Applebee’s, Travelocity and Others: Data Mining for Business Decisions
A limited time offer! Get a custom sample essay written according to your requirements urgent 3h delivery guaranteedOrder Now
Q1. What is the business benefits of taking the time and effort required to create and operate data warehouses such as those described in the case? Do you see any disadvantages? Is there any reason why all companies shouldn’t use data warehousing technology? Ans: The business benefits of taking the time and effort required to create and operate data warehouses is to gather information in a rapid time so the business user can make decisions faster. Prior to having data warehouses, large companies had several independent decision support environments that will gather data, clean it up and integrate it from several sources. Each environment will go through the same process and each needed to attain similar data as the other decision support environment in the company. (1) Having data warehouses allows companies to gain insight on subjects they were not concentrating on. For example, Applebee’s was using data warehouses to examine “back of the house performance”, but then got a vision to use the data to analyze the “front of the house performance” to improve customer experiences. Travelocity.com uses data warehouses to mine important data so they can tailor to their customers.
It also allows Travelocity to gain valuable information about early warning signs of an issue so they can address it more rapidly. There can also be some disadvantage for a company to decide to add and operate a data warehouse system. For example, VistaPrint senior manager Dan Malone says it is hard to track the exact return of investment. To implement a data warehouse for data mining will also initially take up a lot of time and high cost. During implementation there could be important data that can go unnoticed because the system is still not fully functional. A reason why all companies should not use data warehousing technology is because of its high cost. Small and medium size business cannot afford to implement the technology. Smaller businesses do not need the complexity of data warehousing when simple reports are sufficient for their line of business.
Q2. Applebee’s noted some of the unexpected insights obtained from analyzing data about “back-of-house” performance. Using your knowledge of how a restaurant works what other interesting questions would you suggest to the company? Provide several specific examples. Ans: Restaurant business is based upon direct customer relations. If any mistakes happen there is no looking back. Unexpected things are more likely to happen in such business because they are performed on the spot with direct contact with its customers. So it’s very important for this business to make its customers happy by living up to their expectations and preparing themselves to face the unexpected outcomes. •How long it would take a customer to leave the seat after getting seated so that the seat could be used by next customer? •How they will manage if they have the special guests in rush hour? •How will they manage to operate if their employees couldn’t make it to the work at last hour? •What if customer is looking for some unknown dish?
Q3. Data mining and warehousing technologies use data about past events to inform better decision making in the future. Do you believe this stifles innovative thinking, causing companies to become too constrained by the data they are already collecting to think about unexpected opportunities? Compare and contrast both viewpoints in your answer. Ans: The main motive of maintaining data warehouses is to use past data and information in future whenever companies faces similar kind of situation. Data warehouses facilitates the future decision making process. Past and future are two different scenarios regarding environment and situation. So it may not always be fruitful to decide about future activities based upon past records. In future there may be the completely different surrounding and condition so decisions they went good in past may not go good in present In spite of all these possibilities it’s always better to have an idea about what has been done about the similar kind of situations in past. Basing up a decision doesn’t mean totally implementing the same decision. Rather than being 100% wrong by predicting what to do, it’s better to be at least 10 % right by taking a clear assumption in mind. If data are kept properly in a systematic pattern then the user can always benefit from it.