Скачать презентацию Info Sphere Information Server Trends Tactics for Скачать презентацию Info Sphere Information Server Trends Tactics for

bc4bcd97640dffc6afb1cbb538934d6f.ppt

  • Количество слайдов: 22

Info. Sphere Information Server: Trends & Tactics for Improving Data Quality of your Business Info. Sphere Information Server: Trends & Tactics for Improving Data Quality of your Business Intelligence Solution

Session Abstract è Many organizations struggle with broader user adoption of Business Intelligence and Session Abstract è Many organizations struggle with broader user adoption of Business Intelligence and Performance Management due to a lack of trust in their data, and the inability to deliver the breadth, speed and consolidated information perspective necessary to keep pace with the business. è This "how-to" session will discuss how to enhance your existing and planned Cognos initiatives by addressing the need for on-time delivery of trusted information. è Specifically, learn how to leverage the IBM Info. Sphere product portfolio, as a foundation for Cognos 8 BI, to immediately address your data quality; real-time information integration and data warehousing challenges to drive more business value. 2

Performance Management Challenges Faced Business Challenge How to address the diverse needs of everyone Performance Management Challenges Faced Business Challenge How to address the diverse needs of everyone in the business with a complete, consistent view of information? Information Challenge How to deliver: quality information from fragmented, disparate systems at volume and velocity required by the business? Process Challenge How to establish standards, governance, and breakdown barriers to establish an IT-business partnership 3

Increasing Focus on Data Quality è Businesses are beginning to realize that data quality Increasing Focus on Data Quality è Businesses are beginning to realize that data quality issues not only cost them time and money, but also inhibit their ability to address core strategic projects è More and more businesses are establishing programs for data quality, to measure and improve the reliability of information è Analysts contend that companies with focused data quality programs will find more opportunities to outperform their peers 4

Why Does this Problem Exist? è Most enterprises are running distinct sales, services, marketing, Why Does this Problem Exist? è Most enterprises are running distinct sales, services, marketing, manufacturing and financial applications, each with it’s own “master” reference data. è No one system is the universally agreed-to system of record. è Enterprise Application Vendors do not guarantee a complete & accurate integrated view – they point to their dependence on the quality of the raw input data è Data quality continues to erode at the point of entry, though it is not a data entry problem 5

Business Drivers for Investment Depend on Data Quality è Empowering risk and compliance initiatives Business Drivers for Investment Depend on Data Quality è Empowering risk and compliance initiatives with the information they require è Optimizing Revenue Opportunities by ensuring effective and efficient interactions with customers, partners, and suppliers è Enabling collaborative business processes with consistent and trustworthy information è Reducing the total cost of ownership for maintaining consistent information across the enterprise 6

What is the Impact of Poor Data Quality? Lost Sales Opportunity “Hard” Losses § What is the Impact of Poor Data Quality? Lost Sales Opportunity “Hard” Losses § SKU misplaced or hard to find § Out of stocks attributed to the store 1. 5% 1. 7% “Soft” Losses § Lost potential for cross-sell and up-sell (staff not trained or available) § Reduced store visit frequency § Abandoned carts (poor service or excessive queues) Total 2 -4% 1 -3% 1 -2% 7. 2%- 12% Source: GMA/FMI/CIES 2003 (US grocery), ECR Europe 2003, Lineraires. com, California Management Review, IBM case studies, interviews and IBM Institute for Business Value analysis 7

Data Quality is a Subjective Business Standard èData = facts used as a basis Data Quality is a Subjective Business Standard èData = facts used as a basis for decision making suitable for storage on a computer èQuality = the general standard or grade of something Data Quality = a subjective standard used to determine if a set of facts is suitable for a particular business purpose Business Purpose Relevant? Accurate? Valid? Complete? Ultimately, Data Quality = Trust 8

So, What Constitutes Data Quality? èData is standardized èData is fit for purpose (conforms So, What Constitutes Data Quality? èData is standardized èData is fit for purpose (conforms to rules) èEach record is unique èView of information is complete èRecords are certified against authoritative sources èLineage is understood èData quality is measured over time 9

What Do You Need to Establish a Data Quality Program? èA foundation platform that What Do You Need to Establish a Data Quality Program? èA foundation platform that centralizes quality rules and provides auditable data quality èBusiness-driven, data-centric design environment for data quality rules èAn ongoing process for data quality èA way to measure quality over time èUniversal deployment of quality rules across all points of entry èData quality ownership and data governance èManagement sponsorship and a corporate mandate for data quality improvement 10

