Air BnB stakeholder analysis

real time selective information Is often accustomd for navigation or tracking. Continental mapping real time selective information as well for flight statistics (w present the closely valuable customers argon while In flight, which ones be affected by delays and raisecellations), to support Lorene trade protection efforts, crew scheduling, crew performance, fraud detection and so on. The goal with the real-time wargonhousing was therefore to solve on the whole those problems and get happy and trusty customers and employees. Some of the goals are listed below Give employees fast information intimately the business and customers Flight on timeTake all of the baggage to final termination Easy booking, no all everyplacebooking Increase services What have been shown is that the real-time make out hasnt 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 knowle dge about their trips, what they prefer, if they got good service and if something casualty with them in the real-time and in that case give them right and rent service without them to need to ask for it. Customer facing, near and personal converge with customers example personal letter and so on) Improve of security because of canvass selective information in real-time with help of the system, helped FBI with searching after realizable terrorists. It also helps to track people who try to track the system. Be just about always on time because of special booking processes, use all capacity of the plains, be always updating about price changes and play 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 lar ger. Specifically, over the last six years, Continental has realized over $ d 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 positive a warehouse architecture that could grow and scalar to meet these real-time and operational need, developed prototypes to show potential closing curtain users, to get them demented 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 unvarying 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 contrive was successful.Elements of the data warehousing environment at Continental which are inevitable 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 uncommitted and friendly Metadata is kept current and is easily findible 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 substantiate and interpret the complicated data being presented Special issues about data warehouse management Date and time management is amplified because of the finer nubbiness of data Customized views significantly improve query performance and reduce the consign 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 freightage are used to however the effort of starting from scratch to develop each new loading process The large volume of constant data loading substance that it is not humanly possible to watchful TTL processes, so automated guard dog applications are used to alert data warehouse staff via pagers when their worry is needed for some anomaly Data for loading are position 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, limited 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