Scenario-driven Big Data Realization

Xuewu (Shawn) Wang, , China Eastern Airlines

Xuewu (Shawn) Wang, , China Eastern Airlines

The Challenges Faced by Traditional Companies

The data era has arrived, the market, the customer demand and the business model has changed a lot. In the past few years in the aviation industry, China Eastern is facing increasingly fierce competition in the travel market by the rise of oil prices, the continuous expansion of high-speed rail lines, the development of low-cost airlines and the expansion of online travel agencies. China Eastern needs data transformation to improve market competitiveness.

Limitations Encountered by Most Companies When Using Data

There are many limitations when the companies use data. The most common problems are computing capabilities of massive data, data analysis capabilities, data quality, data standards and data islands. The biggest difference between the traditional companies and the internet companies is that the internet companies can digitize the behavior of all online customers. The traditional companies have limited ability to collect the data of customer behavior. Although the traditional companies are lacking service contact points which can be digitizing customer behavior. They still have a vast amount of sales data, ERP data, CRM data, sensor data, and customer feedback data. But in most cases, these data are not connected, in the case of data islands. The traditional companies should establish data platforms or mechanisms to change this situation, allowing data to be shared within the company and allowing data to flow between different business fields. The company can build data warehouses, domain data marts, big data platforms and data sandboxes, which are good solutions to this challenge. Meanwhile the traditional companies should increase service contact points, generate and collect more data and fully exploit the value of data.

Three KEY Capabilities for Data Realization

“Although the data has not yet been included in the balance sheet of the company, this is only a matter of time.” - Viktor Mayer-Schönberger.

The transformation of data has three core drivers: data-ization capability, big data analysis capability and data product capability.

Data-ization Capability

Data-ization capability means the company can collect the data of all business processes and digitizes the business activities such as production, operation, customer service and marketing as much as possible. The data has become a very core asset for every company.

China Eastern has 650 aircraft, it is covering 177 countries, serving more than 100 million passengers per year and travelling to 1,074 destinations.

The biggest difference between the traditional companies and the internet companies is that the internet companies can digitize the behaviour of all online customers

It ranks 7th in the world. We build business analysis systems and operational support systems for passengers of the before-flight, during-flight and after-flight. They are digitizing the entire process of passenger travel and implementing a closed loop of passenger service. In the customer services, the marketing, the business development, the cabin services, the ground services, the flight transportation, the aircraft engines, the call center, the operations center, the e-commerce, the APPs, etc. These business fields generate large amounts of data every day. We face the challenge of managing large amounts of data.

After collecting massive amounts of data, the company needs to manage that data. It would be a successful solution if the company build an Enterprise Master Data Management system and a Metadata Management system for the company. At the same time, to ensure the data quality and the data standards, the company needs to implement data governance.

Big Data Analysis Capability

Big data analysis capability is a very core competency of a company. Efficient big data analysis capability can quickly capture the insights from massive amounts of data, discover patterns and predict the future. Companies only have a large amount of data, but they do not have the ability to analyze the data. It is impossible to realize the value of data assets. Analysts need computing power that can handle massive amounts of data. They also need to have the ability to translate business problems into data analysis problems. Develop data mindset in the process of constantly using data is very important to them. The best big data analysis practices have following characteristics: (Re) use data, Data-driven, Mindset, Continuous exploration process, Iterative, Open and share, Verification. To implement effective big data analysis, analysts need to establish a data analysis process and continually iteratively optimize the analysis results so that the desired benefits can be achieved by analyzing the results.

Data Product Capability

Business objectives can be clearly defined by alliance data team then define what problems can be solved by the analysis results, where the analysis results will be applied and how they are applied. The value of the analysis results can be quantified as much as possible. Companies need to manage data generation, data collection, data integration, data analysis and data productization.

There are many ways to realize the value of data, such as sales data, data products, data services and data sharing. Business experts, analysts and data experts form an alliance data analysis team to clarify business scenarios, required resources, business goals, and implementation plans. The alliance team is driven by the business scenario to designs algorithm solutions and quantifies the business value of the results. They can apply data exploration, data discovery, data visualization, data mining, machine learning, artificial intelligence, NLP or operations research to implement business scenarios and quantify the value of their results. For example, the company can achieve increased profits, improved business efficiency, reduced operating costs, new market opportunities, and risk management through algorithm.

The strategic position of data in the company is becoming more and more obvious. Whoever has more data, can understand the market better, know the situation of competitors and grasp the initiative of the market. Many companies are improving their competitiveness through big data and AI. Big data realization by scenario-driven is a very successful best practice. It will determine the right research direction for us and greatly reduce the risk of investment for the company.