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Data Integration to Data Governance


data relationship management Data Integration to Data Governance Data In the News: Market Forces Affecting the Use of Data What are Companies Doing in Response? – PowerPoint PPT presentation

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Title: Data Integration to Data Governance

Data Integration to Data Governance
data relationship management
Data In the News
Data slipupsRick Whiting , 10-May-2006
Inaccurate business data lead to botched
marketing campaigns, failed CRM projects--and
angry customers. A home valued at US121,900
somehow wound up recorded in Porter County's
computer system as being worth a whopping US400
million. Naturally, the figure ended up on
documents used to calculate tax rates. By the
time the blunder was uncovered in February, the
damage was done.
Market Forces Affecting the Use of Data
Privacy Regulations
Data Inaccuracies, Over-billing
Straight-through processing, customer service,
Consumer confidence
Competitive Edge
Customer Pressure
SEC/NAD rule, SARBOX, legal liability, Mergers
Business Governance
What are Companies Doing in Response?
Credit Card Company Where is the Sensitive
  • Business Problem
  • Risk of a security breach exposes potential
    regulatory fines, negative PR and customer
  • Proposed Solution
  • Identify sensitive data flows in structured
    databases so critical data can be consolidated
    and properly secured
  • Roadblock
  • 50 data analysts over 5 years estimate makes
    project appear to be unbounded and infeasible
  • Status
  • Project put on hold

Health Insurance Company Outsourcing
  • Business Problem
  • Data must be sent to India for offshore
    application development.
  • Sensitive data must be masked for HIPAA
  • Proposed Solution
  • Mask sensitive data before sending it outside the
  • Roadblock
  • Sensitive data, where is it?
  • Can two sets of data that individually contain no
    sensitive data be combined to make it sensitive?
  • Status
  • Manual discovery of sensitive data slows
    outsourcing to a crawl

Wall Street Firm Data Consistency will Increase
  • Business Problem
  • Transaction errors are expensive and the risk of
    regulatory fines due to inconsistent reference
    data is unacceptable
  • Proposed Solution
  • Deploy a master data management solution
  • Roadblock
  • 5 years to determine the business rules that
    relate the master data system to legacy systems
  • Unable map two tables to each other after 6 weeks
    of work (70 tables total to map)
  • Status
  • Project on hold

Auto Insurance Migrating Fragile Legacy
Integration Code to Modern Tools
  • Business Problem
  • Business changes force expensive and difficult to
    implement changes in hand written legacy
    integration code
  • Proposed Solution
  • Migrate legacy code to a modern ETL (extract,
    transform, load) tool. Cost of maintenance of
    ETL is a fraction of legacy code
  • Roadblock
  • No one knows the code. The cost of migration is
  • Status
  • Company continues to manually change hand written
    code ad hoc as the business demands

The Common ? in the Project Schedule
  • Data Relationship Discovery
  • You have to know where your data is, how it flows
    and relates across systems if you hope to secure
    it, move it, consolidate, integrate it ...

Dont We Know Our Own Data?
Myth 1 We know our data
Im a professional. Of course I know my data!
  • Subject matter experts (SMEs) only know their own
  • But they cant tell you how it changes and is
    transformed as it moves from system to system
  • Relationships between systems are complex
  • SMEs sometimes change jobs!

But, once it leaves my hands, it is someone
elses problem!
Wow, that transformation is complex. Are you
sure that is in my data?
Im going to start my own consulting firm
Myth 2 We know our data
All of my data follows the business rules for
this system!
  • Business rules are broken all the time as data
    crosses business and system boundaries
  • 83 year old man in system A is a youthful
    driver in system B
  • Bond yield is listed as 5 in system X and 5.3
    in system Y
  • Exceptions result in lost revenue, customer
    dissatisfaction, and regulatory fines

Myth 3 We know our data
I cant keep up with all the acquisitions and
reorganizations. They mess up the way systems
work together. It is very inconvenient.
  • Business rules change as organizations change
  • Mergers and Acquisitions
  • New products or services
  • Products/services are retired
  • Reorganizations
  • New IT systems are added

The Reality
  • Companies lack a global view of their corporate
    data map

Current Trend Data Governance
  • What is it?
  • The latest over-hyped term
  • Data Integration is to Tactical as
    Data Governance is to Strategic
  • Definition
  • Data Governance encompasses the people, processes
    and procedures to create a consistent, enterprise
    view of your data in order to
  • Improve data security
  • Increase consistency confidence in decision
  • Decrease the risk of regulatory fines

The Problem with Data Governance
  • How do you do it?
  • Where is the sensitive data?
  • What are the business rules and data
  • Where are the exceptions?
  • How do you ensure a consistent, repeatable

Traditional Proposed Approach Metadata
  • What is it?
  • Another over-hyped term
  • Data about data datatype (character, integer,
    number, date etc), column width, frequency,
    cardinality etc
  • The Problem
  • Single system metadata only
  • Profiling
  • Traditional data integration tools do not
    discover metadata
  • Cleansing, ETL, EAI and EII
  • The Reality
  • Data analysts manually examine data values to
    figure out the data map
  • The most sophisticated tool generally used today

Traditional Data Relationship Discovery Tool
There is a Better Way
The Solution Data-Driven Relationship Discovery
  • New approach to a 40 year old problem
  • Sophisticated heuristics and algorithms analyze
    actual data values
  • Automates the discoveryand validation of
  • Sensitive data flows
  • Business rules
  • Complex transformations
  • between structured data sets in a consistent and
    repeatable manner

Solution Data-Driven Exception Discrepancy
  • Identify exceptions to avoid
  • Regulatory fines
  • Lost revenue
  • Customer dissatisfaction

Data-Driven Discovery Results
  • Credit Card Company
  • Status Project moving forward again
  • Reduced estimated effort from 250 engineering
    years to 25 eng. years
  • Eliminated project feasibility risk
  • Wall Street Firm
  • Status Back on track
  • Over 5x (2 days vs 6 weeks manually) improvement
    in discovery of business rules made MDM project
  • Found bond yield discrepancies
  • Auto Insurance Company
  • Status Predictable affordable migration
  • 80 reduction in effort required to migrate
    hand-code to ETL tool
  • Mapping process discovered potentially costly
    business rule errors
  • Health Insurance Company
  • Status Outsourcing rollout accelerated
  • Now confident in sensitive data discovery
    accuracy and speed
  • Launching new data masking service companywide

Summary Data Governance Strategic Data
  • Companies are implementing data governance
    projects to
  • Improve Security
  • Increase Consistency
  • Decrease Regulatory Risk
  • First step of data governance Discovery
  • Automated data-driven discovery is a consistent,
    repeatable and proven approach to identify
  • Sensitive Data
  • Business Rules
  • Data Exceptions

Key Contacts
  • Bob Shannon U.S. East Coast Sales
  • Phone (203) 878-8472
  • Email
  • Brian Smogard U.S. Central Sales
  • Phone (612) 605-9236
  • Email
  • Clive Harrison U.S. West Coast and International
  • Phone 415-608-4632
  • Email
  • If you have any other follow up questions,
    contact me
  • Todd Goldman
  • Phone (408) 919-0191 ext 1115
  • Email