Common Data Problems è Lack of information standards - different formats & structures across Common Data Problems è Lack of information standards - different formats & structures across different systems è Data surprises in individual fields - data misplaced in the database Kate A. Roberts 416 Columbus Ave #2, Boston, Mass 02116 Catherine Roberts Four sixteen Columbus APT 2, Boston, MA 02116 Mrs. K. Roberts 416 Columbus Suite #2, Suffolk County 02116 Name Tax ID Telephone J Smith DBA Lime Cons. Williams & Co. C/O Bill 1 st Natl Provident HP 15 State St. 228 -02 -1975 025 -37 -1888 34 -2671434 508 -466 -1200 6173380300 415 -392 -2000 3380321 Orlando WING ASSY DRILL 4 HOLE USE 5 J 868 A HEXBOLT 1/4 INCH è Information buried in freeform fields WING ASSEMBY, USE 5 J 868 -A HEX BOLT. 25” - DRILL FOUR HOLES USE 4 5 J 868 A BOLTS (HEX. 25) - DRILL HOLES FOR EA ON WING ASSEM RUDER, TAP 6 WHOLES, SECURE W/KL 2301 RIVETS (10 CM) è Data myopia - lack of consistent identifiers inhibit a single view è The redundancy nightmare duplicate records with a lack of standards 11 19 -84 -103 RS 232 Cable 6' M-F Cand. S CS-89641 6 ft. Cable Male-F, RS 232 #87951 C&SUCH 6 Male/Female 25 PIN 6 Foot Cable 90328574 90328575 90238495 90233479 90233489 90345672 IBM I. B. M. Inc. Int. Bus. Machines International Bus. M. Inter-Nation Consults I. B. Manufacturing 187 N. Pk. Str. Salem NH 01456 187 N. Pk. St. Salem NH 01456 187 No. Park St Salem NH 04156 187 Park Ave Salem NH 04156 15 Main Street Andover MA 02341 Park Blvd. Bostno MA 04106

A Platform for Data Quality 12 A Platform for Data Quality 12

A Process For Data Quality Establish Data Quality Ownership & Sponsorship Understanding Data Quality A Process For Data Quality Establish Data Quality Ownership & Sponsorship Understanding Data Quality Analyze Source Data Measure & Baseline Data Quality Standardize Certify & Enrich Enforcing Data Quality Standards Match Link or Survive Re-Measure Report 13 Monitoring Data Quality

Data Quality Capabilities Understanding and Monitoring Data Quality è Analyzes data structure, Quality Controls Data Quality Capabilities Understanding and Monitoring Data Quality è Analyzes data structure, Quality Controls for Completeness and Validity of data values è Incomplete or Invalid values set by value, range, or reference sources è Consistency checks for data formats 14 Enforcing Data Quality Standards è Removes duplicates è Cross-references matching records è Survives a single complete record è Cleanses and enriches data

Understanding Data Quality: Data Quality Assessment Methodology è Define clear business problem statement • Understanding Data Quality: Data Quality Assessment Methodology è Define clear business problem statement • Increase revenue by cross selling more effectively our services to all clients • Reduce materials costs by negotiating better prices from our suppliers • Reduce parts inventory across our manufacturing plants • Reduce IT costs and improve service levels by consolidating overlapping applications è Over 5 days, our technical experts analyze data that supports your business problem statement • IBM and customer map issues to relevant data samples • Agree scope of measures and customer provides data sample: e. g. , 4 or 5 key tables and 5 -10 key columns è IBM analyzes the data • • Compliance with business formats • Variation in standards • Range and outliers • 15 Column usage and completeness Incidence of duplicates Business Subject Matter Expert Data Quality Analysis Info. Sphere Information Analyzer Data Steward

Understanding Data Quality: Assessment Outcomes è Management report and presentation of findings • Identify Understanding Data Quality: Assessment Outcomes è Management report and presentation of findings • Identify Performance Management project exposures • Optional follow-on workshops • Regulatory exposures è Data Discovery • Quantitative results • Data completeness and format issues • Business rule compliance è Data Quality Baseline • The DQA sets a shared baseline platform for an ongoing data quality improvement initiative (data governance) or tactical remedial project è Case Study: Pharmaceutical company è The Tipping Point – unable to get a consolidated view of data. Report accuracy was suspect. è The Hurdle – marketing and sales data warehouse contained many data quality issues è The Result – using IBM Info. Sphere Information Analyzer and IBM Info. Sphere Quality. Stage they reduced development time and their reports now support better targeted marketing

