With the realization of DX becoming an urgent management issue in various industries, “DataOps” is attracting a great deal of attention. DataOps is one of the methods of data utilization, and its importance is also attracting attention in the manufacturing field. Therefore, in this article, we will explain the overview of DataOps, the benefits it brings to the manufacturing field, and actual introduction examples.
What is DataOps?
“DataOps” is a concept advocated by the US research firm Gartner, Inc. It focuses on deepening communication between data administrators and users throughout an organization, and on streamlining and automating data utilization. It’s a data management method.
With the development of information and communication technology, the amount of data handled by companies has increased explosively, and how to utilize the accumulated big data in the management area has become an important issue. DataOps plays an important role in implementing such data utilization.
DataOps is a concept derived from the coined word ” DevOps ,” which combines “development” and “operation .” DevOps refers to a software development method in which system developers and operators collaborate and cooperate. The “developers” and “operators” of the system in DevOps correspond to the “administrators” and “users” of data in DataOps, and DataOps is the application of the culture and theory of DevOps to the data management area.
In other words, the goal of DataOps is to achieve labor-saving and efficient data utilization through collaboration and cooperation between data administrators and users.
Benefits of DataOps for Enterprise DX
In recent years, the realization of “DX (Digital Transformation)”, a management reform using digital technology, has become an important management issue for many companies. Data management is essential to realizing DX, and DataOps supports its optimization. Incorporating DataOps into your organization’s data management structure provides the following benefits:
- Agile data processing and analysis
- Centralized data management
- Enhanced security by migrating to the cloud
- Realization of data-driven management
Agile data processing and analysis
The important thing in data management is to build a management system that allows you to obtain the data you want when you need it. As technology advances and develops, the speed of market change is accelerating. In order to respond quickly and accurately to changing market trends and diversifying customer needs, decision-making based on speedy data analysis is essential. .
DataOps, in which data administrators and users collaborate and cooperate to develop an information management system, plays a major role in this. For example, by coordinating data administrators and users to develop and improve business processes and system environments, it is possible to more smoothly build an “information management system that allows the person in charge to obtain the data they want when they need it.” You can do it. By developing an information management system from the perspective of data administrators and users, agile data analysis becomes possible.
Centralized data management
The reason behind the need for DataOps is “data silos”. Silo refers to “a state in which the information systems and data managed by each department within an organization are isolated, making it difficult to share and collaborate with information.” Although it differs depending on the business form and management system, it is common for the organization’s data to be managed individually by the information system of each department.
In data analysis, basically, it is necessary to follow the steps of “collection/accumulation”, “processing/conversion”, and “visualization/analysis” of data. However, when data is siloed, it takes a lot of time in the first step of collecting and accumulating data. If you can build an information management system based on the concept of DataOps, you will be able to centrally manage the data distributed within the organization, speed up the analysis process, and collaborate across departments.
Enhanced security by migrating to the cloud
Data management using cloud services is very compatible with the DataOps approach. An on-premises system is highly customizable and flexible, and has the advantage of being able to build an environment that meets your company’s functional and security requirements. However, in order to do so, it is necessary to build a corresponding IT infrastructure, and a huge cost is required to introduce server equipment, network equipment, and software.
A cloud-based system environment does not require the introduction of hardware, and file servers and storage can be built in a public environment, contributing to a significant reduction in installation and management costs. And by using cloud services that have obtained international standard security certifications, such as AWS, Microsoft Azure, and Google Cloud, you can operate and manage data and files under a robust security system.
Realization of data-driven management
“Data-driven management” refers to a management method that uses quantitative data analysis as a starting point for management judgment and decision-making. Rather than relying on intuitive elements such as intuition and experience, it is a management system that makes decisions based on logical elements based on data analysis. In order to build a data-driven management system, data analysis such as web analysis, statistical analysis, and big data analysis is essential.
Without such a logical data analysis process, it is impossible to grasp market trends and make precise demand forecasts. In order to realize data-driven management, it is necessary to permeate the importance of data management throughout the organization and establish data governance. Developing such an information management system is also the goal of DataOps, and can be said to be an essential concept in realizing a management system based on quantitative data analysis.
Why DataOps is Good for Manufacturing
In a narrow sense, DataOps is one of the management methods for data utilization, but in a broader sense, it can be said to be an initiative to optimize data management through the use of digital technology and bring about changes in organizational systems and corporate culture. And that is the essence of DX, and the way it should be in today’s manufacturing industry . Currently, Japan is facing a serious shortage of human resources in various industries due to the declining working-age population due to the declining birthrate and aging population.
Especially in the manufacturing sector, the labor shortage is accompanied by an aging workforce, and the industrial structure itself is declining due to the delay in digitization. However, today is said to be the dawn of the “Fourth Industrial Revolution,” which is technological innovation driven by AI and IoT , and the manufacturing industry is also expected to make great strides through the use of these new technologies. For example, by incorporating AI and IoT into the production system, equipment maintenance using deep learning and machine learning, automation of inspection work, and anomaly detection with accuracy that far surpasses that of humans can be realized.
In order to build such a next-generation production system, it is essential to have a data analysis platform that collects all kinds of operational data from production facilities and operates them efficiently. If we can build an information management system based on DataOps, we can streamline the process of data analysis and support the production system through the strategic use of AI and IoT. Data management is essential for the efficient operation of cutting-edge technology, and for that reason, it can be said that efforts toward DataOps are also indispensable.
DataOps case study in manufacturing
Yokogawa Electric Corporation, a major electronics manufacturer of process control systems, has long had business issues related to equipment maintenance. For example, maintenance work for machine tools, drives, etc. is generally carried out on a regular basis according to a set schedule. However, in this case, the person in charge would have to check the production equipment that is operating well, wasting time and opportunities that could be devoted to core operations.
Therefore, the company utilized the DataOps platform “Cognite Data Fusion” provided by Cognite to build a new maintenance solution. The solution includes the ability to notify maintenance-free equipment, allowing equipment maintenance to be based on actual conditions rather than fixed schedules. As a result, we were able to realize labor-saving and efficient equipment maintenance, and smoothly build a production system that allows human resources to concentrate on core operations.
summary
DataOps is the cooperation between data administrators and users to achieve more efficient and productive data utilization. It can be said that it is an indispensable initiative in the manufacturing field where the realization of DX is an urgent issue. Please try to promote DataOps in order to build a data-driven production system.
