Real Time Data Warehouse

Continental Airlines Flies High with Its Real-Time Data Warehouse

As business intelligence (BI) becomes a critical component of daily operations, real-time data warehouses that provide end users with rapid updates and alerts generated from transactional systems are increasingly being deployed. Real-time data warehousing and BI, supporting its aggressive Go Forward business plan, have helped Continental Airlines alter its industry status from “worst to first” and then from “first to favorite.” Continental Airlines is a leader in real-time BI. In 2004, Continental won the Data Warehousing Institute’s Best Practices and Leadership Award.

Problem(s):

Continental Airlines was founded in 1934, with a single-engine Lockheed aircraft in the Southwestern United States. As of 2006, Continental was the fifth largest airline in the United States and the seventh largest in the world. Continental has the broadest global route network of any U.S. airline, with more than 2,300 daily departures to more than 227 destinations. Back in 1994, Continental was in deep financial trouble. It had filed for Chapter 11 bankruptcy protection twice and was heading for its third, and probably final, bankruptcy. Ticket sales were hurting because performance on factors that are important to customers was dismal, including a low percentage of on-time departures, frequent baggage arrival problems, and too many customers turned away due to overbooking.

Solution:

The revival of Continental began in 1994, when Gordon Bethune became CEO and initiated the Go Forward plan, which consisted of four interrelated parts to be implemented simultaneously. Bethune targeted the need to improve customer-valued performance measures by better understanding customer needs as well as customer perceptions of the value of services that were and could be offered. Financial management practices were also targeted for a significant overhaul. As early as 1998, the airline had separate databases for marketing and operations, all hosted and managed by outside vendors. Processing queries and instigating marketing programs to its high-value customers were time-consuming and ineffective. In additional, information that the workforce needed to make quick decisions was simply not available. In 1999, Continental chose to integrate its marketing, IT, revenue, and operational data sources into a single, in-house EDW. The data warehouse provided a variety of early, major benefits.

As soon as Continental returned to profitability and ranked first in the airline industry in many performance metrics, Bethune and his management team raised the bar by escalating the vision. Instead of just performing best, they wanted Continental to be their customers’ favorite airline. The Go Forward plan established more actionable ways to move from first to favorite among customers. Technology became increasingly critical for supporting these new initiatives. In the early days, having access to historical, integrated information was sufficient. This produced substantial strategic value. But it became increasingly imperative for the data warehouse to provide real-time, actionable information to support enterprise-wide tactical decision making and business processes.

Luckily, the warehouse team had expected and arranged for the real-time shift. From the very beginning, the team had created architecture to handle real-time data feeds into the warehouse, extracts of data from legacy systems into the warehouse, and tactical queries to the warehouse that required almost immediate response times. In 2001, real-time data became available from the warehouse, and the amount stored grew rapidly. Continental moves real-time data (ranging from to-the-minute to hourly) about customers, reservations, check-ins, operations, and flights from its main operational systems to the warehouse. Continental’s real-time applications include Revenue management and accounting, Customer relationship management (CRM), Crew operations and payroll, Security and fraud, and Flight operations.

Results:

In the first year alone, after the data warehouse project was deployed, Continental identified and eliminated over $7 million in fraud and reduced costs by $41 million. With a $30 million investment in hardware and software over 6 years, Continental has reached over $500 million in increased revenues and cost savings in marketing, fraud detection, demand forecasting and tracking, and improved data center management. The single, integrated, trusted view of the business (i.e., the single version of the truth) has led to better, faster decision making. Continental is now identified as a leader in real-time BI, based on its scalable and extensible architecture, practical decisions on what data are captured in real time, strong relationships with end users, a small and highly competent data warehouse staff, sensible weighing of strategic and tactical decision support requirements, understanding of the synergies between decision support and operations, and changed business processes that use real-time data.

Question #1

Analyze the above case from the perspective of Drivers and Enablers of Big Data Analytics. What were the divers and enablers of the Go Forward Strategy?

Continental Airlines received vast amounts of data but did not have clear ways of analyzing it to come up with an efficient strategy. There are various drivers and enablers of big data analytics which can be attributed to the success of the Go Forward Strategy (Chadwick, 2016).

One of the enablers of a data-driven process is placing strategic emphasis on brands or customers. Continental Airlines, having a sharp focus on its brand as best and the favorite, worked on real-time data systems to ensure the best customer experience in accessing services.

Secondly, the key performance metrics and clear strategic challenges are another enabler. After the struggle from losses and bankruptcy ended, the focus shifted from simply profitability to be the most favorite. The management introduced this other performance indicator, leading to further usage of big data to make the brand a customer favorite.

