如何創建數據管理和治理的協作平臺.pptx

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1、How to Create a Collaborative Platform for Data Management and GovernanceFelicia PerezManaging Director,Information as a Product at National Student Clearinghouse Transitioning to a cloud data ecosystem and Master Data Management Selecting Semarchy as our MDM partner The implementation journey A new

2、 era of data governance and data quality Agenda IntroductionNational Student Clearinghouse made the decision to move its data ecosystem to the cloud.Fundamental drivers for that decision were a need for:improved,targeted matching rules improved data governance and data quality consistent,clear data

3、steward experience Introduction Transitioning to a cloud data ecosystem and Master Data Management Why MDM is key to realizing the benefits of moving to the cloud Why Move to the Cloud?Improved Data QualityImproved Data Governance&VisibilityPoised for modernized,cloud-based data submissionAble to ta

4、ke advantage of advancements in technologyContinual Data Security improvementBetter,quicker,more robust visibility into outcomesWhy Master Data Management?Improved understanding of unique learner population Continuous improvement of matching rules Focus on bringing in ALL learner data traditional an

5、d life long Democratization of our data quality improvement journey through an intuitive User Interface Streamline data use/sharing across all platforms and applicationsWalt DisneyThe way to get started is to quit talking and begin doing.Selecting Semarchy as our MDM Partner Master Data Management T

6、ool Selection CriteriaThe first step in the journey is ensuring that you understand:Enterprise priorities Core data and direct business impacts Business drivers EcosystemBased on this analysis,the selection criteria was:Cloud compatibility(AWS)Ability to handle large volume of learners Ability to id

7、entify unique learners from a large body of learner records Ability to customize matching rules per application Intuitive UI to support data stewards System integration support that“just gets it Pricing The Implementation JourneyHow we got from here to there Implementation TimelineSeptember 2019Deci

8、sion to move to the cloud and implement a Master Data Management solution was made.January 2020MDM Providers vetted and Semarchy selected.March 2020Proof of Value Presentation and decision to move forward with Semarchy.May 2020Training complete and Semarchy implementation begins.January 2021Historic

9、al data fully processed through Semarchy;Golden Records created.Semarchy is Production ready.Opportunities and ChallengesOpportunity:Data Quality Improvement through UnificationOrganic power of all the valuable customer data domains working together Reduces/eliminates siloed data systemsImproves eff

10、iciency to get better data qualityDrives innovation opportunities with ML/AIOpportunity:Clarity on Unique LearnersDrives better insights for the Chief Data Officer Benefit to entire enterprise as the integration tool matures Challenge:Historical DataSemarchy Pre-Sales and Customer Success team partn

11、ered with us every step of the wayData partitioning was the key to successThe More We Know.continuing the journey to irradicate all under/over matches A New Era of Data Governance and Data QualityThe destination was worth the journey!What does success look like?Unique LearnersDistilling millions of

12、education records into an absolute number of unique learnersPoised for the future to better understand our unique learners and provide even more meaningful insightsMatching RulesTime consuming to change matching rules before Semarchy xDM implementationCurrently can be done via SemQL(similar to SQL)1

13、-5 rules can be modified in no more than two sprintsHunger for MoreThe initial fundamental improvement of understanding our data is driving excitement for continued augmentation of our learner dataData Lake,Data Analytics,and more Whats Next?Data Lake&New Data Source EvaluationRealizing potential va

14、lue in third-party data Proving the actual valueIntelligent,reasoned support for the most valuable third-party dataSupporting Enterprise Applications Unique,targeted matching rulesPurpose built for specific applicationsMachine Learning&Continual InnovationAutomated matching and data quality improvement Easier development of classifiers to detect mismatchesRecommendation engine to correct mismatches Thank you!Visit Semarchy at in the Showcase FloorConnect with M.Felicia Perez on LinkedIn or at fperezstudentclearinghouse.org.