SlideShare a Scribd company logo
Silwood Technology Limited
Accelerating Source Data Discovery Of Packaged
Applications For Business Intelligence
BBBT
21ST April 2015
Roland Bullivant
Sales and Marketing Director
rbullivant@silwoodtechnology.com
@rolandatsilwood
www.silwoodtechnology.com
Nick Porter
Technical Director
nporter@silwoodtechnology.com
One thing
Perspective
“Our management team is becoming inseparable from the technology which supports it.”
Paul Allaire, President, Xerox Coporation, The EIS Report, 1989, Business Intelligence
EIS/DSS
Financials
Manufacturing
Distribution
Sales
News
Market Data
What is our one thing?
Image courtesy of Hortonworks.com
“Where’s the data?”
SAP, Salesforce, Oracle
etc..
VENDORTOOLSPROLIFERATE
The Big Idea
“Metadata for the masses”
“The Google for SAP metadata”
“GPS for application packages”
“90,000 tables on your laptop”
“Discover - Scope - Deliver”
Perhaps it is easier to show you..
Quick demonstration
General ledger accounting
Agenda
• Silwood Technology
• Why are we here?
• Our market
• Background
• What is Safyr?
• Case studies
• Demonstration
• Wrap up and close
Silwood Technology
• UK based
• Privately held
• Data modelling (ERwin)
• Developed Safyr
• Major partners
• World class customers
• Continuous development
Partners
Sample customers
Why are we here?
• Visibility
• Education
• Feedback
Our market
• Increase value from
applications
• IT challenged with
data complexity
• SAP, Salesforce and
Oracle applications
New Low Latency world
IN MEMORY / BIG DATA / HADOOP / DATA LAKES
• Real time data
• Faster analytics
• Faster ERP/CRM etc
Source Data Intelligence
• Same challenge
• Delays have more impact
• Cannot wait for consultants
“The biggest internal debates so far have been around where we source the data
from and how we do integrated data modeling,” says Brian Raver, IT Manager of BI
Strategy and Systems Architecture at Medtronic. “Even though SAP HANA is a
high-performance appliance, you still have to think about the optimal way to model
the data.”
Why are these applications so challenging?
• Large
• Complex
• Customised
• Specialists only
• ‘Invisible’ data model
“The data in these (ERP) systems makes sense and are useful, but only in the context of the hard-coded processes. In
short, the data is trapped inside a complex web of thousands of database tables whose integrity is solely controlled by
a rigid fossilized collection of software algorithms. If you don’t believe me, just ask your SAP support staff for access to
directly update (or even read) a data table.”
John Schmidt (vice president of Global Integration Services at Informatica Corporation)
Barry Devlin
“..as any data warehouse manager
will confirm from bitter experience
the biggest technical challenge they
face is in understanding the source
systems for the warehouse,
extracting the data from them and
building a consistent set of
information from the combined
sources”
Barry Devlin (2011)
Data Warehouse Design Redux
Claudia Imhoff
“Another best practice for getting
started is to start with the database
schema of the existing operational or
transaction (source) systems. It is
possible to convert these designs
into technology and system models.
These can in turn be used as a
starting point for the enterprise data
model and subject area model.”
Claudia Imhoff (January 2010)
Fast-tracking Data Warehouse and
Business Intelligence Projects via
Intelligent Data Modelling
Quote from Hydro Tasmania
“The team was originally
informed that no data model
was available for the SAP
application or for SAP BW”.
Scott Delaney
BI Team Leader
Hydro Tasmania
Implications of not understanding data model in context of project
• Delay in benefits
• Late or under delivery
• Increased risk
• Over budget
• Loss of trust
“Where’s my data?”
Typical environment
• 000’s tables (but only
need a few)
• Complex relationships
(how are tables joined?)
• Descriptions
• Customisations
“How do I quickly and accurately find the right tables needed for my project?”
Safyr summary
• Extracts metadata
• Easy search and filter
• Visualise models
• Metadata in context
• 3rd Party export
Packaged Application Metadata: How do most companies do it now?
• Read documentation
• Ask technical specialists
• Ask consultants
• Re-key into spreadsheets
• Informed guesswork
• Internet search
• Use modelling tool
• Expect vendor to provide
Typical vendor approaches
• Interface to get data
– Connectors
– Templates
– Lists
• Inadequate context
David Marco
EDW 2015
You can try reverse engineering database with modelling tool
...and SAP has 90,000 tables!
Quote from AMD
“After doing a quick prototype
metadata extract from SAP, the
response has been very
positive!
I’m really grieving for the lost
years without access to this
tool. It has met and exceeded
my lofty expectations.”
Brian Farish
IT Architecture Manager
AMD
Safyr approach
Automates rapid harvesting and
discovery of metadata including
customisations
Powerful scoping and
introspection tools usable by data
specialists
Fast and easy integration with 3rd
party tools
Safyr – single source of trusted application metadata
Export
results of
scoping
SAP Business Suite
SAP BW
SAP Business Suite on HANA
Safyr™
Metadata
Discovery
Modelling
Data
Warehouse
Data
Integration
Metadata
management
Master Data
Management
Oracle eBusiness Suite
PeopleSoft
Siebel
JD Edwards EnterpriseOne
Source Applications Extract, discover and export
Other Packaged Applications
Salesforce (and Force)
Safyr main features
 Reverse engineers application metadata (inc. customisations)
 Finds all tables, fields, view, descriptions (logical AND physical)
 Automatically discovers all relationships and Application module
hierarchy
 Search, filter, navigate
 Compare (complete applications or individual subject areas)
 Visualise as models for easier understanding & communication
 Export to modelling, metadata management, integration and others
 Pre-configured Subject areas for SAP, JD Edwards, Siebel, Oracle EBS
 “ETL for Metadata” supports other packages (eg Dynamics)
 Rapid – extraction < 3 hours, analysis in days not months
 Accurate – works with system as implemented
Typical Safyr users
• Analysts
• Modellers
• Architects
• Miners
• Scientists
• Janitors
• Heroes
• Ninjas
Customer return and value from rapid source metadata discovery
• Faster project delivery
• Manage/reduce costs
• Higher productivity
• Accuracy of deliverables
• Fewer surprises during
project
Case study – Oil company
Challenge
JD Edwards EnterpriseOne
Replacing SAP
Customisations
Operational reporting
Under time pressure
‘Discovery’ bottleneck
Solution - Safyr™
Accelerate development
Meeting deadlines
Rapid implementation (hours)
Used by data architects
Automated discovery
Models for OBIEE
Case study – RS Components
Challenge
SAP
Heavily customised (117k)
Individual project delays
Reporting
Integration
‘Discovery’ bottleneck
Obstructs understanding
Reduces IT effectiveness
Hinders communication
Solution - Safyr™
Project deadlines met
Rapid implementation (days)
Better understanding of SAP
No guesswork
Enhanced communications
Additional uses:
JD Edwards to SAP migration
Summary from RS Components
 RS are succeeding in achieving a level of understanding of data in SAP that
we previously thought impossible
 We have quickly assembled a set of detailed subject area data models
which we can now use to guide project activities. The Safyr models deliver
a level of detail that we would not otherwise be able to achieve without
extensive user research (and a large helping of guesswork)
 We have high confidence in the detail in each model as it is coming directly
from SAP itself
 Based on the success of the Safyr option for SAP, we are looking to assess
the Safyr option for JD Edwards to accelerate the data mapping and
migration process for our SAP rollout to Asia
Case Study - Hydro Tasmania
• New SAP and BW
• New DWH and BI
• “No SAP data model”
• Reduced productivity
• Business losing faith
• Safyr for SAP data
model
• Rapid implementation
• Quick learning curve
• Back on track
• No backlog
“As a result of our investment in Safyr we are able to take a more agile approach
to meeting the demands for new reports and data within acceptable timescales
and the business’ trust in the information provided is growing”
Scott Delaney,
Hydro Tasmania
Case study – Global Semi-conductor maker
• Situation
– Multiple SAP instances
(30+)
– Customisations
• Global datawarehouse
– BW as staging area for
Teradata
• Application
consolidation
• ‘Discovery’ bottleneck
• Solution - Safyr™
– All reversed engineered in 1
month
– Understanding SAP and BW
– Huge productivity gain
• Was 4 staff for a month to find
transaction tables
• Now 1 person for a week
– Enhanced communications
– Time/cost saving
– Project delivery
Technical section
• It’s all about Scoping
• There are thousands of tables, but probably only interested
in a few 10s or 100s – but which?
• Metadata discovery is the key
– ‘scope’ the required tables
– Then visualize as a data model
• Utilize metadata in project EIM tools
How to identify ‘required’ tables and relationships?
38
• Need to make ‘Subject Areas’ relevant to the task
• Relationships really help
– Give context to a table
– Provide an important means to find tables that are ‘in
scope’
• Seeing tables in the context of function
– Which tables are used by a program, or component?
Divide and Conquer
39
Want to find data behind key Business Concepts
o Manufacturing
o Shipping to Warehouse
o Customer Orders
o Bill of Materials
o Invoicing
o Payments
o Returns
o Customer Master
o Vendor Master
DEMONSTRATION
Watch the full recording and demonstration at: http://www.bbbt.us/resources/videos/2015-2/
Questions
Comments
Feedback
Want to learn more?
Visit: www.silwoodtechnology.com
Email: info@silwoodtechnology.com
Request evaluation copy:
http://www.silwoodtechnology.com/safyr-evaluation-licence/
Call: +44 1344 876 553
Watch the full recording and demonstration at: http://www.bbbt.us/resources/videos/2015-2/

