The Open Business Data Lake Standard, Part VI
Blog: Capgemini CTO Blog
In my previous blog posts (Part I, Part II, and Part III) about the ‘Open Business Data Lake Conceptual Framework (O-BDL) I introduced its background, concept, characteristics and platform capabilities. In Part IV and Part V I compared a Data Lake with other data processing platforms and described how an O-BDL should work. In this sixth part I’ll define possible business scenario’s which can make use of an O-BDL.
The following business scenarios can be enabled by O-BDL platform implementations. Their order shows a possible underlying maturity progression:
- Enterprise Data Warehouse (EDW) Off-Load
- Discovery Platform
- Big Data Applications
- Data-Driven Enterprise
- Ecosystem of Data-Driven Enterprises
Enterprise Data Warehouse (EDW) Off-Load
This scenario is relevant for enterprises that already have large Enterprise Data Warehouses (EDWs) and experience difficulties in dealing with increasing data storage needs. Some EDWs are populated by data that could be archived for instance when the EDW is forced to hold the raw data/detailed information for a long period to meet regulatory requirements. Other example are the EDW is the only places to consolidate and store historical information so data of low value is incorporated into the consolidated model – with all of the cost that that entails. An O-BDL offers low cost mechanisms for storing data and processing this type of (low-value) data, while the EDW can focus on high-value workloads.
O-BDLs provide a lot of flexibility, notably regarding the data structure, the data lifecycle (“live” or historic), processing workloads, and input/output rates. From a business point of view, this flexibility can be leveraged to explore data and discover data patterns through experiment analytics. A discovery platform scenario starts small with proof-of-concepts for business cases that are mostly aimed at increasing operational efficiency and improving existing services. It can also start with existing data that is already stored or collected by the enterprise and leveraging the fact that an O-BDL can store and process large sets of structured and unstructured data.
Big Data Applications
This scenario goes one step further compared to the discovery platform. Instead of discovering and exploring possible data patterns, analytics (i.e. data assembly and real- time insights) are designed and tuned to be integrated into analytics-led downstream applications. thus increasing top-line revenues, possibly for multiple lines of business. By building building big data applications, the O-BDL for becomes a real and important enterprise asset.
Within a data-driven enterprise scenario data and analytics are used to create new services and/or opens new revenue streams. This way the O-BDL as a platform supports the digital transformation of the enterprise business, by leveraging an internal community of contributors who continuously bring new data and develop new analytics capabilities. Therefor data-driven enterprises provide self-service data access to the O-BDL so that people in the organization turn to the data sets when they need to make decisions or when they want to create new insights based on own data and data within the O-BDL.
Ecosystem of Data-Driven Enterprises
In this scenario, one O-BDL is serving multiple organizations. Examples are suppliers and third-party data or analytics contributors. Compared to a “single” data-driven enterprise, the community of contributors is enlarged outside of the organization and can ultimately become an open community. When the ecosystem is created to enable multiple “equivalent” organizations to collaborate, an O-BDL should be seen as an outside-in platform that could be hosted and operated by a third-party organization providing an O-BDL “as-a-service” to the ecosystem members.
Now that possible business scenario’s have been described I want to introduce a set of concepts which explain O-BDL related data processing topics in more detail. This will be discussed in the seventh blog post.