Business Systems And Process / Operations Management

Databases are the heart of the company. This is where all crucial company information is stored and can be accessed. Some databases are stored on site others in remote locations or using clouds. The information within a database can be manipulated in any way that the company needs it to be. Databases help to quickly search and retrieve information, it saves from data redundancy.

Second, Database is needed to take the collection of all sorts of sensitive data to organize, analyze, and extract data. It is the heart of many functions in today’s world. For example, when a password or user in a program it is checking the information type in against the information it stored in order to open the software. Databases solve most of the data management problems that are encountered.

A database is used to store collections of information and easily retrieved at a later date. The larger the amount of information, the more organized a database needs to be. A database is created with the requirements and needs of current and future users and most importantly, with past users and their information. Out book defines database systems as “an organization of components that define and regulate the collection, storage, management, and use of data within a database environment”. (Database Systems, 2013)

A database is a collection of data that is organized and is supposed to be organized in such a way that it resembles reality. An example is finding the amount of room on a flight, so you find one with a spare seat available. Databases are supposed to be organized in a way where in the end the users and stake holders that may not have the same knowledge of a database as you, can easily read and

Data mining software allows users to analyze large databases to solve business decision problems. Data mining is, in some ways, an extension of statistics, with a few

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

Data mining uses computer-based technology to evaluate data in a database and identify different trends. Effective data mining helps researchers predict economic trends and pinpoint sales prospects. Data mining is stored in data warehouses, which are sophisticated customer databases that allow managers to combine data from several different organization functions.

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

As explained by (Sharda, Delen, & Turban, 2013), Data Mining “is a term used to describe discovering or mining knowledge from large amounts of data”. Another interesting definition of data mining is “it is the use of the data from the warehouse to discover unpredictable patterns, trends and threats” (Kim, Lin, & Wang, ). Suite Spot has a distinct advantage over its competition, which is the massive amounts of data that we collect about our customers, i.e. people staying in Suite Spot hotels. If we can mine the data, understand customer behavior and then cater to their needs, Suite Spot can develop a competitive advantage over its competition, thereby improving the loyalty of our customers. A simple example of using data mining to turn raw data into information is shown below.

Database is any collection of data or information, that is specially organized for rapid search and retrieval by a computer. Databases are structured to facilitate the storage, retrieval, modification, and deletion of data in conjunction with various data-processing operations.

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.

With rapid advancements in the technology, new concepts are hitting the industry and it is redefining itself over a course of time. The data mining is one of its kind to improvise the lives of people. Data mining uses techniques which are helpful in finding out the different forms of data. The data mining is closely related to the database technology. Almost every industry takes the help of the datamining to grow in their respective fields. For instance, stock management, quality control, risk management, fraud detection, marketing and analysis of investments. It has its applications ranging from finding the molecule structure of the gene to identifying a robbery at an international level.

In today’s business world, information about the customer is a necessity for a businesses trying to maximize its profits. A new, and important, tool in gaining this knowledge is Data Mining. Data Mining is a set of automated procedures used to find previously unknown patterns and relationships in data. These patterns and relationships, once extracted, can be used to make valid predictions about the behavior of the customer.

As computer storage capacities increased in 1980s, many companies began to collect more transactional information. The resulting record collections, often called data warehouse. These companies used computer science to explode their data warehouses, and attempt to learn the behaviour patterns of their clients. One of the earliest successful applications of data mining was credit-card-fraud detection. Through learning a consumer’s buying behaviour, a typical pattern usually becomes apparent; By a data collection of a long period of time and a large population, it would be not difficult to find distinction between normal and fraudulent behaviour works.

From figure 5 and 6, data mining techniques compliment with queries helps to find hidden relationships in the data marts in order to find customer preferences and suggest customized advertisements (Faria 2012, p. 255-256). As a result of acquisition, expansion and business nature, Almost 21 requires to do substantial analytical works in order to source complex data relationships within different customer segments such as the relation of spending behavior against age groups and the relation of product sales against age groups. Therefore, BI applications aims to help supply chain planners to implement product mix decisions which refers to the process of choosing the right product options to the customers for sale (Chowdhury & Das 2012, p. 219). Additionally, BI applications advance the performance of Almost 21’s SCM and CRM. From a SCM perspective, the transparency of data, the availability of data mining and OLAP in data warehouse help to construct a more customized product and strengthen the supplier-suppler relationship. Hence, the BI applications facilitate the coordination and decision-making process in the supply-chain network, which in turn benefits the end-users. For example, Zara is a successful firm which had short lead times for new fashion idea because of its comprehensive data analysis, design and product management (Walter 2013). Furthermore, the BI applications help companies to