More Related Content

What's hot

Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016
Caserta
 
You're the New CDO, Now What?
You're the New CDO, Now What?You're the New CDO, Now What?
You're the New CDO, Now What?
Caserta
 
The Emerging Role of the Data Lake
The Emerging Role of the Data LakeThe Emerging Role of the Data Lake
The Emerging Role of the Data Lake
Caserta
 
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing KeynoteArchitecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
Caserta
 
Making Big Data Easy for Everyone
Making Big Data Easy for EveryoneMaking Big Data Easy for Everyone
Making Big Data Easy for Everyone
Caserta
 
Incorporating the Data Lake into Your Analytic Architecture
Incorporating the Data Lake into Your Analytic ArchitectureIncorporating the Data Lake into Your Analytic Architecture
Incorporating the Data Lake into Your Analytic Architecture
Caserta
 
Big Data: Setting Up the Big Data Lake
Big Data: Setting Up the Big Data LakeBig Data: Setting Up the Big Data Lake
Big Data: Setting Up the Big Data Lake
Caserta
 
Operational Analytics Using Spark and NoSQL Data Stores
Operational Analytics Using Spark and NoSQL Data StoresOperational Analytics Using Spark and NoSQL Data Stores
Operational Analytics Using Spark and NoSQL Data Stores
DATAVERSITY
 
Big Data Discovery
Big Data DiscoveryBig Data Discovery
Big Data Discovery
Harald Erb
 
Creating a Next-Generation Big Data Architecture
Creating a Next-Generation Big Data ArchitectureCreating a Next-Generation Big Data Architecture
Creating a Next-Generation Big Data Architecture
Perficient, Inc.
 
