Proper Data Solutions Can Accelerate the Company's Digitization Process and Enhance Its Competitiveness

By Xuewu Wang, Head of Data Labs, China Eastern Airlines

Xuewu Wang, Head of Data Labs, China Eastern Airlines

In the past few years, Chinese airlines have faced the impact of high-speed rails, low-cost airlines and OTAs, and the market competition has become increasingly fierce. It is imperative that digital-transformation be carried out to increase the core competitiveness by data-driven. The development of new technologies such as Big Data, Cloud Computing, Artificial Intelligence and Internet of Things has brought many new opportunities to the aviation industry. However, Airlines need to face more new challenges.

China Eastern Airlines(CEA) established Data Labs to meet the challenges of the era of big data. Data Labs is an innovative accelerator for CEA and has an open data science platform bases on Hadoop. It provides data sandbox, data exploration, data mining, data visualization and other tools for analysts in various business and IT fields. It also provides data expert consultation in various fields, as well as design a series of big data analysis courses. Labs is driven by business scenario such as improve service capabilities, improve business efficiency, reduce operating costs, reduce business risk or discover new market opportunities. Finally, Labs will output market insights, algorithm solutions, data products, data capabilities and analysis talents for the company.

Our goal is to empower all fields of CEA through the concept of “Data Labs + Field.” CEA is a traditional enterprise, which different from internet companies and professional software vendors, and most rely on cooperation with external professional service providers to reduce the cost of building IT platforms and simplify the complexity of their management.

I have many years of experience in digital projects in the aviation industry. I have learned or actually used Oracle's many series of products. Here are some of my experiences with Oracle products. Hope it could help.

Enterprise Data Management

The data science platform of Labs has three major sources of data: Enterprise Data Warehouses, Big Data Platform and Real-time Data Platform.

The most primitive production data is generally stored in the Oracle Database 11g. The Relational Database has the following advantages:

1) Consistency of the transaction.

2) The two-dimensional table and the connection between them are also easy to understand.

3) SQL operation is simple and it has a very rich community technical support.

But the disadvantage are also obvious:

1) Read and write performance is relatively poor.

The development of new technologies such as Big Data, Cloud Computing, Artificial Intelligence and Internet of Things has brought many new opportunities to the aviation industry

2) It is unable to meet the high concurrent read/write demand.

3) Low-efficiency read/write of massive data.

Analysis users do not perform data analysis directly on Relational Database. We aggregate production data into Data Warehouse then perform reporting and BI analysis. Labs get production data from the Data Warehouse.

Another data source for Labs is the Big Data Platform, which is mainly used to store unstructured data and structured massive QAR data. QAR is a flight data recorded by thousands of sensors throughout the aircraft and annual total over 100TB. We adopt a clustered platform built by Oracle Big Data Appliance(BDA)to provide a highly scalable system for data management. BDA can effectively process and analyze massive data in Hadoop, and the performance and efficiency are improved significantly. Administrators use the user interface to centrally configure and manage the cluster, making management very convenient and intuitive.

With the popularity of big data technology, data analysis needs to be implemented faster. The traditional data analysis is a subject to the bottleneck of the storage medium. The In-memory calculation comes into being. The In-memory calculation can perform real-time analysis and calculation on large-scale massive data without prior data preprocessing and data modeling. To meet this challenge, we use the high-performance computing platform Oracle Exadata to provide real-time data computing capabilities. Exadata can obtain better performance through hardware and software through comprehensive internal acceleration methods.

Big Data Analysis and Data Visualization

Experts need to analyze the massive data that we collected. The traditional BI tools can no longer meet the needs. Oracle has several cool products for big data analysis and data visualization.

Oracle Big Data Discovery (BDD): BDD is a product built on Apache Spark. It allows anyone to find, explore, transform and analyze big data. Discover new insights, then share results with other tools and resources in the ecosystem. It is very convenient to use. It can easily find the correlation between different data dimensions and the data quality.

Data Visualization Desktop (DVD): DVD has rich visual controls, interactive exploration in a drag-and-drop manner, and can be easily shared with others. It can intelligently interpret the data, recommend the best form of expression, and automatically perform linkage based on context. Easy to analyze database data or user’s own text data.

Oracle Analytics Cloud (OAC): OAC is the Oracle’s strategic analytics platform for the future. It works at the SaaS layer. Unlike other cloud products, OAC is a product portfolio that includes BI as a Service, Big Data as a Service, Mobile data analysis, etc. Companies that plan to use cloud services can consider OAC.

Conclusion

Proper Data Solutions can accelerate the company's digitization process and increase its competitiveness.

Each company is faced with different situations of market competition and the implementation of digital strategy is not the same, but in the end, it is all about setting up data-driven companies. In the process of moving toward a data-driven company, we must be very clear about the current situation of the company, the basic data platform architecture, the status of big data applications, the reserve of technical capabilities and the driving factors of digital strategies, etc., and select the appropriate products based on these actual conditions.