1. xBRL US Pacific Rim Workshop
Database and Business Intelligence Workshop
Karen Hsu
Director Product Marketing, Informatica

2. Extend Data Transformation and Exchange The Informatica Economy Enabled Through Data
On-demand Enterprise
(SaaS)
Traditional
Enterprise
Partner Trading
Enterprise (B2B)
SEPA
xBRL
SWIFT
Key Points:
Data is fragmented - everywhere.
Explosion of data sources both inside and outside the firewall.
Need to seamlessly connect and move trusted data around the extended enterprise.
Change is continuous.
Example text:
Today’s information economy is enabled through data. Data is the lifeblood of any corporation, it is the currency of the modern economy and over the last 10 years we have seen an explosion of data across the enterprise.
Within the traditional enterprise (inside a corporation firewall) there are large quantities of data held within operational databases. However, alongside those are enormous quantities of valuable data held within unstructured data sources such as spreadsheets, PDF’s, word processing documents. This traditional environment is changing continually as vendor consolidation occurs alongside mergers & acquisitions and business modernization.
However, alongside this change we are also seeing an explosion taking place outside the firewall as corporations turn to outsourcing and software-as-a-service to augment traditional computing delivery. In addition they are increasing interacting with partners through supply chains or partner networks to facilitate business differentiation. Financial institutions are needing to adhere to SEPA standards, or Healthcare to NAACHA.
This expanded workplace and global immediacy of information is placing huge pressures on IT. Within the traditional enterprise corporations need to integrate data from disparate systems and ensure quality. They also need to integrate outsourced solutions with their mainstream computing solutions in order to facilitate seamless quote to cash processes. A the same time they need to be able to move data seamlessly between partner networks to optimise supply chain processes. Finally every piece of data must be as accurate as possible across the entire enterprise.
Transition:
So how can corporations make sense of all of this and ensure alignment of IT in delivering the trusted information required to enable corporations to compete globally?
On-demand Integration
Enterprise Data Integration
B2B Data Integration
Data Quality Across the Whole Enterprise

3. The Information Platform To Enable Business Imperatives
Improve
Decisions &
Regulatory
Compliance
Modernize
Business &
Reduce IT
Costs
Facilitate
Mergers &
Acquisitions
Customer
Service &
Operational
Efficiency
Outsource
Non-core
Functions
Increase
Partner network
Informatica On Demand
Access
Discover
Cleanse
Integrate
Deliver
Develop + Manage
Audit + Monitor + Report
Data Quality
Data Explorer
PowerCenter – Metadata Manager
PowerExchange
B2B Exchange
Key Points:
We provide a commercially available platform for data integration
It is a platform that empowers your business to deliver on their business imperatives.
Example Text:
Informatica provides the software and services to manage the processes involved in all of the data integration projects that we have discussed. Our platform comprises a number of tightly integrated solutions called Powercentre, Powerexchange, Data Explorer, Data Quality and B2B Exchange. Through our data integration platform organizations can close the data value gap by reducing costs and expediting the time to address data issues of any complexity and scale.
Through our Velocity methodology you have a rapid and repeatable process for analysing, designing and implementing data integration projects
Transition:
I’d like to spend a few moments now looking at some customers who have accomplished key business imperatives with Informatica.
On-demand Integration
Enterprise Data Integration
B2B Data Integration
Data Quality Across the Whole Enterprise

4. Vision: Enterprise Wide Transformation
Payroll
PO Order
Tracking
REPORTING
EDIFACT
CUSTOMER
SWIFT
PARTNER
TRADE PO
Typical Infrastructure Streamlined Processing
Typical
100s of formats, legacy, and industry standards e.g. SWIFT MT and MX, and ISO20022, EDIFACT etc. Implemented multiple times.
Multiple channel types and format combinations. Often format and channel type inextricably linked. Unused combinations don’t work.
Lack of standard implementations formats and channels. Multiple implementations of SFTP usage and SWIFT MT etc.
Some channels etc limited volume capability. Implemented before volume growth.
Streamlined
Single implementation of each format. Easy to link one format to another via common central format.
Format and channels separated so any format can be received/send from any channel
Fewer transformations required to link internal systems using legacy formats.
Standard usage for formats, e.g. SWIFT FIN 50F .vs. 50L (Instructing Party/Ordering Customer) different usages in different systems.
As a result of streamlined…
Costs reduced.
Less complexity and consequent maintenance.
Less infrastructure, machines etc.
Less operational overhead. Single platform to run.
Less work developing single implementations of formats and channels.
Reduced Complexity.
Reduces Time to Market. Complexity slows development and testing.
Improves STP less scope for unforeseen combination in data flows (format and channel combinations) causing failures.
Single Data Processing Hub
All data passed though one hub in consistent formats allows corporate dashboard style analysis allowed fact based business decisions.
100s of formats
Multiple interaction channels
1000s of combinations of formats, protocols, rules between channels
Lack of data and process standardization
Unable to support growth in volume
Reduce processing costs and costs of integrating data across product lines to target new or existing relationships