The Maturity Model: Taking the Growing Pains Out of Hadoop
The Maturity Model: Taking the Growing Pains Out of HadoopThe Maturity Model: Taking the Growing Pains Out of Hadoop
The Maturity Model: Taking the Growing Pains Out of Hadoop
Inside Analysis
 
Agile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric ApproachAgile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric Approach
SoftServe
 
Data lake benefits
Data lake benefitsData lake benefits
Data lake benefits
Ricky Barron
 
The Emerging Data Lake IT Strategy
The Emerging Data Lake IT StrategyThe Emerging Data Lake IT Strategy
The Emerging Data Lake IT Strategy
Thomas Kelly, PMP
 
Data Discovery and BI - Is there Really a Difference?
Data Discovery and BI - Is there Really a Difference?Data Discovery and BI - Is there Really a Difference?
Data Discovery and BI - Is there Really a Difference?
Inside Analysis
 
Agile, Automated, Aware: How to Model for Success
Agile, Automated, Aware: How to Model for SuccessAgile, Automated, Aware: How to Model for Success
Agile, Automated, Aware: How to Model for Success
Inside Analysis
 
Predictive analytics from a to z
Predictive analytics from a to zPredictive analytics from a to z
Predictive analytics from a to z
alpinedatalabs
 
Best Practices for Building a Warehouse Quickly
Best Practices for Building a Warehouse QuicklyBest Practices for Building a Warehouse Quickly
Best Practices for Building a Warehouse Quickly
WhereScape
 
Building enterprise advance analytics platform
Building enterprise advance analytics platformBuilding enterprise advance analytics platform
Building enterprise advance analytics platform
Haoran Du
 
Accelerate Self-Service Analytics with Virtualization and Visualisation (Thai)
Accelerate Self-Service Analytics with Virtualization and Visualisation (Thai)Accelerate Self-Service Analytics with Virtualization and Visualisation (Thai)
Accelerate Self-Service Analytics with Virtualization and Visualisation (Thai)
Denodo
 

What's hot (20)

Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016
Building New Data Ecosystem for Customer Analytics, Strata + Hadoop World, 2016
 
You're the New CDO, Now What?
You're the New CDO, Now What?You're the New CDO, Now What?
You're the New CDO, Now What?
 
The Emerging Role of the Data Lake
The Emerging Role of the Data LakeThe Emerging Role of the Data Lake
The Emerging Role of the Data Lake
 
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing KeynoteArchitecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
Architecting Data For The Modern Enterprise - Data Summit 2017, Closing Keynote
 
Making Big Data Easy for Everyone
Making Big Data Easy for EveryoneMaking Big Data Easy for Everyone
Making Big Data Easy for Everyone
 
Incorporating the Data Lake into Your Analytic Architecture
Incorporating the Data Lake into Your Analytic ArchitectureIncorporating the Data Lake into Your Analytic Architecture
Incorporating the Data Lake into Your Analytic Architecture
 
Big Data: Setting Up the Big Data Lake
Big Data: Setting Up the Big Data LakeBig Data: Setting Up the Big Data Lake
Big Data: Setting Up the Big Data Lake
 
Operational Analytics Using Spark and NoSQL Data Stores
Operational Analytics Using Spark and NoSQL Data StoresOperational Analytics Using Spark and NoSQL Data Stores
Operational Analytics Using Spark and NoSQL Data Stores
 
Big Data Discovery
Big Data DiscoveryBig Data Discovery
Big Data Discovery
 
Creating a Next-Generation Big Data Architecture
Creating a Next-Generation Big Data ArchitectureCreating a Next-Generation Big Data Architecture
Creating a Next-Generation Big Data Architecture
 
The Maturity Model: Taking the Growing Pains Out of Hadoop
The Maturity Model: Taking the Growing Pains Out of HadoopThe Maturity Model: Taking the Growing Pains Out of Hadoop
The Maturity Model: Taking the Growing Pains Out of Hadoop
 
Agile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric ApproachAgile Big Data Analytics Development: An Architecture-Centric Approach
Agile Big Data Analytics Development: An Architecture-Centric Approach
 
Data lake benefits
Data lake benefitsData lake benefits
Data lake benefits
 
The Emerging Data Lake IT Strategy
The Emerging Data Lake IT StrategyThe Emerging Data Lake IT Strategy
The Emerging Data Lake IT Strategy
 
Data Discovery and BI - Is there Really a Difference?
Data Discovery and BI - Is there Really a Difference?Data Discovery and BI - Is there Really a Difference?
Data Discovery and BI - Is there Really a Difference?
 
Agile, Automated, Aware: How to Model for Success
Agile, Automated, Aware: How to Model for SuccessAgile, Automated, Aware: How to Model for Success
Agile, Automated, Aware: How to Model for Success
 
Predictive analytics from a to z
Predictive analytics from a to zPredictive analytics from a to z
Predictive analytics from a to z
 
Best Practices for Building a Warehouse Quickly
Best Practices for Building a Warehouse QuicklyBest Practices for Building a Warehouse Quickly
Best Practices for Building a Warehouse Quickly
 
Building enterprise advance analytics platform
Building enterprise advance analytics platformBuilding enterprise advance analytics platform
Building enterprise advance analytics platform
 
Accelerate Self-Service Analytics with Virtualization and Visualisation (Thai)
Accelerate Self-Service Analytics with Virtualization and Visualisation (Thai)Accelerate Self-Service Analytics with Virtualization and Visualisation (Thai)
Accelerate Self-Service Analytics with Virtualization and Visualisation (Thai)
 

Similar to Bbbt presentation 210415_final_2

Metadata discovery for enterprise packages - a better approach
Metadata discovery for enterprise packages - a better approachMetadata discovery for enterprise packages - a better approach
Metadata discovery for enterprise packages - a better approach
Roland Bullivant
 
