Can A Supply Chain Be Managed Using All Possible Data? Part 3

To fully realize the benefits of Big Data, Enterprise requires massive transformation, in order to:

  •  Adopt new technology and techniques
- New patterns – DW to data reservoir, ETL based on M/R, parallel steam processing
- Data Processing – limited parallelism to parallel processing, shared nothing architecture
- Vertical and horizontal scalability
- New development tools
- New analytics from Canned Reports, mining to pattern discovery, machine learning
  • Change the organization and acquire new skills and talents
-  New structure from siloed analytics to federated analytics, introduction of Chief Data Officer (CDO) function
-  New talent required data scientists
-  New technologists with parallel processing and advanced data analytics skills
  • Rebuild IT Infrastructure
- From specialized servers to clusters of commodity hardware and software
- Enterprise-strength solutions for DR and security also need to be created
In the table below, we provide a general list of challenges, comparing traditional and Big Data approaches:

Logistics companies operate with massive chunks of data, be it inventories, goods in transit, traffic information or even customer complaints. Big Data helps them improve the customer experience, increase operational efficiency and drive business innovations on the back of social insights.
Big Data is a powerful enabler of optimized and new business models in logistics. But it’s easy for companies to start out on the wrong foot by talking about Big Data as a readily available technical solution to a business problem. It’s important to remember that Big Data is not only technology, it’s also advanced data governance, data protection and skills that can turn vast raw data into valuable information
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