5. Accelerate Adoption of xBRL
Reduce risk
Leverage existing expertise and infrastructure for lower TCO
Pre-built transformations to industry standards
Best-in-class transformation specification environment
Accelerate xBRL usage from within to outside
Transform any file to and from xBRL
Documents (excel, PDF, word)
Proprietary, COBOL\PL1
Complex positional and delimited files
Industry standard interoperability (standard to standard, version to version, e.g SWIFT MT, MX, BAI)
Ensure quality of data
Manage and monitor data coming from sources and going to destinations
How is this different: xbrl to database…, now that it is in the db there many different tools
Proprietary to SWIFT
SWIFT MT to MX
Documents (excel, PDF)

6. Before: Shenzen Stock Exchange
Issuers
Stock Exchange
Custodian\ Brokers
Flat file
Enterprise actions DB
SFTP
MT 564
Flat file
Flat file
Fax
PDF
Fax
SMTP
Receive “paper” (fax, , pdf, excel, Word)
Process Order Manually
PDF
SMTP
Business user time lost in error analysis
20+ IT persons full time
Flat file
PDF

7. Shenzen Stock Exchange with Informatica
Issuers
Custodian\ Brokers
B2B Data Transformation
Data Quality
Flat file
Enterprise actions DB
MT 564
SFTP
Flat file
Flat file
Fax
PDF
SMTP
SMTP
Web Portal
PDF
B2B Data Exchange
Sources Data Feeds management
Monitoring

8. Automatic extraction of Annual Report PDF to XML canonical format
Semi Annual report

9. Large Credit Rating Agency Accelerate time-to-market of credit ratings
ref123
KEY BUSINESS IMPERATIVE
Accelerate time-to-market of credit ratings
Enable customers to respond quickly to market changes
Replace manual process of copying and pasting from PDF files to analytics application
Replace hand coded excel extraction VB macros
Large Credit
Rating Agency
THE CHALLENGE
B2B ADVANTAGE
RESULTS/BENEFITS
Need to analyze raw data coming from 10,000 different formats every month
40-80 people required per month to extract the data from these formats, delaying the publishing of securities ratings
Best-of-breed transformation for unstructured (PDF) and financial reporting formats
Leverage existing resources familiar with PowerCenter for end to end data integration
Eliminated need for people every month to extract data with up front investment of 60 days
Ability to support any new format or industry standard quickly 9

10. B2B Data Exchange External sources Internal Systems B2B Data Exchange
Monitoring
Partner Management
SWIFT
Data Warehouse
Managed File Transfer
Data Transformation
PowerCenter
What is Data Exchange, and how does it fit?
DX sits between the internal systems and the external partners.
When we look at the components of DX at a very high level, <click to build> we see PowerCenter at it’s core.
How does data typically get delivered from external partners? <click to build> As files.
Since files are involved, what do we need? <click to build> DT to handle the translation and validation.
Why not just sell PowerCenter with Data Transformation? <click to build> Because these exchanges are operational and require tools for management and monitoring – both by operators and by the business.
<click to build>
Finally, since DX is built on the Platform with PowerCenter, customers can take advantage of all other Informatica Platform components – including PowerExchange, IDQ, IIR, MM, Data Masking, etc.
The same technology used for internal system integration is used for multi-enterprise integration.
Adapters
Quality
Identity
External sources
Internal Systems

11. Business and IT Collaboration Step 1: Profile the Data with Informatica Data Explorer
Catalog of Issues
Completeness
Conformity
Consistency
Accuracy
Duplicates
Dependencies
Cleansing specifications
Transformation specifications
Typically a data analyst / Subject Matter Expert profilesthe data. This involves accessing the data and running a series of prebuilt rules using the metadata and data. The user then “surf” the results and tags the data where anomolies exist.
A repository / database of tags are then available for the next stage of the project – which may be a (i) cleansing (ii) data movement project. The next stage of the project is managed by a developer. The developer uses the anomolies to ensure cleansing or transformation rules take into acccount all the issues identified in the data.
Profiles and tags anomalies with Informatica Data Explorer (IDE)
Reviews anomalies with Informatica Data Explorer (IDE)
SME
Developer

