Data Warehousing and Data Mining

Data mining is a very useful business strategy that allows companies to improve their business model and overall

Data mining is another concept closely associated with large databases such as clinical data repositories and data warehouses. However data mining like several other IT concepts means different things to different people. Health care application vendors may use the term data mining when referring to the user interface of the data warehouse or data repository. They may refer to the ability to drill down into data as data mining for example. However more precisely used data mining refers to a sophisticated analysis tool that automatically dis­ covers patterns among data in a data store. Data mining is an advanced form of decision support. Unlike passive query tools the data mining analysis tool does not require the user to pose individual specific questions to the database. Instead this tool is programmed to look for and extract patterns, trends and rules. True data mining is currently used in the business community for market­ ing and predictive analysis (Stair & Reynolds, 2012). This analytical data mining is however not currently widespread in the health care community.

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

Although data mining is still in its infancy, companies in a wide range of industries –

WalMart is pioneering massive data mining to transform its supplier relationships. WalMart captures point-of-sale transactions from over 2,900 stores in 6 countries and continuously transmits this data to its massive 7.5 terabyte Teradata data warehouse. WalMart allows more than 3,500 suppliers, to access data on their products and perform data analyses. These suppliers use this data to identify customer buying patterns at the store display level. They use this information to manage local store inventory and identify new merchandising opportunities. In 1995, WalMart computers processed over 1 million complex data queries.

Worked closely with BAs to gather and provide specific and crucial information through data mining using advanced SQL queries, OLAP functions and PL/SQL Stored Procedures and functions.

Data mining is when a financial analyst gathers consumer information and looks for patterns that a business can exploit. A simplified data mining example is when a restaurant manager knows the local yearly convention schedule based on experience. The manager can cross-reference that information with historical sales results to predict such things as forecasted profit or labor demand. With this information, the manager can estimate an advertising budget or hire temporary staff to handle anticipated work load. When medium to large-sized businesses use data mining, they uncovering these same information points; however, revenue gains can range from millions to billions of dollars. There are several techniques that firms frequently employ to find gold in information.

Data mining allows companies to focus on the more important information in their data warehouses. Data mining can be broken down into two major categories. Automated prediction of trends and behaviors, and automated discovery of previously unknown patterns. In the first category, data mining automates the process of finding predictive information in large databases. Questions that traditionally required exhaustive hands-on analysis can now be quickly answered directly from data. In the second category, data mining tools sweep through databases and identify previously hidden patterns in one step. This category is where the major focus of research has been on.

In this paper, this writer will focus on the evolution of data analytics in business, and has chosen Macy’s as the company to analyze the main advantages and disadvantages of using data analytics. This writer will also discuss challenges businesses must overcome to implement data analytics, and a strategy to help overcome these obstacles. This author will additionally evaluate the overall manner in which data analytics transformed Macy’s regarding customer responsiveness and satisfaction. And lastly, this writer will speculate on the trend of using data analytics for Macy’s in the next ten (10) years, and determine an

All great organizations share one thing in common, the use of business intelligence. Business intelligence (BI) provides tools that revolutionize the way organizations manage business and decision-making. It allows them to transform mass amounts of raw data into reliable information necessary to make important business decisions. BI delivers relevant and reliable information to those who seek it with the goal of achieving better decisions faster. An employee is independently able to navigate through a company’s data and find what he or she needs without relying on others. This means an organization no longer needs to dig through compiled webs of linked spreadsheets, analyze the data manually and mash together reports. Instead, employees can use BI systems to request the specific information that is useful for them (Hitachi Solutions Canada, 2014). BI allows managers to reach the most accurate and contemporaneous information an organization’s database cannot retrieve. The software offers applications for both data analysis and presentation of results. Applications such as data mining and decision support systems allow one to contemplate how he or she wants to analyze the data. Data mining refers to the process of searching for valuable business information in a large database, data warehouse, or data mart. Decision support systems combine models and data in an attempt to analyze semi structured and some unstructured problems with

Data warehousing and on-line analytical processing (OLAP) are key elements of decision support which has primarily become focus on database

The overall goal of the data mining process is to extract information from data sets and transform it into an understandable structure such as patterns and knowledge for further use [3].

The competition between retailers is really intense, which force them to improve themselves to avoid a business failure. Data mining is one of the ways that retailers use to help with improvement. The data mining can be done by having a special program that can track the customers’ purchases. Most retailers use the “membership card” method to collect their customers’ data. Every time a customer purchases goods with the card, the system will collect the purchase history for analyzation. Retailers then use the data “to learn what customers want, understand historical trends, and improve the quality of marketing decisions” (Corrigan, Craciun, & Powell, 2014, p. 160). It is important to retailers to understand the demand from both suppliers and customers so that they can have a better marketing strategy in the future.

The way a business market or service determines their revenue, sustainability, and longevity. In the past business and entrepreneurs advertised to their customers by going door-to-door or if they were financially stable they would pay for an advertisement on television. Unfortunately, advertising door-to-door would limit coverage and commercials pricing may start at 500. Therefore, a new venture was becoming and reaching more customers called data mining. Now all the managers have to do is figure out the statistic of the population they want to advertise too and market to a specific group of people with their previous purchases. They can use data visualization to analyze customer’s preference and patterns.

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.