BAR360 open data platform presentation at DAMA, Sydney
BAR360 open data platform presentation at DAMA, SydneyBAR360 open data platform presentation at DAMA, Sydney
BAR360 open data platform presentation at DAMA, Sydney
Sai Paravastu
 
When and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data ArchitectureWhen and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data Architecture
DATAVERSITY
 
Trends in Enterprise Advanced Analytics
Trends in Enterprise Advanced AnalyticsTrends in Enterprise Advanced Analytics
Trends in Enterprise Advanced Analytics
DATAVERSITY
 
Big Data Made Easy: A Simple, Scalable Solution for Getting Started with Hadoop
Big Data Made Easy:  A Simple, Scalable Solution for Getting Started with HadoopBig Data Made Easy:  A Simple, Scalable Solution for Getting Started with Hadoop
Big Data Made Easy: A Simple, Scalable Solution for Getting Started with Hadoop
Precisely
 
Analytics in a Day Virtual Workshop
Analytics in a Day Virtual WorkshopAnalytics in a Day Virtual Workshop
Analytics in a Day Virtual Workshop
CCG
 
Is your big data journey stalling? Take the Leap with Capgemini and Cloudera
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaIs your big data journey stalling? Take the Leap with Capgemini and Cloudera
Is your big data journey stalling? Take the Leap with Capgemini and Cloudera
Cloudera, Inc.
 
Hadoop meets Agile! - An Agile Big Data Model
Hadoop meets Agile! - An Agile Big Data ModelHadoop meets Agile! - An Agile Big Data Model
Hadoop meets Agile! - An Agile Big Data Model
Uwe Printz
 
Retail & CPG
Retail & CPGRetail & CPG
Analytics in a Day Ft. Synapse Virtual Workshop
Analytics in a Day Ft. Synapse Virtual WorkshopAnalytics in a Day Ft. Synapse Virtual Workshop
Analytics in a Day Ft. Synapse Virtual Workshop
CCG
 
Data Vault Introduction
Data Vault IntroductionData Vault Introduction
Data Vault Introduction
Patrick Van Renterghem
 
Future of Making Things
Future of Making ThingsFuture of Making Things
Future of Making Things
JC Davis
 
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
DATAVERSITY
 
Total Data Industry Report
Total Data Industry ReportTotal Data Industry Report
Total Data Industry Report
Ran Zhang
 
[DSC Europe 22] Overview of the Databricks Platform - Petar Zecevic
[DSC Europe 22] Overview of the Databricks Platform - Petar Zecevic[DSC Europe 22] Overview of the Databricks Platform - Petar Zecevic
[DSC Europe 22] Overview of the Databricks Platform - Petar Zecevic
DataScienceConferenc1
 
The Practice of Big Data - The Hadoop ecosystem explained with usage scenarios
The Practice of Big Data - The Hadoop ecosystem explained with usage scenariosThe Practice of Big Data - The Hadoop ecosystem explained with usage scenarios
The Practice of Big Data - The Hadoop ecosystem explained with usage scenarios
kcmallu
 
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Denodo
 
Architecting Agile Data Applications for Scale
Architecting Agile Data Applications for ScaleArchitecting Agile Data Applications for Scale
Architecting Agile Data Applications for Scale
Databricks
 
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
email2jl
 
5 Things that Make Hadoop a Game Changer
5 Things that Make Hadoop a Game Changer5 Things that Make Hadoop a Game Changer
5 Things that Make Hadoop a Game Changer
Caserta
 

Similar to Bbbt presentation 210415_final_2 (20)

Metadata discovery for enterprise packages - a better approach
Metadata discovery for enterprise packages - a better approachMetadata discovery for enterprise packages - a better approach
Metadata discovery for enterprise packages - a better approach
 
BAR360 open data platform presentation at DAMA, Sydney
BAR360 open data platform presentation at DAMA, SydneyBAR360 open data platform presentation at DAMA, Sydney
BAR360 open data platform presentation at DAMA, Sydney
 
When and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data ArchitectureWhen and How Data Lakes Fit into a Modern Data Architecture
When and How Data Lakes Fit into a Modern Data Architecture
 
Trends in Enterprise Advanced Analytics
Trends in Enterprise Advanced AnalyticsTrends in Enterprise Advanced Analytics
Trends in Enterprise Advanced Analytics
 
Big Data Made Easy: A Simple, Scalable Solution for Getting Started with Hadoop
Big Data Made Easy:  A Simple, Scalable Solution for Getting Started with HadoopBig Data Made Easy:  A Simple, Scalable Solution for Getting Started with Hadoop
Big Data Made Easy: A Simple, Scalable Solution for Getting Started with Hadoop
 
Analytics in a Day Virtual Workshop
Analytics in a Day Virtual WorkshopAnalytics in a Day Virtual Workshop
Analytics in a Day Virtual Workshop
 
Is your big data journey stalling? Take the Leap with Capgemini and Cloudera
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaIs your big data journey stalling? Take the Leap with Capgemini and Cloudera
Is your big data journey stalling? Take the Leap with Capgemini and Cloudera
 
Hadoop meets Agile! - An Agile Big Data Model
Hadoop meets Agile! - An Agile Big Data ModelHadoop meets Agile! - An Agile Big Data Model
Hadoop meets Agile! - An Agile Big Data Model
 
Retail & CPG
Retail & CPGRetail & CPG
Retail & CPG
 
Analytics in a Day Ft. Synapse Virtual Workshop
Analytics in a Day Ft. Synapse Virtual WorkshopAnalytics in a Day Ft. Synapse Virtual Workshop
Analytics in a Day Ft. Synapse Virtual Workshop
 
Data Vault Introduction
Data Vault IntroductionData Vault Introduction
Data Vault Introduction
 
