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Integrating Big Data and Application Development into today’s Social Supply Chain

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In my last post I explained the concept of the enterprise social graph (ESG), and discussed why supply chains  can benefit greatly from the advanced cloud platforms that can enable an ESG. Today I want to go a step further and show how Big Data and application development are handled within these new environments.

First, today’s advanced cloud platforms  provide a flexible architecture that enables the aggregation, storage, querying, processing, and analyzing of Big Data. This capability to provide the scalability across millions of nodes and edges for aggregating, storing and analyzing the data is substantial. The advanced data store is optimized for graph operations and uses a flexible and extensible data schema.

Integration and extensibility for various types and formats of internal as well as external data sources are also being provided . The extracting and mapping functionality can handle both structured as well as unstructured information. The data aggregation monitors and regularly updates entities, properties and associations by requesting the newest representation from the respective data source, and re-aggregates if new data is available. The combination of various data analysis technologies makes it possible to constantly interconnect entities, enhance the data quality and transform any unstructured data into useful knowledge.

Advanced  cloud platforms also provide the ability to distribute and recommend information from the network graph to the right users based on a specific context. Moreover, it  provides easy, comprehensive and consistent user access to the data in the knowledge graph via APIs, so it is possible to create applications and extensions based on this structured information. Users can visualize, explore, and collaboratively edit the information in the supply network graph based on the tags the owners decide to attach to their assets. A graphical user dashboard enabling this level of interaction is also included.

Finally, in order to populate the ESG, either the existing or custom API’s are used to take data directly from the customers’ ERP or shop floor systems without having to spend months on data mapping and configuration. With many  supply network participants still communicating by email, the environment supports various data schemes including the typical standards like EDI or EDIFACT. Each of these data connections is included as part of the definition of the network node when it is modeled as an extension on the supply network graph. This node can be modeled as a supply network hub or spoke, with many of the larger spokes becoming hubs over time. These systems also allow for discovery and alerts across the network assets based on permission structure. Network assets are tagged by owners in order to participate in this process.

The ESG has the potential to innovate significant functional capabilities over time. The graph provides information about related entities, and can therefore be used to locate potential demand, supply, network assets, products, processes, materials or locations.  Further, the structured knowledge in the ESG can be utilized for advanced search capabilities such as natural language processing, and to provide semantic capabilities by understanding the contextual meaning of a search query.

Over 400,000 companies are testing various levels of enterprise knowledge sharing with Yammer. The next logical step will be for them to move into a well-structured, high value ESG environment.

Ranjit Notani
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