Enforcing Data Quality Standards: Investigation 123 St. Virginia St. Parsing: 123 | St. | Enforcing Data Quality Standards: Investigation 123 St. Virginia St. Parsing: 123 | St. | Virginia | St. Separating multi-valued fields into individual pieces Number Lexical analysis: Street Type Alpha Street Type 123 | St. | Virginia | St. Determining business significance of individual pieces Context Sensitive: House Number Street Name Street Type 123 | St. Virginia | St. Identifying various data structures and content “The instructions for handling the data are inherent within the data itself. ” 17

Enforcing Data Quality Standards: Standardization Input File: Address Line 1 Address Line 2 639 Enforcing Data Quality Standards: Standardization Input File: Address Line 1 Address Line 2 639 N MILLS AVENUE 306 W MAIN STR, CUMMING, GA 30130 3142 WEST CENTRAL AV 843 HEARD AVE 1139 GREENE ST ACCT #1234 4275 OWENS ROAD SUITE 536 EVANS ORLANDO, FLA 32803 TOLEDO OH 43606 AUGUSTA-GA-30904 AUGUSTA GEORGIA 30901 GA 30809 Result File: House # Dir Str. Name Type Unit No. 639 306 3142 843 1139 4275 N W W MILLS MAIN CENTRAL HEARD GREENE OWENS AVE ST RD STE 536 NYSIIS City SOUNDEX State Zip MAL MAN CANTRAL HAD GRAN ON ORLANDO CUMMING TOLEDO AUGUSTA EVANS O 645 C 552 T 430 A 223 E 152 FL GA OH GA GA GA 32803 30130 43606 30904 30901 1234 30809 Results in strongly “typed” fixed fielded standardized data 18 ACCT#

Enforcing Data Quality Standards: Matching è Clerical review ? è Record linkage Cross-reference è Enforcing Data Quality Standards: Matching è Clerical review ? è Record linkage Cross-reference è Survivorship è Append/Fix sources 19 =

Lessons Learned and Best Practice è Recruit an executive sponsor • Signals that the Lessons Learned and Best Practice è Recruit an executive sponsor • Signals that the initiative is important • Assures that funds continue to be available • Discourages other business units from implementing conflicting projects è Convene a data quality working group • Assess and report on quality early in the process • May coincide with implementation teams or data warehousing teams • Business leads, but IT coordinates and facilitates • Strive for consensus è Have the business appoint a data quality steward for each business unit • 20 For business units with large user populations, several stewards are appropriate

Summary è Data quality is becoming an increasingly important organizational issue è Improving data Summary è Data quality is becoming an increasingly important organizational issue è Improving data quality and ensuring information delivery requires a focused programmatic and varied approach è At the core of any data quality program is a platform capable of providing auditable data quality assessment services è IBM Info. Sphere Information Server, Info. Sphere Warehouse and Cognos 8 BI delivers informational understanding, ownership and trust 21

How Can IBM Help? è Comprehensive platform for data quality assessment, cleansing and on-going How Can IBM Help? è Comprehensive platform for data quality assessment, cleansing and on-going monitoring è Experience and repeatable process for helping organizations set up data quality programs è Domain and industry-specific expertise in establishing repeatable data quality services è Data quality assessment offering to report on existing data quality and establish the business value of a data quality program è Stop by the “Solution Center” for demos of Info. Sphere with Cognos 8 BI integration è Contact your Cognos or IBM Info. Sphere representative for more information, or visit: www. ibm. com/infosphere èThank you for your time © Copyright IBM Corporation 2008 All rights reserved. The information contained in these materials is provided for informational purposes only, and is provided AS IS without warranty of any kind, express or implied. IBM shall not be responsible for any damages arising out of the use of, or otherwise related to, these materials. Nothing contained in these materials is intended to, nor shall have the effect of, creating any warranties or representations from IBM or its suppliers or licensors, or altering the terms and conditions of the applicable license agreement governing the use of IBM software. References in these materials to IBM products, programs, or services do not imply that they will be available in all countries in which IBM operates. Product release dates and/or capabilities referenced in these materials may change at any time at IBM’s sole discretion based on market opportunities or other factors, and are not intended to be a commitment to future product or feature availability in any way. IBM, the IBM logo, Cognos, the Cognos logo, and other IBM products and services are trademarks of the International Business Machines Corporation, in the United States, other countries or both. Other company, product, or service names may be trademarks or service marks of others.