Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Requirements
â˘Als PPTX, PDF herunterladenâ˘
3 gefällt mirâ˘27,048 views
In the U.S., pharmaceutical firms must meet electronic record-keeping regulations set by the Food and Drug Administration (FDA). The regulation is Title 21 CFR Part 11, commonly known as Part 11. Part 11 requires regulated firms to implement controls for software and systems involved in processing many forms of data as part of business operations and product development. Enterprise data warehouses are used by the pharmaceutical and medical device industries for storing data covered by Part 11. QuerySurge, the only test tool designed specifically for automating the testing of data warehouses and the ETL process, is the market leader in testing data warehouses used by Part 11-governed companies. For more on QuerySurge and Pharma, please visit http://www.querysurge.com/solutions/pharmaceutical-industry
Empfohlen
Empfohlen
Weitere ähnliche Inhalte
Was ist angesagt?
Was ist angesagt? (20)
Andere mochten auch
Andere mochten auch (6)
Ăhnlich wie Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Requirements
Ăhnlich wie Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Requirements (20)
Mehr von RTTS
Mehr von RTTS (14)
KĂźrzlich hochgeladen
KĂźrzlich hochgeladen (20)
Data Warehousing in Pharma: How to Find Bad Data while Meeting Regulatory Requirements
- 1. 1 Presenter Bill Hayduk Founder / President Presenter Jeff Bocarsly, Ph.D. Senior Architect built bybuilt by QuerySurgeâ˘
- 2. built by The average organization loses $8.2 million annually through poor Data Quality. - Gartner 46% of companies cite Data Quality as a barrier for adopting Business Intelligence products. - InformationWeek The cost per patient data of Phase 3 clinical studies of new pharmaceuticals exceeds $26,000. - Journal of Clinical Research Best Practices built by QuerySurgeâ˘
- 3. Pharmaâs 2 Largest Data Warehousing Concerns built by QuerySurgeâ˘
- 4. Pharmaâs Largest Data Warehouse Concerns (1) Data Integrity (2) Compliance built by QuerySurgeâ˘
- 5. (1) Data Integrity high risk of defects that are not readily visible Missing Data Truncation of Data Data Type Mismatch Null Translation errors Incorrect Type Translation Misplaced Data Extra Records Transformation Logic Errors/Holes Simple/Small Errors Sequence Generator errors Undocumented Requirements Not Enough Records built by Pharmaâs Data Warehouse Concerns QuerySurgeâ˘
- 6. Pharmaâs Data Warehouse Concerns (2) Compliance Need to comply with Part 11 mandates historical test information test version history test execution data: who, what & when test cycle information visibility of assets archived test results built by QuerySurgeâ˘
- 7. Why is this Important? ď Periodic data reporting to FDA ď Periodic data reporting to intâl bodies (1) Data Integrity (2) Compliance ď FDA announced audits ď Unannounced FDA audits Consequences Severe financial and business built by QuerySurgeâ˘
- 8. Pharmaâs Testing & Reporting Needs built by QuerySurgeâ˘
- 9. ď automate the manual testing of data ď compare millions of rows of data quickly ď flag mismatches and inconsistencies in data sets ď provide flexibility in scheduling test runs ď generate informative reports that can easily be shared with the team ď validate up to 100% of all of all data, mitigating the risk Data Integrity needs Need a testing solution that can⌠built by QuerySurgeâ˘
- 10. Part 11 Reporting needs ď track test history ď provide reporting on test version history ď record all test execution by testing ownerâs name and date ď deliver auditable reports of test cycles ď store all test outcomes and test data ď offer a read-only user for reviewing test assets ď support archiving of results Need a testing solution that can⌠built by QuerySurgeâ˘
- 11. built by The solution⌠built by QuerySurgeâ˘
- 12. What is QuerySurgeâ˘? the collaborative Data Testing solution that finds bad data & provides a holistic view of your dataâs health built by QuerySurgeâ˘
- 13. ⢠Reduce your costs & risks ⢠Improve your data quality ⢠Accelerate your testing cycles ⢠Share information with your team with QuerySurge⢠you can: built by QuerySurge⢠⢠Provides huge ROI (i.e. 1,300%)* *based on clientâs calculation of Return on Investment
- 14. Finding Bad Data SQL HQL SQL HQL SQL SQL ď QS pulls data from data sources ď QS pulls data from target data store ď QS compares data quickly ď QS generates reports, audit trails How? Reports, Data Health Dashboard built by QuerySurgeâ˘
- 15. QuerySurge⢠Architecture Web-based⌠Installs on... Linux Connects to⌠âŚor any other JDBC compliant data source built by QuerySurge⢠QuerySurge Controller QuerySurge Server QuerySurge Agents Flat Files