Future of Making Things
Future of Making ThingsFuture of Making Things
Future of Making Things
 
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data ArchitectureADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
ADV Slides: When and How Data Lakes Fit into a Modern Data Architecture
 
Total Data Industry Report
Total Data Industry ReportTotal Data Industry Report
Total Data Industry Report
 
[DSC Europe 22] Overview of the Databricks Platform - Petar Zecevic
[DSC Europe 22] Overview of the Databricks Platform - Petar Zecevic[DSC Europe 22] Overview of the Databricks Platform - Petar Zecevic
[DSC Europe 22] Overview of the Databricks Platform - Petar Zecevic
 
The Practice of Big Data - The Hadoop ecosystem explained with usage scenarios
The Practice of Big Data - The Hadoop ecosystem explained with usage scenariosThe Practice of Big Data - The Hadoop ecosystem explained with usage scenarios
The Practice of Big Data - The Hadoop ecosystem explained with usage scenarios
 
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
Why Your Data Science Architecture Should Include a Data Virtualization Tool ...
 
Architecting Agile Data Applications for Scale
Architecting Agile Data Applications for ScaleArchitecting Agile Data Applications for Scale
Architecting Agile Data Applications for Scale
 
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
Creatinganext generationbigdataarchitecture-141204150317-conversion-gate02
 
5 Things that Make Hadoop a Game Changer
5 Things that Make Hadoop a Game Changer5 Things that Make Hadoop a Game Changer
5 Things that Make Hadoop a Game Changer
 

More from Roland Bullivant

Using Safyr to navigate and analyse SAP data model demonstration screen shots
Using Safyr to navigate and analyse SAP data model demonstration screen shotsUsing Safyr to navigate and analyse SAP data model demonstration screen shots
Using Safyr to navigate and analyse SAP data model demonstration screen shots
Roland Bullivant
 
The Business Value of Metadata for Data Governance
The Business Value of Metadata for Data GovernanceThe Business Value of Metadata for Data Governance
The Business Value of Metadata for Data Governance
Roland Bullivant
 
Silwood Webinar: Comparing data models for different instances of CRM and ERP...
Silwood Webinar: Comparing data models for different instances of CRM and ERP...Silwood Webinar: Comparing data models for different instances of CRM and ERP...
Silwood Webinar: Comparing data models for different instances of CRM and ERP...
Roland Bullivant
 
Where's the data
Where's the dataWhere's the data
Where's the data
Roland Bullivant
 
Managing change in an agile Salesforce development environment
Managing change in an agile Salesforce development environmentManaging change in an agile Salesforce development environment
Managing change in an agile Salesforce development environment
Roland Bullivant
 
"Where's the data?" The role of metadata in enabling the transformation to a ...
"Where's the data?" The role of metadata in enabling the transformation to a ..."Where's the data?" The role of metadata in enabling the transformation to a ...
"Where's the data?" The role of metadata in enabling the transformation to a ...
Roland Bullivant
 

More from Roland Bullivant (6)

Using Safyr to navigate and analyse SAP data model demonstration screen shots
Using Safyr to navigate and analyse SAP data model demonstration screen shotsUsing Safyr to navigate and analyse SAP data model demonstration screen shots
Using Safyr to navigate and analyse SAP data model demonstration screen shots
 
The Business Value of Metadata for Data Governance
The Business Value of Metadata for Data GovernanceThe Business Value of Metadata for Data Governance
The Business Value of Metadata for Data Governance
 
Silwood Webinar: Comparing data models for different instances of CRM and ERP...
Silwood Webinar: Comparing data models for different instances of CRM and ERP...Silwood Webinar: Comparing data models for different instances of CRM and ERP...
Silwood Webinar: Comparing data models for different instances of CRM and ERP...
 
Where's the data
Where's the dataWhere's the data
Where's the data
 
Managing change in an agile Salesforce development environment
Managing change in an agile Salesforce development environmentManaging change in an agile Salesforce development environment
Managing change in an agile Salesforce development environment
 
"Where's the data?" The role of metadata in enabling the transformation to a ...
"Where's the data?" The role of metadata in enabling the transformation to a ..."Where's the data?" The role of metadata in enabling the transformation to a ...
"Where's the data?" The role of metadata in enabling the transformation to a ...
 

Recently uploaded

Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...
Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...
Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...
Paul Brebner
 
Alluxio Webinar | 10x Faster Trino Queries on Your Data Platform
Alluxio Webinar | 10x Faster Trino Queries on Your Data PlatformAlluxio Webinar | 10x Faster Trino Queries on Your Data Platform
Alluxio Webinar | 10x Faster Trino Queries on Your Data Platform
Alluxio, Inc.
 
一比一原版(UMN毕业证)明尼苏达大学毕业证如何办理
一比一原版(UMN毕业证)明尼苏达大学毕业证如何办理一比一原版(UMN毕业证)明尼苏达大学毕业证如何办理
一比一原版(UMN毕业证)明尼苏达大学毕业证如何办理
dakas1
 
Safelyio Toolbox Talk Softwate & App (How To Digitize Safety Meetings)
Safelyio Toolbox Talk Softwate & App (How To Digitize Safety Meetings)Safelyio Toolbox Talk Softwate & App (How To Digitize Safety Meetings)
Safelyio Toolbox Talk Softwate & App (How To Digitize Safety Meetings)
safelyiotech
 
Migration From CH 1.0 to CH 2.0 and Mule 4.6 & Java 17 Upgrade.pptx
Migration From CH 1.0 to CH 2.0 and  Mule 4.6 & Java 17 Upgrade.pptxMigration From CH 1.0 to CH 2.0 and  Mule 4.6 & Java 17 Upgrade.pptx
Migration From CH 1.0 to CH 2.0 and Mule 4.6 & Java 17 Upgrade.pptx
ervikas4
 
