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Lessons In Data Integrity



Presented at the CDW Technology Conference

Presented at the CDW Technology Conference



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Lessons In Data Integrity Lessons In Data Integrity Presentation Transcript

  • Data IntegrityMaking 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 “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 “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
  • 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 CostsWhat is the estimated cost of customer dataintegrity problems on American businesses?a) $200 billionb) $400 billionc) $600 billiond) $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 CustomerData•  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
  • ExampleA major bank had 251 different customer filesthat it had to analyze just to answer thequestion,“Who is our bestcustomer?”
  • Customer Data VariationsOne Life . . . . . . Multiple Identities Life Event Changes Data Entry Errors Identity Variations
  • Customer Data Integrity ToolsCapabilities DescriptionAnalysis Automated Data Assessment "  CRMCleansing Data Extract, Reengineering, Transformation and/or Cleansing " " " "Cleansing Cleanses and enriches name andName & address data " " " " "AddressMetadata Provides quality assessment or metadataQuality management "Data Defect Prevents data errors in sourcePrevention applications that create and update data " "Rule Analyzes and discovers rule in businessDiscovery data " " " "Service Provides the quality services or has thirdProvider 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 Factors1.  Defining the business need2.  Integrating business and technology professionals3.  Securing sponsorship4.  Taking an iterative approach5.  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? ?
  • 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 Cuckoos Egg: Tracking a Spy Through the Maze of Computer Espionage