- 16. Collaboration Testers - functional testing - regression testing - result analysis Developers / DBAs - unit testing - result analysis Data Analysts - review, analyze data - verify mapping failures Operations teams - monitoring - result analysis Managers - oversight - result analysis Share information on the built by QuerySurgeâ˘
- 17. the QuerySurge advantage built by QuerySurge⢠Automate the entire testing cycle ď Automate kickoff, tests, comparison, auto-emailed results Create Tests easily with no SQL programming ď ensures minimal time & effort to create tests / obtain results Test across different platforms ď data warehouse, Hadoop, NoSQL, database, flat file, XML Collaborate with team ď Data Health dashboard, shared tests & auto-emailed reports Verify more data & do it quickly ď verifies up to 100% of all data up to 1,000 x faster Integrate for Continuous Delivery ď Integrates with most Build, ETL & QA management software
- 18. built by QuerySurge⢠QuerySurge⢠Modules Design Tests SchedulingDeep-Dive Reporting Run Dashboard Query WizardsData Health Dashboard
- 19. Fast and Easy. No programming needed. built by QuerySurge⢠QuerySurge⢠Modules Compare by Table, Column & Row ⢠Perform 80% of all data tests â˘Automatically generates SQL code ⢠Opens up testing to novice & non- technical team members ⢠Speeds up testing for skilled SQL coders ⢠provides a huge Return-On-Investment
- 20. built by QuerySurge⢠QuerySurge⢠Modules 3 Types of Data Comparison Wizards: The also provide you with automated features for: o filtering (âWhereâ clause) and o sorting (âOrder Byâ clause) Column-Level Comparison: This is great for Big Data stores and Data Warehouses where tables will have some columns containing transformations and some columns with no transformations. Many tables and columns can be compared simultaneously and quickly. Table-Level Comparison: This comparator is great for Data Migrations and Database Upgrades with no transformations at all. Many tables can be compared simultaneously and quickly. Row Count Comparison: Great for all - Big Data stores, Data Warehouses, Data Migrations and Database Upgrades. Many tables and rows can be compared simultaneously and quickly.
- 21. Design Library ď Create custom Query Pairs (source & target SQLs) ď Great for team members skilled with SQL QuerySurge⢠Modules Scheduling ď Build groups of Query Pairs ď Schedule Test Runs for: ⢠immediately ⢠at a specific date/time ⢠automatically after build or ETL process built by QuerySurgeâ˘
- 22. Deep-Dive Reporting ď Examine and automatically email test results Run Dashboard ď View real-time execution ď Analyze real-time results QuerySurge⢠Modules built by QuerySurgeâ˘
- 23. built by QuerySurge⢠⢠view data reliability & pass rate ⢠add, move, filter, zoom-in on any data widget & underlying data ⢠verify build success or failure
- 24. Test Management Connectors built by QuerySurgeâ˘ ďź Drive QuerySurge execution from your Test Management Solution ďź Outcome results (Pass/Fail/etc.) are returned from QuerySurge to your Test Management Solution ďź Results are linked in your Test Management Solution so that you can click directly into detailed QuerySurge results ⢠HP ALM (Quality Center) ⢠Microsoft Team Foundation Server ⢠IBM Rational Quality Manager Integration with leading Test Management Solutions
- 25. Case Study Fortune 500 firm: Clinical Trial Data built by QuerySurgeâ˘
- 26. Case Study: Fortune 500 Pharma Challenge How can a Data Warehouse team assure data integrity over multiple builds when the cost per patient data of Phase 3 clinical studies exceeds $26,000 and volume of live case data is > 1 TB? Strategy Implement QuerySurge⢠to dramatically increase coverage of data that is verified for each build. Implementation ⢠1,000 SQL queries written to compare case data from the source systems to the DWH after ETL. ⢠QuerySurgeâ˘automated the scheduling, test runs, comparisons and reporting for each build. built by QuerySurgeâ˘
- 27. Metrics ďź 500 mappings ďź 2.5 million data items ďź 1.25 billion verifications ďź Complete run finished in 7 days ďź 45% of data was covered. ďź 14 builds were deployed ďź 115 defects were discovered and remediated Case Study: Fortune 500 Pharma Benefits ⢠10-fold increase in the speed of testing. ⢠Huge increase in coverage of data (from less than 1/10 % to 45%) ⢠Production defects discovered that were missed in previous cycles ⢠Huge savings on clean records (115 defects x $26,000/record) ⢠A huge time savings (3.6 years x 10 people) ⢠Avoidance of lawsuits and FDA fines built by QuerySurgeâ˘
