请选择 进入手机版 | 继续访问电脑版
用户
 找回密码
 立即注册
搜索
启微科技 首页 资讯 查看内容

The Forrester Wave™: Master Data Management, Q1 2016

qiwei 2017-3-3 08:40

the MDM Market is Expanding to support context And insightover a third of vendor customers we interviewed tightly link their MDM initiatives to real-time customerengagement and business processes. the ...

The MDM Market is Expanding to support context And insight over a third of vendor customers we interviewed tightly link their MDM initiatives to real-time customer engagement and business processes. these use cases depend on a wide array of attributes and metadata that provide context for personalization, logistics, and preventative maintenance. in the past,a hub accounted for only a few hundred data elements at most. today, customers need solutions that can support thousands of data elements for one domain and tens of thousands for a multidomain hub.Additionally, the data models are more dimensional and the data levels are deeper. to accommodate this added complexity and sophistication of the organization’s needs, customer references tended to prefer contextual and analytic MDM solutions over traditional MDM tools. Enterprise architects can see that (see Figure 3):
› Data management MDM tools solve data integration and standards challenges. Enterprise architects commonly choose these MDM tools for a reference data management style to uniformly persist and govern hierarchies and data mappings. Deployments support the reconciliation of multiple data sources that feed a data warehouse or business applications through extract, transform, load (Etl) processes or data virtualization. these tools are meant for scenarios where data requires a single view or golden record and where defnitions change infrequently.
› Model management MDM tools support multidomain and multiview needs. Enterprise architects use these MDM tools to support multidomain MDM in a single master data model(e.g., account master, supplier master) or several master data models that need to come together(e.g., integration of customer, product, account, supplier). Model management MDM tools add scenarios that require more than one defnition of a customer or that manage a variety of product catalogs and assortments, data orchestration, and presentation to data integration and movement capabilities.
› contextual MDM tools support semantic representations of business data. these MDM tools use a graph database to collect and link master data with additional attributes and metadata.Behavioral, preference, permission, security, identity, location, and time are all maintained and connected in the graph and represented to data consumers within a business context. For example, behavior patterns can defne the customer domain instead of focusing only on identity.this creates relevance, agility, and flexibility to shape master data to any business service.
› Analytic MDM solutions converge MDM with insight platforms. this solution combines MDM capabilities with analytics. powered by a graph database, machine learning, big data, and analytic visualization, these solutions translate master data directly into insight. Visualizations of data patterns show customer connections and preferences. Machine learning provides insights simply by understanding data linkage. Example scenarios are product recommendations, identity and
fraud analysis, mergers and acquisitions reconciliation, and patient outcomes.
鲜花
鲜花
握手
握手
雷人
雷人
路过
路过
鸡蛋
鸡蛋
分享至 : QQ空间
收藏