Table of Contents
1.0 Introduction 3
2.0 Discussion 4
2.1 Background information regarding data mining and knowledge discovery 4
2.2 Impact of the technology on the use of information at Di Stefano cafe 6
2.3 Impact of the technology on the managers 6
2.4 Business strategy implications associated with the technology 7
2.5 Data mining simplified model for knowledge discovery based on OLAP analysis 8
3.0 Conclusions and recommendations 15
Executive summary This report focuses on the various advantages associated with the adoption and implementation of data warehousing and data mining technologies at Di Stefano cafe. Specifically, the adoption of data mining and data warehousing technologies at Di Stefano cafe implies that the managers need extra training on the effective use of the technologies; the cafe will utilize the consumer information to discover knowledge regarding consumption and spending patterns, the consumer data will be utilized in accordance with the stipulated guidelines to ensure data integrity, availability, confidentiality and privacy, and that the use of the technologies will give Di Stefano cafe a competitive advantage in terms of value co-creation and differentiated products. However, in order to effectively leverage on the benefits associated with the data mining and data warehousing technologies, Di Stefano cafe needs to train employees and managers on the new system, develop policies and implement security protocols that will ensure and promote
The theme of this book is how businesses in today’s world use ever-improving technology to collect data, convert it into information and business intelligence, and combine this information and intelligence with the knowledge of the workers to help make the best decisions they possibly can for the benefit of the company and the customers. Throughout the book, there are discussions on the different ways that technology can help a business with this process. When going into detail about the various information systems, this book also brings into
Kulder Fine Foods wants to create a frequent shopper program to help their customers by rewarding them, but also wanting them to be protected by malicious entities that might want to do them harm. In developing a Frequent Shopper Program, Kudler ne Foods (hereby known as Johnson) will need to implement, and adhere, to strict policies and guidelines that will protect the patrons that shop at the establishments of Kudler. We can also use different modeling structures to find what kind of Frequent Shopper Program works best for Kudler. By contacting an outside agency, such as Management Science Associates, Inc., (MSA) we are able to look at data that will tell us the demographics of who is
Scanner data, Loyalty card data, Web Shopping provide bring significant new capabilities to the customer service in the grocery industry. All of these innovations were crucial components for a modern grocery store. They unified by an information system platform which supported the daily running of the entire store. Scanner data was the fundamental of the whole system. All the products in the store will be digitalized
One of the main functions of any business is to be able to use data to leverage a strategic competitive advantage. The use of relational databases is a necessity for contemporary organizations; however, data warehousing has become a strategic priority due to the enormous amounts of data that must be analyzed along with the varying sources from which data comes. Company gathers data by using Web analytics and operational systems, we must design a solution overview that incorporates data warehousing. The executive team needs to be clear about what data warehousing can provide the company.
Kudler Fine Foods (KFF) is a specialty food company that is base in California and has stores located in Encinitas, Del Mar, and La Jolla, its main store. At the stores customers can purchase fine foods such as produce, meat, and other specialty items that they provide. Kudler Fine Foods is considered one of the best fine food stores around. However, Kudler is in serious need of a network infrastructure upgrade of their old one. With the introduction of technology advancements in data collection items such as; network access and increased speeds while still providing information protection; and company communication; are the primary goals of the enterprise network. This step is important because it will increase the income and will reduce the cost of operations within the KFF stores. Kudler Fine Foods will go back up to technological speed as the network upgrade is completed, while at the same time improving the way they keep track of inventory and sales by using data mining techniques, which will be collected and analyzed in real time.
Kudler Foods uses shared databases as a means to increase efficiency and to reduce costs. The use of shared databases creates opportunities for the company to view, manage, and analyze information simultaneously thus providing Kudler with the historical data and forecasting options to consider expanding and increasing profits. Tracking technology, groupware, and workflow software allow Kudler the opportunity to track customer tracking purchase behavior patterns to assist in process refining.
