简化数据处理方法.pdf

1、From Obstacles to OpportunitiesMastering Data Governance for AI SuccessEmma McGrattanChief Technology OfficerThe GenAI MirageImpressive demos mask operational complexityWhat works in a sandbox often fails in the enterprise.Model-centric thinking ignoresthe data ecosystemGovernance,lineage,quality,an

2、d context are afterthoughts.Everyone wants theoutcome,but few investin the foundationModel outputs are only as good as the data feeding them.“Hallucinations”arent just model issuestheyre metadata failuresMissing context leadsto misleading results.Value is promised at the UI layer,but trust is built

3、at the data layerInsight without provenance is just guesswork.The DataGovernance ParadoxSuccessful AI demands governance thats flexible and easy to useGovernance frameworks canbe cumbersome and are often ignored or bypassedData access is restricted in the name of control,but shadow systems emerge in

4、steadGovernance is treated as a one-time project,not a living,evolving processGovernancemust be built-in,not bolted onBuilt into dataproducts,notwrappedaround themPolicyenforcementaligned withbusiness contextAutomated,transparent,andscalable bydefaultEmpowers usersto trust,access,and act withconfide

5、nceGovernanceby DesignTreat datalike a product,Governance like UXGovernancemust be built-in,not bolted onBuilt into dataproducts,notwrappedaround themPolicyenforcementaligned withbusiness contextAutomated,transparent,andscalable bydefaultEmpowers usersto trust,access,and act withconfidenceWhat Good

6、Governance EnablesAI readinessTrustworthy data at scaleFaster time to insightData and analytics self-service with safeguardsData productization and reuseOperational efficiencyData ContractsData ObservabilityData Quality DashboardFederated Data CatalogData Line