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...
kalichargn70th171
 
Penify - Let AI do the Documentation, you write the Code.
Penify - Let AI do the Documentation, you write the Code.Penify - Let AI do the Documentation, you write the Code.
Penify - Let AI do the Documentation, you write the Code.
KrishnaveniMohan1
 
WMF 2024 - Unlocking the Future of Data Powering Next-Gen AI with Vector Data...
WMF 2024 - Unlocking the Future of Data Powering Next-Gen AI with Vector Data...WMF 2024 - Unlocking the Future of Data Powering Next-Gen AI with Vector Data...
WMF 2024 - Unlocking the Future of Data Powering Next-Gen AI with Vector Data...
Luigi Fugaro
 
一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理
一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理
一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理
kgyxske
 
如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样
如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样
如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样
gapen1
 
Superpower Your Apache Kafka Applications Development with Complementary Open...
Superpower Your Apache Kafka Applications Development with Complementary Open...Superpower Your Apache Kafka Applications Development with Complementary Open...
Superpower Your Apache Kafka Applications Development with Complementary Open...
Paul Brebner
 
Optimizing Your E-commerce with WooCommerce.pptx
Optimizing Your E-commerce with WooCommerce.pptxOptimizing Your E-commerce with WooCommerce.pptx
Optimizing Your E-commerce with WooCommerce.pptx
WebConnect Pvt Ltd
 
Boost Your Savings with These Money Management Apps
Boost Your Savings with These Money Management AppsBoost Your Savings with These Money Management Apps
Boost Your Savings with These Money Management Apps
Jhone kinadey
 
Operational ease MuleSoft and Salesforce Service Cloud Solution v1.0.pptx
Operational ease MuleSoft and Salesforce Service Cloud Solution v1.0.pptxOperational ease MuleSoft and Salesforce Service Cloud Solution v1.0.pptx
Operational ease MuleSoft and Salesforce Service Cloud Solution v1.0.pptx
sandeepmenon62
 
The Rising Future of CPaaS in the Middle East 2024
The Rising Future of CPaaS in the Middle East 2024The Rising Future of CPaaS in the Middle East 2024
The Rising Future of CPaaS in the Middle East 2024
Yara Milbes
 
Upturn India Technologies - Web development company in Nashik
Upturn India Technologies - Web development company in NashikUpturn India Technologies - Web development company in Nashik
Upturn India Technologies - Web development company in Nashik
Upturn India Technologies
 
Transforming Product Development using OnePlan To Boost Efficiency and Innova...
Transforming Product Development using OnePlan To Boost Efficiency and Innova...Transforming Product Development using OnePlan To Boost Efficiency and Innova...
Transforming Product Development using OnePlan To Boost Efficiency and Innova...
OnePlan Solutions
 
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...
XfilesPro
 
美洲杯赔率投注网【​网址​🎉3977·EE​🎉】
美洲杯赔率投注网【​网址​🎉3977·EE​🎉】美洲杯赔率投注网【​网址​🎉3977·EE​🎉】
美洲杯赔率投注网【​网址​🎉3977·EE​🎉】
widenerjobeyrl638
 

Recently uploaded (20)

Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...
Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...
Why Apache Kafka Clusters Are Like Galaxies (And Other Cosmic Kafka Quandarie...
 
Alluxio Webinar | 10x Faster Trino Queries on Your Data Platform
Alluxio Webinar | 10x Faster Trino Queries on Your Data PlatformAlluxio Webinar | 10x Faster Trino Queries on Your Data Platform
Alluxio Webinar | 10x Faster Trino Queries on Your Data Platform
 
一比一原版(UMN毕业证)明尼苏达大学毕业证如何办理
一比一原版(UMN毕业证)明尼苏达大学毕业证如何办理一比一原版(UMN毕业证)明尼苏达大学毕业证如何办理
一比一原版(UMN毕业证)明尼苏达大学毕业证如何办理
 
Safelyio Toolbox Talk Softwate & App (How To Digitize Safety Meetings)
Safelyio Toolbox Talk Softwate & App (How To Digitize Safety Meetings)Safelyio Toolbox Talk Softwate & App (How To Digitize Safety Meetings)
Safelyio Toolbox Talk Softwate & App (How To Digitize Safety Meetings)
 
Migration From CH 1.0 to CH 2.0 and Mule 4.6 & Java 17 Upgrade.pptx
Migration From CH 1.0 to CH 2.0 and  Mule 4.6 & Java 17 Upgrade.pptxMigration From CH 1.0 to CH 2.0 and  Mule 4.6 & Java 17 Upgrade.pptx
Migration From CH 1.0 to CH 2.0 and Mule 4.6 & Java 17 Upgrade.pptx
 
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...
The Power of Visual Regression Testing_ Why It Is Critical for Enterprise App...
 
Penify - Let AI do the Documentation, you write the Code.
Penify - Let AI do the Documentation, you write the Code.Penify - Let AI do the Documentation, you write the Code.
Penify - Let AI do the Documentation, you write the Code.
 
WMF 2024 - Unlocking the Future of Data Powering Next-Gen AI with Vector Data...
WMF 2024 - Unlocking the Future of Data Powering Next-Gen AI with Vector Data...WMF 2024 - Unlocking the Future of Data Powering Next-Gen AI with Vector Data...
WMF 2024 - Unlocking the Future of Data Powering Next-Gen AI with Vector Data...
 
一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理
一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理
一比一原版(sdsu毕业证书)圣地亚哥州立大学毕业证如何办理
 
如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样
如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样
如何办理(hull学位证书)英国赫尔大学毕业证硕士文凭原版一模一样
 
Superpower Your Apache Kafka Applications Development with Complementary Open...
Superpower Your Apache Kafka Applications Development with Complementary Open...Superpower Your Apache Kafka Applications Development with Complementary Open...
Superpower Your Apache Kafka Applications Development with Complementary Open...
 