- 28. (1) a Trial in the Cloud of QuerySurge, including self-learning tutorial that works with sample data for 3 days or (2) a Downloaded Trial of QuerySurge, including self-learning tutorial with sample data or your data for 15 days or (3) a Proof of Concept of QuerySurge, including a kickoff & setup meeting and weekly meetings with our team of experts for 30 days http://www.querysurge.com/compare-trial-optionsfor more information, Go here TRIAL IN THE CLOUD built by QuerySurge⢠Free Trials
- 29. built by QuerySurge⢠QuerySurge For more on the Pharma & QuerySurge, go to www.querysurge.com/solutions/pharmaceutical-industry
Hinweis der Redaktion
- Other Pharmaceutical Industry Complexities ------------------------------------------------------------------------ Industry consolidation causing massive integration of data FDA CFR Part 11 compliance A broad variety of data types and sources may be fed into a data warehouse. general Pharma-specific information exchange formats (e.g., HL7 feeds, CDISC feeds, other XML grammars) multiple proprietary and internal data formats, which may have been acquired in the process of industry consolidation.
- QuerySurge can automate the comparison of all data from source files and databases through different legs of the ETL process to the target data warehouse. QuerySurge can be scheduled to run immediately, next Monday at 11:00pm or when an event, such as the current ETL process ends. QuerySurge will execute tests that automate the comparison of target data to source data very quickly, comparing millions of rows of data in minutes. On completion of the run, QuerySurge will produce informative summary and detailed reports that can be viewed immediately or shared with the team via the automated email scheduler. QuerySurge will validate 100% of all of your data, providing full coverage and mitigating the risk while providing reports highlighting every data difference, down to the individual character.
- - tracks test history (user, date, each test version) - provides reporting on test version history for convenient auditing - supports tracking of deviations from approved tests - records all test execution owners by name and date - delivers auditable results reporting of test cycles - stores all test outcomes and test data for post-facto review or audit - offers a read-only user type for reviewing test assets - supports off-database archiving of results (for future restore) for effective long-term results data management
- QuerySurge provides insight into the health of your data throughout your organization through BI dashboards and reporting at your fingertips. It is a collaborative tool that allows for distributed use of the tool throughout your organization and provides for a sharable, holistic view of your dataâs health and your organizationâs level of maturity of your data management.
- QuerySurge helps your team coordinate your data quality initiatives while speeding up your development and testing cycles and finding your bad data. Why risk having your team identify trends and develop strategic initiatives when the underlying data is incorrect? QuerySurge reduces this risk.
- QuerySurge finds bad data by natively connecting to: any data source, whether it is any type of database, flat file or xml and can connect to any data target, whether it is a db, file, xml, data warehouse or hadoop implementation. QuerySurge pulls data from the source and the target and compares them very quickly (typically in a few minutes) and then produces reports that show every data difference, even if there are millions of rows and hundreds of columns in the test. These reports can be automatically emailed to your team. You can pick from a multitude of reports or export the results so that you can build your own reports.
- Your distributed team from around the world can use any of these web browsers: Internet Explorer, Chrome, Firefox and Safari. Installs on operating systems: Windows & Linux. QS connects to any JDBC-compliant data source. Even if it is not listed here.
- QuerySurge can utilized by active practitioners such as testers & developers to create and launch tests, or by managers, analysts and operations to view data test results and the overall health of the data. QuerySurge facilitates this by providing 2 types of licenses: (1) full user & (2) participant user. (1) Full User â This type of user has unlimited access to create QueryPairs, Suites, and Scenarios. This user can also schedule and run tests, see results, run and export reports, and export data. Perfect for anyone creating and/or running data tests while performing analysis of results. (2) Participant User â This user cannot create or run tests, but has access to all other information - including viewing all query pairs, results, and reports, receiving email notifications, and exporting test results and reports. Perfect for managers, analysts, architects, DBAs, developers, and operations users who need to know the health of their data.