Air BnB stakeholder analysis

Real-time data Is often used for navigation or tracking. Continental use real-time data also for flight statistics (where the most valuable customers are while In flight, which ones are affected by delays and cancellations), to support Lorene security efforts, crew scheduling, crew performance, fraud detection and so on. The goal with the real-time warehousing was therefore to solve all those problems and get happy and loyal customers and employees. Some of the goals are listed below: Give employees fast information about the business and customers Flight on time
Take all of the baggage to final destination Easy booking, no overbooking – Increase services What have been shown is that the real-time Blob hasn’t Just improved and completed all of the goals, Continental has even started to use the system in much broader perspective. Make a list of most valuable customers, get knowledge about their trips, what they prefer, if they got good service and if something happening with them in the real-time and in that case give them right and direct service without them to need to ask for it. Customer facing’, near and personal contact with customers example: personal letter and so on) Improve of security because of comparing data in real-time with help of the system, helped FBI with searching after possible terrorists. It also helps to track people who try to track the system. Be almost always on time because of special booking processes, use all capacity of the plains, be always updating about price changes and act from there. Continental has invested approximately $30 million Into real-time warehousing over the last six years.
Of this amount, $20 million was for hardware and software expenses, and $10 million for personnel costs. Although this Investment Is significant, the quantifiable benefits from real-time warehousing are magnitudes larger. Specifically, over the last six years, Continental has realized over $500 million In Increased revenues and cost savings, resulting In a ROI of over 1,000 percent. The data warehousing group made some Important Improvements.

They developed a warehouse architecture that could grow and scalar to meet these real-time and operational need, developed prototypes to show potential end users, to get them excited about data warehousing and to give them ideas about new applications that here was business benefits for each application. They also made that data warehouse operates consistent with organizational culture. The warehouse proved employees with different information so that they can do they Job better and faster. All that changes lead that new project was successful.
Elements of the data warehousing environment at Continental which are necessary to support and extensive end-user Blob application development are: Data exist in the data warehouse from sources that are trusted by end users Help from data warehousing staff is readily available and friendly Metadata is kept current and is easily accessible by end users via the web Users have access to and are trained in tools to access and manipulate data Graphics are used, when appropriate, for data display, making it easier for users to understand and interpret the complicated data being presented Special issues about data warehouse management: Date and time management is amplified because of the finer granularity of data Customized views significantly improve query performance and reduce the load on the data warehouse With the extensive number of on-line, real-time users, views also revived an extra level of security against access to unauthorized data Data loads come in via many different routes and methods, so generalize components to handle data loading are used to save the effort of starting from scratch to develop each new loading process The large volume of constant data loading means that it is not humanly possible to watchful TTL processes, so automated watchdog applications are used to alert data warehouse staff via pagers when their attention is needed for some anomaly Data for loading are put into standardized queues, from which pre- Ritter load utilities pull data for loading into the data warehouse, no matter what the source of the data are There are data loads, tactical queries and strategic queries, each with different patterns of data warehouse use, specific priorities are given to the different types of loads against the warehouse. Priorities also change by type of day. Higher priority is given to queries that require the fewest data warehouse processing resources. I learned ten specific lessons are outlines in the Lessons Learned section. These lessons can be applied to the development of real-time data warehousing in any organization. Blab Britain