Optimizing Your E-commerce with WooCommerce.pptx
Optimizing Your E-commerce with WooCommerce.pptxOptimizing Your E-commerce with WooCommerce.pptx
Optimizing Your E-commerce with WooCommerce.pptx
 
Boost Your Savings with These Money Management Apps
Boost Your Savings with These Money Management AppsBoost Your Savings with These Money Management Apps
Boost Your Savings with These Money Management Apps
 
Operational ease MuleSoft and Salesforce Service Cloud Solution v1.0.pptx
Operational ease MuleSoft and Salesforce Service Cloud Solution v1.0.pptxOperational ease MuleSoft and Salesforce Service Cloud Solution v1.0.pptx
Operational ease MuleSoft and Salesforce Service Cloud Solution v1.0.pptx
 
The Rising Future of CPaaS in the Middle East 2024
The Rising Future of CPaaS in the Middle East 2024The Rising Future of CPaaS in the Middle East 2024
The Rising Future of CPaaS in the Middle East 2024
 
bgiolcb
bgiolcbbgiolcb
bgiolcb
 
Upturn India Technologies - Web development company in Nashik
Upturn India Technologies - Web development company in NashikUpturn India Technologies - Web development company in Nashik
Upturn India Technologies - Web development company in Nashik
 
Transforming Product Development using OnePlan To Boost Efficiency and Innova...
Transforming Product Development using OnePlan To Boost Efficiency and Innova...Transforming Product Development using OnePlan To Boost Efficiency and Innova...
Transforming Product Development using OnePlan To Boost Efficiency and Innova...
 
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...
Everything You Need to Know About X-Sign: The eSign Functionality of XfilesPr...
 
美洲杯赔率投注网【​网址​🎉3977·EE​🎉】
美洲杯赔率投注网【​网址​🎉3977·EE​🎉】美洲杯赔率投注网【​网址​🎉3977·EE​🎉】
美洲杯赔率投注网【​网址​🎉3977·EE​🎉】
 

