Kudler Dimensional Model Hands-on-Project Essay

Define data analytics in general and provide a brief overview of the evolution of utilizing data analytics in business

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.

Data is an important component used by all technology and businesses to gain insights and refine their marketing techniques to improve customer experience. Through the use of data analysis companies can capture a large amount of data, and retrieve information regarding the company’s trends. In Richard’s case using data analytics allows him to gain better insights on customer behaviors, in which he was able to determine which factors influence a user to purchase the eReader early. These factors will allow the team to develop new business models and improve their marketing strategies to capture these individuals (“Big Data Analytics”, 2017). For example, if most of the early users are from California, it would be beneficial to promote the eReader

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.

Regression models are very useful in determination of important statistics in the corporate world. For instance, multiple regression models can be used to determine whether advertising, product loyalty, or price is the most important determinant of business growth. With this information, businesses are able to focus their resources into the channels which will help them achieve their targets effectively. It can also be used to calculate the predicted mileage for a vehicle with respect to different possible variables such as weight of vehicle, age of vehicle, and climate of country. Car manufacturers can capitalize on

For years, companies have been relying on market research, data tracking, and data warehousing to help create marketing strategies. Marketing managers rely on this data to spot opportunities and problems and try to stay ahead of the competition.

With “big data” and analytics being a hot topic as of late, drawing headlines like “The Sexiest Job of the 21st Century: Data Analyst” (Morris, 2013), it’s clear that this isn’t just a fad passing through. Data analytics has been around for a while now, but it is finally getting the recognition in deserves by many businesses. More businesses are seeing the value in using their data to make better business decisions, and one method of doing that is through using predictive analytics. According to Teresa Meek, “Predictive analytics can improve retailers’ pricing, inventory control, customer service, and ultimately, their bottom line” (2015). This paper is going to focus on looking at the different types of predictive analytics in relation to the retail industry. After highlighting the techniques and different software available to do predictive analytics, different retail businesses will be examined on how they are currently using predictive analytics as well.

What is data analytics? How has its use in business evolved over time? What are the advantages and disadvantages of using data analytics within a specific company or industry? Are there any challenges or obstacles that business management must overcome in order to implement data analytics? If so, is there a strategy that can be used to overcome those challenges or obstacles? How has data analytics transformed the healthcare industry with regard to customer responsiveness and satisfaction? Within the next ten years, what do you think the trend in using data

Design, code and deliver user friendly multi-tier business intelligence solutions that utilize data warehouse/data mining technologies to consume data across various database platforms and data stores.

In this paper I am going to share my last company NTT DATA GLOBAL DELIVERY SERVICES LTD’s tactics, strategies, operations and management and its usage of analytics in the global market to achieve its goals.

Data mining can aid direct marketers by providing them with useful and accurate trends about

To compete effectively in an era in which advantages are ephemeral, companies need to move beyond historical, rear-view understandings of business performance and customer behavior and become more proactive(tableau). Predictive Analytics is the use of data science for audience profiling. Generic audience profiling involves determining specific characteristics of your target audience and creating specific personas to represent each type of person within your target audience. Predictive analytics is essentially the same process, but from a data perspective (koozai). Predictive analytics can be used in wide areas in the industry, it’s importance is not constrained to a particular domain and ranges from marketing, telecommunication, retail, banking, etc. For example, the telecommunication industry has noticed a high customer churn since the switching costs are slim to none. So telecommunication companies operating in this industry are looking for new ways to differentiate themselves from competitors in order to retain customers. By using predictive analytics as a solution to this problem, they would be able to understand the customer needs, requirements and retain them also allowing them to acquire new ones more effectively. With predictive analytics, companies can predict trends, understand customers, improve business, drive strategic decision making and predict behavior. A company named Cox Communications, the third largest cable entertainment and broadband

Business analytics plays a major in sourcing new customers in emerging markets. Target has over the years, employed business analyst that mine customer data for shopping habits and use the data observed to design advertising and marketing schemes that can

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

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.