12. Data Quality Dimensions Framework
Completeness
What data is missing or unusable?
Conformity
What data is stored in a non-standard format?
Consistency
What data values give conflicting information?
Accuracy
What data is incorrect or out of date?
In order to implement a data quality process, the first step is to define what you mean by Data Quality. A potential data quality dimension framework includes a range of parameters which can be used to identify and categorise data quality issues. So if someone says “our data is not good”, we can now investigate further and describe the levels of data quality with tangible numbers e.g. data quality equals 80% could be an aggregate of completeness of key attributes = 70%, lack of conformity = 75%, duplicates of 20%, etc.
Typically customers take this framework and edit the framework to suit their own company based on maturity and priorities at any point on time. So the framework needs to be customized per organization and agreed on by both the business and IT.
Question: Which of these data quality dimensions describe the problems you have?
Transition:
Lets take an example:
Duplicates
What data records or attributes are repeated?
Integrity
What data is missing or not referenced?

13. Business and IT Collaboration Step 2: Continued
Business and IT Collaboration Step 2: Continued. Establish Metrics and Define Targets
Create scorecards (i) for the initial business case and (ii) as the basis for web based monitoring
Configures and deploys web based data quality reports for monitoring on an ongoing basis – using Informatica Reporting or a 3rd party reporting tool
SME
Developer
Using data quality dimensions, the Subject matter expert then configures DQ rules that generate data quality dimension flags – used to create initial scorecards to help build the business case – but also as a basis for web based reporting – - which is configured and deployed by a developer.

14. Business and IT Collaboration Step 3: Design and Implement Data Quality Rules
Standardize values
Correct missing or inaccurate data
Enrich the data
Identify and consolidate duplicate records
Once the DQ dimension framework has been applied to the data, the priority fields will be identified and the priority data quality rules configured to “fix” or “remedy” the issues. The SME knows the rules and the developer must configure them.
Key points:
- IDQ supports all types of master data i.e. customer/supplier/product/asset/finance – i.e. data quality rules can be applied to any attribute in the cleansing, parsing, standardization and matching processes.
Assists with design of Data Quality rules in Informatica Data Quality (IDQ)
Creates Data Quality rules with Informatica Data Quality (IDQ)
SME
Developer

15. Browser based exception review and manual consolidation process
Exception Management process Step 4: Manage exceptions and consolidate
Data
Quality
Checks
Exception Management
Sources
Target
Records that passed
DQ rules
Cleansing
and
Matching
Rules
High Quality Data
Exceptions
Data
Quality
Assistant
Low
Quality
Data
Here is the High level Process Flow
Step 1 Source is fed into IDQ.
Step 2 IDQ applies Data Quality rules. Data is split into exception records and passed records.
Step 3 Passed records go straight to Target location.
Step 4 Exception records go to staging area accessible by DQA .
This is where Users Manage Bad records or Consolidate suspect duplicates.
Once users are satisfied that the cleansed and consolidated records, the records can then be pushed out to target
Browser based exception review and manual consolidation process

16. Business and IT Collaboration Step 5: Monitor Data Quality Versus Targets
Informatica’s scorecarding and reporting also support monitoring of quality over time
Companies can utilize Informatica’s toolset to perform the monitoring or utilize 3rd party reporting tool
Finally, data quality metrics are measured and tracked over time via browser based reporting and trending. Reports can be configured per user requirements, alerts can be automated and drilldown can be enabled for viewing the low quality records.
Monitors the quality of data to determine if objectives are being met. Uses results to support root cause analysis
Review results to determine if rule enhancement is necessary as part of the automated data quality process
SME
Developer

17. Accelerate Adoption of xBRL
Reduce risk
Leverage existing expertise and infrastructure for lower TCO
Pre-built transformations to industry standards
Best-in-class transformation specification environment
Accelerate xBRL usage from within to outside
Transform any file to and from xBRL
Documents (excel, PDF, word)
Proprietary, COBOL\PL1
Complex positional and delimited files
Industry standard interoperability (standard to standard, version to version, e.g SWIFT MT, MX, BAI)
Ensure quality of data
Manage and monitor data coming from sources and going to destinations
How is this different: xbrl to database…, now that it is in the db there many different tools
Proprietary to SWIFT
SWIFT MT to MX
Documents (excel, PDF)

18. Thank you