Bbbt presentation 210415_final_2

  • 1. Silwood Technology Limited Accelerating Source Data Discovery Of Packaged Applications For Business Intelligence BBBT 21ST April 2015
  • 2. Roland Bullivant Sales and Marketing Director rbullivant@silwoodtechnology.com @rolandatsilwood www.silwoodtechnology.com Nick Porter Technical Director nporter@silwoodtechnology.com
  • 4. Perspective “Our management team is becoming inseparable from the technology which supports it.” Paul Allaire, President, Xerox Coporation, The EIS Report, 1989, Business Intelligence EIS/DSS Financials Manufacturing Distribution Sales News Market Data
  • 5. What is our one thing? Image courtesy of Hortonworks.com “Where’s the data?” SAP, Salesforce, Oracle etc.. VENDORTOOLSPROLIFERATE
  • 6. The Big Idea “Metadata for the masses” “The Google for SAP metadata” “GPS for application packages” “90,000 tables on your laptop” “Discover - Scope - Deliver”
  • 7. Perhaps it is easier to show you.. Quick demonstration General ledger accounting
  • 8. Agenda • Silwood Technology • Why are we here? • Our market • Background • What is Safyr? • Case studies • Demonstration • Wrap up and close
  • 9. Silwood Technology • UK based • Privately held • Data modelling (ERwin) • Developed Safyr • Major partners • World class customers • Continuous development
  • 12. Why are we here? • Visibility • Education • Feedback
  • 13. Our market • Increase value from applications • IT challenged with data complexity • SAP, Salesforce and Oracle applications
  • 14. New Low Latency world IN MEMORY / BIG DATA / HADOOP / DATA LAKES • Real time data • Faster analytics • Faster ERP/CRM etc Source Data Intelligence • Same challenge • Delays have more impact • Cannot wait for consultants “The biggest internal debates so far have been around where we source the data from and how we do integrated data modeling,” says Brian Raver, IT Manager of BI Strategy and Systems Architecture at Medtronic. “Even though SAP HANA is a high-performance appliance, you still have to think about the optimal way to model the data.”
  • 15. Why are these applications so challenging? • Large • Complex • Customised • Specialists only • ‘Invisible’ data model “The data in these (ERP) systems makes sense and are useful, but only in the context of the hard-coded processes. In short, the data is trapped inside a complex web of thousands of database tables whose integrity is solely controlled by a rigid fossilized collection of software algorithms. If you don’t believe me, just ask your SAP support staff for access to directly update (or even read) a data table.” John Schmidt (vice president of Global Integration Services at Informatica Corporation)
  • 16. Barry Devlin “..as any data warehouse manager will confirm from bitter experience the biggest technical challenge they face is in understanding the source systems for the warehouse, extracting the data from them and building a consistent set of information from the combined sources” Barry Devlin (2011) Data Warehouse Design Redux
  • 17. Claudia Imhoff “Another best practice for getting started is to start with the database schema of the existing operational or transaction (source) systems. It is possible to convert these designs into technology and system models. These can in turn be used as a starting point for the enterprise data model and subject area model.” Claudia Imhoff (January 2010) Fast-tracking Data Warehouse and Business Intelligence Projects via Intelligent Data Modelling
  • 18. Quote from Hydro Tasmania “The team was originally informed that no data model was available for the SAP application or for SAP BW”. Scott Delaney BI Team Leader Hydro Tasmania
  • 19. Implications of not understanding data model in context of project • Delay in benefits • Late or under delivery • Increased risk • Over budget • Loss of trust
  • 20. “Where’s my data?” Typical environment • 000’s tables (but only need a few) • Complex relationships (how are tables joined?) • Descriptions • Customisations “How do I quickly and accurately find the right tables needed for my project?”
  • 21. Safyr summary • Extracts metadata • Easy search and filter • Visualise models • Metadata in context • 3rd Party export
  • 22. Packaged Application Metadata: How do most companies do it now? • Read documentation • Ask technical specialists • Ask consultants • Re-key into spreadsheets • Informed guesswork • Internet search • Use modelling tool • Expect vendor to provide
  • 23. Typical vendor approaches • Interface to get data – Connectors – Templates – Lists • Inadequate context David Marco EDW 2015
  • 24. You can try reverse engineering database with modelling tool
  • 25. ...and SAP has 90,000 tables!
  • 26. Quote from AMD “After doing a quick prototype metadata extract from SAP, the response has been very positive! I’m really grieving for the lost years without access to this tool. It has met and exceeded my lofty expectations.” Brian Farish IT Architecture Manager AMD
  • 27. Safyr approach Automates rapid harvesting and discovery of metadata including customisations Powerful scoping and introspection tools usable by data specialists Fast and easy integration with 3rd party tools
  • 28. Safyr – single source of trusted application metadata Export results of scoping SAP Business Suite SAP BW SAP Business Suite on HANA Safyr™ Metadata Discovery Modelling Data Warehouse Data Integration Metadata management Master Data Management Oracle eBusiness Suite PeopleSoft Siebel JD Edwards EnterpriseOne Source Applications Extract, discover and export Other Packaged Applications Salesforce (and Force)
  • 29. Safyr main features  Reverse engineers application metadata (inc. customisations)  Finds all tables, fields, view, descriptions (logical AND physical)  Automatically discovers all relationships and Application module hierarchy  Search, filter, navigate  Compare (complete applications or individual subject areas)  Visualise as models for easier understanding & communication  Export to modelling, metadata management, integration and others  Pre-configured Subject areas for SAP, JD Edwards, Siebel, Oracle EBS  “ETL for Metadata” supports other packages (eg Dynamics)  Rapid – extraction < 3 hours, analysis in days not months  Accurate – works with system as implemented
  • 30. Typical Safyr users • Analysts • Modellers • Architects • Miners • Scientists • Janitors • Heroes • Ninjas
  • 31. Customer return and value from rapid source metadata discovery • Faster project delivery • Manage/reduce costs • Higher productivity • Accuracy of deliverables • Fewer surprises during project
  • 32. Case study – Oil company Challenge JD Edwards EnterpriseOne Replacing SAP Customisations Operational reporting Under time pressure ‘Discovery’ bottleneck Solution - Safyr™ Accelerate development Meeting deadlines Rapid implementation (hours) Used by data architects Automated discovery Models for OBIEE
  • 33. Case study – RS Components Challenge SAP Heavily customised (117k) Individual project delays Reporting Integration ‘Discovery’ bottleneck Obstructs understanding Reduces IT effectiveness Hinders communication Solution - Safyr™ Project deadlines met Rapid implementation (days) Better understanding of SAP No guesswork Enhanced communications Additional uses: JD Edwards to SAP migration
  • 34. Summary from RS Components  RS are succeeding in achieving a level of understanding of data in SAP that we previously thought impossible  We have quickly assembled a set of detailed subject area data models which we can now use to guide project activities. The Safyr models deliver a level of detail that we would not otherwise be able to achieve without extensive user research (and a large helping of guesswork)  We have high confidence in the detail in each model as it is coming directly from SAP itself  Based on the success of the Safyr option for SAP, we are looking to assess the Safyr option for JD Edwards to accelerate the data mapping and migration process for our SAP rollout to Asia
  • 35. Case Study - Hydro Tasmania • New SAP and BW • New DWH and BI • “No SAP data model” • Reduced productivity • Business losing faith • Safyr for SAP data model • Rapid implementation • Quick learning curve • Back on track • No backlog “As a result of our investment in Safyr we are able to take a more agile approach to meeting the demands for new reports and data within acceptable timescales and the business’ trust in the information provided is growing” Scott Delaney, Hydro Tasmania
  • 36. Case study – Global Semi-conductor maker • Situation – Multiple SAP instances (30+) – Customisations • Global datawarehouse – BW as staging area for Teradata • Application consolidation • ‘Discovery’ bottleneck • Solution - Safyr™ – All reversed engineered in 1 month – Understanding SAP and BW – Huge productivity gain • Was 4 staff for a month to find transaction tables • Now 1 person for a week – Enhanced communications – Time/cost saving – Project delivery
  • 38. • It’s all about Scoping • There are thousands of tables, but probably only interested in a few 10s or 100s – but which? • Metadata discovery is the key – ‘scope’ the required tables – Then visualize as a data model • Utilize metadata in project EIM tools How to identify ‘required’ tables and relationships? 38
  • 39. • Need to make ‘Subject Areas’ relevant to the task • Relationships really help – Give context to a table – Provide an important means to find tables that are ‘in scope’ • Seeing tables in the context of function – Which tables are used by a program, or component? Divide and Conquer 39
  • 40. Want to find data behind key Business Concepts o Manufacturing o Shipping to Warehouse o Customer Orders o Bill of Materials o Invoicing o Payments o Returns o Customer Master o Vendor Master
  • 41. DEMONSTRATION Watch the full recording and demonstration at: http://www.bbbt.us/resources/videos/2015-2/
  • 43. Want to learn more? Visit: www.silwoodtechnology.com Email: info@silwoodtechnology.com Request evaluation copy: http://www.silwoodtechnology.com/safyr-evaluation-licence/ Call: +44 1344 876 553 Watch the full recording and demonstration at: http://www.bbbt.us/resources/videos/2015-2/