DcX Support Service Achieve Data-Driven Management(Data-centric Transformation)

B-EN-G defines DcX (Data Centric Transformation) as not only visualizing the processes of each department by integrating applications such as ERP, MES (Manufacturing Execution Management System), LIMS (Quality Test Management System), and IoT, but also combining and utilizing the data generated from them to achieve overall optimization and create new value. 
We support the construction of integrated platforms that connect applications and data, helping companies realize data-driven management.

Please also see the related page Data Chain and its Realization.

Approaches to Data Analysis in DcX

By analyzing data accumulated in IT systems on a daily basis and obtaining suggestions for problem solving, we provide one-stop support for action plans for business process reform and improvement, as well as implementation of IT systems to realize these plans.

Examples of DcX Support

This is an example of DcX support. We derive effective data analysis methods from current issues. There are many different analysis approaches depending on the issue to be solved, but we proceed with data analysis by concretely identifying phenomena that cannot be understood from data alone, such as through interviews with the business side. B-EN-G's distinctive feature is that we can support the formulation of on-site actions and improvements to IT systems based on the results of our insights.

For Data-Driven Corporate Activities: Data Integration Management Infrastructure

Data-driven management requires the use of accurate and fresh data, including ERP, to manage the data stored in different applications, to correctly measure corporate activities, and to make quick decisions in case of a situation where there is a lack of data.
B-EN-G supports the construction of data integration management platforms from the concept stage to development based on our extensive experience in implementing multiple applications.

B-EN-G's DcX Support Service

We support our clients throughout the entire data utilization analytics lifecycle, including organizing various business challenges, setting goals for data utilization, analyzing existing data to identify inadequate data, identifying data to be collected, building a system for collection, and verifying the data.

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