Adopting a design thinking approach to problem-solving was another enabler of the Go Forward Strategy. Data analytics was used to solve fraud problems and efficiency. It also brought about the accuracy of bookings, and therefore no other customers were turned back due to over-booking flights.

Question #2

Describe the four (4) benefits of implementing the Continental Go Forward strategy.

The first benefit of implementing the Continental Go Forward Strategy was bringing the airline back to profitability. This was arrived at through improved customer experience through better bookings and departure timing management. Initially, too many customers were turned back due to overbookings and baggage arrival challenges.

Secondly, the Go Forwards Strategy helped the airline save $41 million by investing $30 million for 6 years. Besides, they were able to eliminate fraud of about $7 million.

Thirdly, the integration of all the systems together led to the increment of their revenue to over $500 million and saved costs in the marketing activities, detection of fraud, ability to forecast demand and act accordingly, and improvement of the data center management (Ostrom et al., 2010).

The Go Forward Strategy also positively impacted the achievement of various awards of performance metrics in the interest. This made the airline achieve and maintain its goal of being the best.

Question #3

Explain with examples why it is important for an airline to use a real-time data warehouse.

Real-time warehousing is important to airlines because it helps them integrate revenue management and accounting systems, customer relations management, staff operation and payroll, security and fraud detection, and flight operations. This significantly helps increase efficiency by reducing the cost associated with such systems being operated separately and reducing the turnaround time in operations. For example, in the case of Continental Airlines 1998, the separate financial management, marketing management, and operations systems led to ineffectiveness. However, after the systems were integrated, they led to efficiency.

Question #4

Identify the major differences between the traditional data warehouse and a real-time data warehouse, as was implemented at Continental.

The main difference between the traditional data warehouse and the real-time data warehouse is that the traditional one keeps only the historical data in the sense that it updates its data periodically. In contrast, the real-time one, on the other hand, keeps real-time data (Maayan, 2018). This bears another difference in the use of the two warehouses. The traditional data warehouse can only be used for strategic decision-making. In contrast, the real-time data warehouse can be used for strategic and tactical decisions since the latter generates the reports needed within a significantly short period (Bouaziz, Nabli, & Gargouri, 2017).

The other difference evident in the case study of Continental Airlines is that the traditional data warehouse accepts monthly, weekly, and daily data concurrency. In contrast, the real-time warehouse avails the data within minutes.

Question #5

What strategic advantage can Continental derive from the real-time system as opposed to a traditional information system?

One of the advantages of the real-time data system is that it makes Continental Airlines make quick and accurate decisions. This is enabled by the availability of more current and accurate data that is consistent. They do not need to wait until the day ends to get the required data (Schulte, 2016).

Secondly, real-time data systems allow more users to access, unlike traditional information systems, which can handle a limited number of users (Ramalingam et al., 2017). This makes it possible for the organization to integrate more systems from different departments, allowing them to access the same systems  (Bouaziz, Nabli, & Gargouri, 2017).

References

Bouaziz, S., Nabli, A., & Gargouri, F. (2017). From Traditional Data Warehouse To Real-Time Data Warehouse. Advances in Intelligent Systems and Computing · February 2017, 1-10.

Chadwick, P. (2016, April 29). 5 Enablers of Big Data-Driven Strategy Execution. Retrieved June 18, 2020, from IEDP Developing Leaders: https://www.iedp.com/articles/from-big-data-to-strategy-execution/

Maayan, G. D. (2018, April 30). The difference between a traditional data warehouse and a cloud data warehouse. Retrieved June 18, 2020, from Dataversity: https://www.dataversity.net/difference-traditional-data-warehouse-cloud-data-warehouse/

Ostrom, A. L., Bitner, M. J., Brown, S. W., Burkhard, K. A., Goul, M., Smith-Daniels, V., et al. (2010). Moving Forward and Making a Difference: Research Priorities for the Science of Service. Journal of Service Research 13(1), 4-36.

Ramalingam, B., Barnett, I., Levy, A., Oppenheimer, C., Valters, C., Lee, P., et al. (2017). Bridging the Gap: How Real-Time Data Can Contribute to Adaptive Management in International Development. USAID.

Schulte, R. (2016, June 8). Real-Time Analytics: Six Steps For Fast, Precise Decision-Making. Retrieved June 18, 2020, from Forbes: https://www.google.com/amp/s/www.forbes.com/sites/gartnergroup/2016/06/08/real-time-analytics-six-steps-for-fast-precise-decision-making/amp/