It became an issue for consumer, and they felt uncomfortable due to companies invaded consumers privacy without their consent. Another issue regarding data mining is there no clear policies around how the company uses data once they have collected the information. After Target received negative publicity regarding its original data mining approach, they change their strategy by mixing all their ads, so it not that obvious that they were spying on them. It makes customers’ to assume that everyone else gets same coupons and ads. Data mining has given Target awareness into the relationship between product sales and online reviews. Target retailers identify guests how write reviews, examining their history of purchasing and invited to write review on their products. This program was highly successful, leading Target to obtain incremental reviews and increase sales of those key products. More importantly, data mining has empowered companies to find new opportunities for growth, make better decisions to succeed business goals and safe money. The retailer industry utilizes data mining and customer analytics to support customer decisions. Exploring how collected data can be manipulated to identify customers buying patterns and increase profitability is a growing business
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Having data is not valuable but using data is. Analytic insights are changing the way corporates strategize and also redefining customer expectations. Analytics is the new differentiator between success and failure in the cut throat e-commerce and internet services based industry. The huge proportions of data generated from the increasing number of smart phones, the social networks and the ever more penetrating internet are automating customer centric marketing and other services. The idea is to predict what a customer may want to buy even before the customer realizes what they need. The techniques to achieve these results are broadly classified as Predictive Analytics.
Kudler is looking for ways to increase sales and customer satisfaction. To achieve this goal Kudler will use data mining tools to predict future trends and behaviors to allow them to make proactive, knowledge-driven decisions. Kudler’s marketing director has access to information about all of its customers: their age, ethnicity, demographics, and shopping habits. The starting point will be a data warehouse containing a combination of internal data tracking all customers contact coupled with external market data
The government collects all kinds of useful information about our population. How many people live where, incomes, family sizes, ages, do they rent or own a home, and lots more demographic data that is free for the asking. Modern computer programs make possible for any company to take the masses of demographics and analysis segment populations. This has propelled data mining to the forefront of making customers relationships profitable (Ogwueleka, 2009). This will help Swan understand his customers better and find association between each segment. Customer have life cycle due in part to the time of year, so Swan can now structure his advertising and see results based on a better segment model rather than just counting customers. Data mining can also be used in customer retention applications identifying
Question 1: Assume a base cuboid of 10 dimensions contains only three base cells: (1) (a1, b2, c3, d4; ..., d9, d10), (2) (a1, c2, b3, d4, ..., d9, d10), and (3) (b1, c2, b3, d4, ..., d9, d10), where a_i != b_i, b_i != c_i, etc. The measure of the cube is count. 1, How many nonempty cuboids will a full data cube contain? Answer: 210 = 1024 2, How many nonempty aggregate (i.e., non-base) cells will a full cube contain? Answer: There will be 3 ∗ 210 − 6 ∗ 27 − 3 = 2301 nonempty aggregate cells in the full cube. The number of cells overlapping twice is 27 while the number of cells overlapping once is 4 ∗ 27 . So the final calculation is 3 ∗ 210 − 2 ∗ 27 − 1 ∗ 4 ∗ 27 − 3, which yields the result. 3, How many
Wal-Mart’s advanced data-mining tools allow them to fine tune and improve customer responsiveness, giving customers what and when they want in offer. It can be compared to
One of the most noticeable features about the City of Seattle is the incredible amount of coffee shops in the area; so it’s not very surprising that one of the most recognizable coffee companies in the world hails from Downtown Seattle. Starbucks has been in business since 1971 and has grown from a single coffee shop to one of the largest and most recognizable food companies in America. One of the things that fostered this explosive growth was the company’s willingness to adapt with a changing world. With technology advancing in leaps and bounds, Starbucks has found ways to utilize it in order to further themselves as a business; their strategy worked. This paper will answer three questions to demonstrate how Starbucks has successfully used Information Systems (IS) to grow themselves into the company they are today. The correct understanding, implementation, and management of IS has allowed Starbucks to rise to the top of their industry and distinguish themselves from other businesses.
Since higher education has blurred the lines with traditional businesses, it is important to have the tools to assist them with valuable data and information, in making decisions. Using of data and having the right data mining tools can insure the institute’s success, in many forms, such as, identifying market trends, precision marketing, new products, performance management, grants and funding management, student life cycle management and procurement to mention a few. To get a better grasp on these benefits it’s important to understand data warehouse, data mining and the associated benefits.