Data Integrity Making Your Data More Accessible but Less Vulnerable

Introduction: About Me

•  Senior Manager, Accenture •  Expert in Business Analytics and Data

Management •  20 years of Client Project experience in

Systems Integration, Operations •  Industry experience in Financial Services,

Government, Healthcare, High Technology, Manufacturing, Retail, Telecommunications

About Accenture

•  URL: http://www.accenture.com •  Net Revenues: US$13.67 billion for fiscal 2004 (12 mos. ended

Aug. 31, 2004) •  Exchange/Ticker: NYSE/ACN •  Employees: Over 110,000 •  Global Reach: Over 110 offices in 48 countries •  Clients: Approximately 2,500, including 84 of the Fortune

Global 100, two-thirds of the Fortune Global 500, and government agencies in 26 countries.

•  Ranked #1 in Customer Relationship Management and at the top of the Supply Chain Management and Business Consulting rankings

What To Expect

•  An overview of Data Integrity challenges •  Successful approaches to Data Integrity •  Observations and “Lesson’s Learned”

Google “define:data integrity”

•  A condition in which data has not been altered or destroyed in an unauthorized manner. (http://www.nrc.gov/site-help/eie/terms_id.html)

•  The state that exists when computerized information is predictably related to its source and has been subjected to only those processes which have been authorized by the appropriate personnel. www.utmb.edu/is/security/glossary.htm

•  (IEEE) The degree to which a collection of data is complete, consistent, and accurate. Syn: data quality.

Data Integrity In The News

“Poor record-keeping and the mixing of one day’s leftover hamburger into the next day’s production” created E.coli bacteria contamination that caused several illness and the food services company to lose their largest client. Source: New York Times

“Database giant ChoicePoint said late Wednesday that 145,000 consumers nationwide were placed at risk by a recent data theft at the company. Previously, the company had suggested the theft only affected California residents.” Source: MSNBC, Feb. 18, 2005

Rapid Explosion Of Digital Data

•  Structured Data –  To & From Databases

•  Unstructured Data –  Text –  Spreadsheets –  Audio –  Video –  Images

Emerging Data Challenges

•  Digital Rights Management (DRM) •  Biometrics •  Compression techniques •  Encryption techniques (e.g., steganography) •  Competing Metadata standards •  Proliferation of unique, nonstandard data

definitions and structures for XML tags

Counting The Costs

What is the estimated cost of customer data integrity problems on American businesses?

a) $200 billion b) $400 billion c) $600 billion d) $800 billion

Customer Data Problems

“Poor quality customer data costs U.S. businesses an estimated $611 billion per year in postage, printing, and staff overhead….”

— “Taking Out the (Data) Garbage,” The Data Warehousing Institute, 4/2/2003

Many Sources Of Customer Data •  Pervasive collection of electronic data

–  Credit Cards, ATMs, Debit –  Phone Records, Check Scans

•  Exchange of electronic data –  Insurance, medical diagnosis, employment

verification, tax collections •  Archived electronic records

–  Credit History, Criminal Records, Property Transactions, Licenses & Permits

Example

A major bank had 251 different customer files that it had to analyze just to answer the question, “Who is our best customer?”

Customer Data Variations

Life Event Changes Data Entry Errors Identity Variations

One Life . . . . . . Multiple Identities

Customer Data Integrity Tools

Capabilities Description Analysis Automated Data Assessment "

CRM

Cleansing Data Extract, Reengineering, Transformation and/or Cleansing " " " "

Cleansing Name & Address

Cleanses and enriches name and address data " " " " "

Metadata Quality

Provides quality assessment or metadata management "

Data Defect Prevention

Prevents data errors in source applications that create and update data " "

Rule Discovery

Analyzes and discovers rule in business data " " " "

Service Provider

Provides the quality services or has third party suppliers who use their products. " " "

Other Technologies

•  Network and Virus Protection •  Authentication and Encryption •  Highly-Available Storage •  Reliable Network Infrastructure •  Secure Remote Access •  Monitoring and Audit •  Data Profiling and Cleansing

But, wait…

•  Are tools the complete solution? •  Are there deeper problems? •  What risks are hidden below the surface?

1994 Statistic: only 16% of IT projects are successful

2004 Statistic: only 34% of IT projects are successful

Lesson Learned: Management

•  Data Management results in Data Integrity •  “Management” means

– Accountable Decision-Makers – Defining what the data means – Measuring results – Changing to improve

Key Success Factors

1.  Defining the business need 2.  Integrating business and

technology professionals 3.  Securing sponsorship 4.  Taking an iterative

approach 5.  Using proven

communication principles

Defining The Business Need

Integrating Business & Technology

•  Business users own the data – Requirements for data quality – Content – Definitions

•  IT group designs and builds – Ensures technology meets requirements – Provides technical expertise

Sponsorship is crucial

•  Lack of executive sponsorship is #1 reason projects fail

•  Sponsors provide: – Budget – Business content & context –  Influence – Decision authority

Iterative Approach

•  “Big Bang” projects often fail •  Break projects into short-duration releases

– Set clear objectives – Define “success” in business value

•  Incorporate “lessons learned” in next releases

Communication

•  Managing expectations of key stakeholders –  Use status meetings and

reports –  Project and budget

tracking •  Marketing the project’s

benefit •  Ensuring that key users will

provide their input

Parting Advice

•  Plan for constant change –  Technology –  Executive Leadership

•  There are no “silver bullets” –  Not one cause, not one solution

•  Process is easier to duplicate than talent –  Incremental improvement is the norm

•  Plans, Actions/Decisions, and Metrics –  You have to be moving to steer

Questions?

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References

•  Robb, Drew. Taking Out the (Data) Garbage, 4/2/2003. http://www.tdwi.org/Publications/display.aspx?id=6626&t=y

•  Agosta, Lou. The Essential Guide to Data Warehousing

•  English, Larry P. Improving Data Warehouse and Business Information Quality

•  Mitnick, Kevin D. The Art of Deception : Controlling the Human Element of Security

•  Stoll, Clifford. The Cuckoo's Egg: Tracking a Spy Through the Maze of Computer Espionage