从 DIY 转向 AI 准备.pdf

1、Move from DIY Move from DIY to ready for AIto ready for AIEvolving your data architecture for scalable successMove from DIY to Move from DIY to ready for AIready for AIFIVETRANFIVETRANThe average organization aggregates data from over 400 sources.While traditional DIY pipelines may offer benefits li

2、ke control and customization,they come at the expense of scalability,reliability and maintenance costs.In our talk,you will discover:The complexity of scaling data movement with DIY data pipelines and the challenges it presents How to better control costs and streamline operations using a managed,au

3、tomated data pipeline platform The benefits of leveraging a modern,interoperable platform to enhance performance for advanced analytics and AI initiativesChris RudolphChris RudolphLead Sales Engineer,Enterprise2In the expectations of data teams in the industry and by the business3In the complexity o

4、f the problems for data teams to solve for the business4Data practitioners will shape how GenAI is deployed in the enterprise.Source:MIT Report,2024Proportion of data scientist TIME SPENT TIME SPENT PREPARING DATAPREPARING DATA on averageHow time is spent on AI projectsHow time is spent on AI projec

5、ts567%33%Proportion of data scientist TIME SPENT TIME SPENT BUILDING AI MODELSBUILDING AI MODELS on averageSource:Vanson Bourne,2024How often data pipelines have to be rebuilt How often data pipelines have to be rebuilt after being deployedafter being deployed641%41%of data leaders report that they

6、rebuild data pipelines sometimes.18%18%of data leaders report that they rarely rebuild data pipelines39%39%of data leaders report that they constantly rebuild data pipelines2%2%of data leaders report that they never rebuild data pipelinesSource:Wakefield Research,20217The DIY data pipeline icebergTh