Rearizing DX! Total BI, Dr.Sum

Digital Transformation (DX) is the utilization of various data to transform your business. It sounds difficult to talk about transforming a business, but simply said, it means converting on-site events and the intuition and experience cultivated in the field into data and combining it with technology to streamline on-site operations and provide new services to customers. For example, Kubota, which supplies agricultural machinery, offers Kubota Diagnostics, an application that streamlines the repair of construction equipment malfunctions via smartphone. This is an application* that combines past malfunction data with AR to streamline the process of diagnosing malfunctions and reduce downtime of malfunctioning machines. There are many other examples of DX on the Internet, but most people tend to forget the purpose for which data is collected and how it is used in the field. Many companies focus on collecting data and do not think about how to utilize the data. After collecting the data, they spend a lot of time thinking about what they want to do with the huge amount of data, or they pay huge amounts of money to hire consulting companies to think about it.

*https://www.kubota.com/news/2020/20201217.html

Therefore, digital transformation is not only about digitization, but it is necessary to be aware of the transformation of business operations and to think properly about what data to collect and for what purpose. In order to find out "what data to collect and for what purpose," it is important to connect the data with the organization, and to have a culture of using data throughout the company at all times. This is because it is difficult to determine what is important data if you do not utilize the data usually. If the data is used usually, the team members in the field will understand both the data and the business best. Supporting their utilization of data is the path to realizing transformation. However, there are three barriers to transformation in the field.

1. Operational barriers: Difficult to use because data is divided by operations.

Usually, operations systems are separated, and data is spread out among different operations. Therefore, it is often the case that we need to ask each person in charge of a task to access the data needed for the analysis. Even after a request is made, the inability to centrally manage data poses various problems, such as days elapsing before the report is received, the data provided not containing the necessary information, and the need for processing to make the final report. To solve these problems, it is important to create a system where necessary data is available when needed.

2. Human Resource Barrier: No human resources to analyze data and do not know what to investigate.
Human resources are not data scientists, but people who analyze data at field users. The basics of analyzing data are to "look at the comparison," "look at the composition," and "look at the change," so it is possible for the ordinary user. Data scientists are needed only for big data analysis; in general operations, the focus is on the field users, and they are the ones who hold the requirements. Related to the next “Cultural barrier”, creating an environment that facilitates data analysis by on-site users and staffing for data analysis will lead to transformation.

3. Cultural barriers: Cultures do not utilize data very well

It is difficult to establish data analysis in a business environment that does not utilize data usually. However, in many cases, it is not that they do not utilize data, but that the system is the reason (1. Operational barriers). In such a situation, it is difficult to share only intuition and experience, even if one tries to explain with words alone, and more and more work becomes dependent on people. Thus, it is important to back up intuition and experience with data to make it easier to communicate. This will help to transfer the intuition and experience gained to the field and increase the field strength of the company. Moreover, a culture based on those data will lead to DX. In some cases, outsourced analysts are hired to analyze the data, but if they do not understand the data themselves, the analyzed data may not be utilized. However, this would not happen if the analyst and the user were the same. To achieve this, it is important to create a culture in which everyone can easily utilize data, and to aim for Data-driven Culture.

Dr.Sum, a total BI solution with the No.1 market share* in Japan, was developed to break down these three barriers and connect data and people. Dr.Sum, while using a database for data analysis as its core technology, has various functions for centralized management of various data generated in the business and a user interface tailored to the business that can be used intuitively, so that anyone can handle data, from data input to output. It supports data utilization throughout the company, from general users to analytical users and management, and realizes data utilization in the organization.

Dr.Sum's mission is to create an environment that makes it easy to utilize data, and many users are using Dr.Sum to realize a data-driven culture and digital transformation.
In the next issue, we will introduce various support functions for integrating data occurring in the business, which is a feature of Dr.Sum.