Hitachi Enterprise Application Services

Data Preparation Service helps customers utilize and apply their and supports accelerated digital transformation

When utilizing and applying various types of internal and external data, the data must be prepared (preprocessed to make it usable). Such preparations are said to account for approximately 80% of the data utilization and application process, in terms of hours worked. Hitachi’s Data Preparation Service analyzes data characteristics by using AI, and supports the examination of data processing methods as well as the verification of preprocessing logic in relation to such examinations. The service reduces the burden of data preprocessing, which is dependent on individual human skills, thereby enabling the customer to rightfully focus on . It therefore helps customers utilize and apply their data.

▼ Flow of data utilization and application Data analysis, utilization, Data preprocessing application

Hypothesis Data analysis, testing utilization, Before Data lake application

Data analysis based on Manual verification of Adding verified processing Data processing Variations in quality manual work implemented data methods to ETL tools using ETL tools due to differences (e.g. hearings and surveys) processing methods in people’s skills Examination and Data understanding verification of data Implementation Execution of Data analysis, processing methods data processing utilization, application Data Preparation Service Data Seamless transition from data understanding to implementation, with easy-to-understand screens

1) 2) Supporting examination 3) After AI-supported data and verification of Linkage to ETL tools Supporting understanding processing methods quality improvement Data analysis, utilization, Hypothesis application testing

Data lake - Item name inference - Standard processing methods - Export of processing Data processing - Profiling (patterns, outliers, etc.) (cleansing/integration) methods using ETL tools Promoting focus - Analyzing relationships - Custom processing methods (sharing expert’s know-how) on data analysis

Feedback of results Helping customers utilize and apply their data through efficient data preprocessing

*ETL stands for Extract, Transform, and Load. ETL consists of extracting data accumulated in a core business system, processing or transforming the data accordingly, and then loading the data into the system. Features of the Service AI-supported data understanding The service analyzes data specifications and by using AI to support understanding of data such as item names, outliers, and relationships in the data. It reduces the burden of manual check work.  Identifies potential item names  Visualizes outliers and inconsistently formatted data in  Suggests possible relationships in data graphs, by using data profiling

Supports examination and verification of processing methods In addition to defined, standard processing methods (preprocessing logic), the service also streamlines the examination and verification of processing methods such as cleansing and integration by functions that register and share expert knowledge and by easy-to-use screens. The service reduces the burden of having to examine and verify data processing methods that are dependent on individual human skill and implemented skill.

Linkage with ETL tools The service enables customers to link verified data processing methods with ETL tools. The service promotes data utilization and application, by providing a seamless transition from data understanding to verification of data processing methods and actual operation.

Putting the service into practice (customer cases)

 I want to shorten the preprocessing time for IoT data that is to be utilized and applied Streamlining data preprocessing to focus on data analysis

Data from stores and IoT data collected from factory Support for the AI-supported examination of equipment and other sources is abundant and diverse, data analysis preprocessing logic meaning that data preprocessing for utilization and Facilitates data application often requires a large amount of time. preparation work This service detects unexpected data, and supports the examination of preprocessing logic, thereby reducing Detects • unexpected • Unifying formats Enables the customer the burden of preprocessing and enabling the customer data • Removing to focus on to focus rightfully on data analysis. unnecessary data data analysis

 I want to improve the precision of utilization and application results Improving data quality by detecting outliers to improve the precision of utilization and application results

If the accumulated data (such as purchasing and usage Detects outliers results) to be utilized or applied includes outliers, the desired result might not be achieved. Improves data quality This service detects outliers and improves the quality of accumulated data before utilization and application. Consequently, in areas such as business expansion Examines based on purchasing forecasts, and loss prevention appropriate ways of Leads to more precise based on detection of factory equipment breakdowns, processing data utilization and the service leads to more precise utilization and including outliers application results application results.

● Hitachi reserves the right to improve or otherwise change the specifications of this service without prior notice.

For more information about this service

■Official website of Hitachi, Ltd.’s Applications Services Division https://www.hitachi.com/products/it/appsvdiv/

2019.11