NRI develops kit for analytics project with AWS Japan cooperation

Nomura Research Institute (NRI) has developed an analysis kit for analytics projects in collaboration with Amazon Web Services Japan (AWS Japan). NRI is a professional engineer who has technical knowledge about AWS, AWS AI (artificial intelligence) / ML (machine learning) certified engineer, data analysis, responding to analytics projects carried out by companies while utilizing this kit etc. Through participation for professionals, we will support everything from environment construction to data analysis. The kit is a collection of the know-how that NRI has cultivated in analytics-related projects, and utilizes “Amazon SageMaker.” Amazon SageMaker is a managed service provided by AWS for developers and data scientists that provides model building, machine learning, and deployment capabilities. With this kit, “data model and sample data tailored to the business to be analyzed”, “template for automatic construction of analysis environment”, and “knowledge of NRI are aggregated / systematized” so that analytics projects can be started / operated effectively and efficiently. The three “Process Guides” are standard equipment. We are developing data models and sample data that match the typical themes of data analysis / utilization such as “sales forecast” and “customer targeting”, and sample source code based on them. The prepared data model can be changed and used. Therefore, there is no need to create a data model from scratch. The auto-build template allows you to define the configuration of AWS-provided services required for your analysis project and build them automatically. Normally, you need to consider and select the services provided by AWS that you need, and then set their dependencies and security, but these tasks can be done automatically. By utilizing the template, the construction period can be significantly shortened, so the number of analysis cycles performed during the PoC (proof of concept) period can be increased, and the analysis period in the project can be lengthened. In addition, depending on the content and stage of the theme to be analyzed, you can select and build the necessary functions such as “data capture / visualization”, “machine learning”, and “utilization of DWH (data warehouse)”, for full-scale utilization from PoC. It is also possible to scale up to an analytical platform. In addition, by using the process guide, you can understand how to proceed with an analytics project that utilizes the services provided by AWS, and for members and managers who are inexperienced in data analysis, it will be easier to implement the project. / Leveling can be expected.