为应对零售业革命而制定的以数据为中心的基础设施蓝图

DATA EXCHANGEBLUEPRINT PDx™BLUEPRINT:OPTIMIZINGRETAILDATA EXCHANGE INTRODUCTION:Traditional IT architectures within retail organizations are not designed to effectively leverage data, optimize data exchange or address the challenges of data gravity. Data created fromdifferent lines of business is often stored in silos throughout the company. Some elements on premise and some in the cloud. This distribution without intent leads to performance issues. Operational costs andoverall complexity increase. Successful digital transformation requires a data-centric IT infrastructure that localizes data aggregation, staging, analytics, streaming and management in centers of data exchangeat global points of business presence. Current State Fragmented architectures burdened bytechnical debt and driven by pointsolutions lack capabilities and performancerequired for hybrid IT workflows1 Implement distributed data staging/aggregation to optimize data exchangebetween users, things, networks and clouds1 Integrate public/private data sourcesto enable real-time intelligence acrossdistributed workflows3 Siloed data prevents the enablementof analytics and new business modelscentered around data4 Distribute business intelligence capabilities toallow for the creation of new secure B2B dataexchanges that offer competitive advantagesand unlock new growth opportunities4 Deploy regional data lakes/distributed datawarehouses to maintain data performance,compliance and sovereignty2 Cloud connectivity and network notoptimized, causing poor applicationperformance when leveraging cloud toaccess local data SOLUTION STEP 2INTEGRATE PUBLIC/PRIVATEDATA SOURCES STEP 1IMPLEMENT DATA STAGING/AGGREGATION STEP 3HOST DATA AND ANALYTICSADJACENT TO NETWORKINGRESS/EGRESS Distributed DataStaging/Aggregation Integrated Public and Private Data Sources 4New Business Opportunities Unlocked ACTIONDirectly interconnect cloud on-ramps to centers of data ACTIONImplement a cohesive data storage strategy at centers of ACTIONDistribute business intelligence and connect global data storage ecosystems data exchange +Add processing, analytics and streaming capability at global points ofbusiness presence+Host a B2B meeting place for organizations to collaborate and connecttheir business platforms +Deploy regional data lakes and distributed data warehouses atcenters of data exchange+Solve global coverage and capacity needs +Enable performant data exchange between sources and destinations+Operate deployments as a seamless extension of global infrastructurewith consistent experience, security and resiliency OUTCOME OUTCOME OUTCOME +Enable real-time intelligence across distributed workflows locallyand globally +Optimize data exchange between users, things, networks and clouds +Localized data improves application performance and user experience+Maintain compliance and data sovereignty STEP 1:IMPLEMENT DATA STAGING/AGGREGATION +Deploy regional data lakes and distributed data warehouses atcenters of data exchange+Solve global coverage and capacity needs 1.Deploy centers of data staging in key locations2.Data Lakes store raw data to be analyzed and curated by data scientists3.Refined data sits in the data warehouse for business professionals to use4.Due to the value and sensitivity of enterprise data, access needs to be strictly controlled and logged OUTCOME +Localized data improves application performance and user experience+Maintain compliance and data sovereignty STEP 2:INTEGRATE PUBLIC/PRIVATE DATA SOURCES +Enable performant data exchange between sources and destinations+Operate deployments as a seamless extension of global infrastructurewith consistent experience, security and resiliency 1.The Core Switching Infrastructure terminates connectivity into the Data Hub and enables access to the cloud and other datasources by direct high-performance interconnection2.Additional connectivity is provided by use of software-defined on-ramps such as Service Exchange™3.Other data sources can be cloud storage, IaaS environments, SaaS environments or other remote Data Hubs OUTCOME +Optimize data exchange between users, things, networks and clouds STEP 3:HOST DATA AND ANALYTICS ADJACENTTO NETWORK INGRESS/EGRESS +Add processing, analytics and streaming capability at global points ofbusiness presence+Host a B2B meeting place for organizations to collaborate and connecttheir business platforms 1.GPU Farm is located directly adjacent to data stores for direct access to enable AI Development and workloads2.Bulk Compute Farm is for media content creation, complex modeling and simulations OUTCOME +Enable real-time intelligence across distributed workflows locallyand globally TARGET STATE ARCHITECTURE Summary A purpose built data-centric architecture to optimize retail data exchangereduces security risks, lowers costs with the reduction of bandwidth andduplicated infrastructure, and contributes to revenue growth throughunbounded data analytic p