF business model.# 2 – Automation requires good data models.# 3 – Model and code generation drastically speeds up projects.# 4 – Generation tooling needs to support model changes. # 5 – Business value is made visible at reporting level.# 6 – DV modeling is an enabler, not the goal.

Volvo DV Automation

Dutch Data Vault MastersA Gathering of Creative Minds

Presenter: Date:

Note:

Company:eMail:

Twitter:

Frederik NaessensJune 5 2014In cooperation with Stijn Roelens & Kristof VandurenVolvoFrederik@k25.be

Volvo: criteria Automation Tool selection

Agile way of working– Handle migration between DEV-QA-PROD– Handle parallel development

Automation– Perform automatic data migration when data

model is modified– Automatically generate ELT code

Respect industry standards– Be netezza compliant– Generate a 100% Data Vault compliant data

model

Fluent interaction between business & it– Provide or interact with a business data model

3D – Configure extended properties

3D – Use predefined but flexible Model Conversion rules

3D – Configure extended properties

Are we ready for a Data Vault Standard?

Dutch Data Vault MastersA Gathering of Creative Minds

Presenter: Date:

Note:

Company:eMail:

Twitter:

Frederik NaessensJune 5 2014In cooperation with Stijn Roelens & Kristof VandurenVolvoFrederik@k25.be

Wherescape 3D

What we do: • Configure Source/Business model using 3NF

modeling conventions• Add additional metadata at Table /

Column / Link / … level• Based on a set of preconfigured rules,

generate:– Data vault model– Staging model– Staging Out views

Marketplace

Isn’t this what different software packages or consultancy firms are currently providing in the marketplace?

See:• Presentation of Lulzim Bilali on DWA

congress• QUIPU• BIReady• Generators built on top of data modeling

tools (Powerdesigner, ERWIN,… )

DV Model Conversion standard

Is now not the moment to define a DV model conversion standard?Why?• Different tools can align to a single standard

and create synergies• This can be a catalysator to convince

organisations or people which are new to DV concepts

• Brings the focus to the really important concepts of Data Vault:

• Business keys• UOW• …