The Demand For Data Mining

Technology companies are using big data to analyze millions of voice samples to deliver more reliable and accurate voice interfaces. Banks are using big data techniques to im- prove fraud detection. Health care providers are leveraging more detailed data to im- prove patient treatment. Big data is being used by manufacturers to improve warranty management and equipment monitoring, as well as to optimize the logistics of getting their products to market. Retailers are harnessing a wide range of customer interactions, both online and offline, in order to provide more tailored recommendations and optimal pricing.96

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.

As stated above, data mining is often used to solve business decision problems, “it provides ways to quantitatively measure what business users should already know qualitatively” (Linoff, 2004). A growing number of industries are using data mining to become more competitive in their market by primarily focusing on the customers; increasing their customer relationships and increasing customer acquisition.

Data mining is a very important component in today’s big data [22, 23]. Data mining is essential for everyone from large businesses to government organizations. It helps to identify trends, patterns and make predictions by exploring, comparing, researching and analyzing data.

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

Today with the ever growing use of computers in the world, information is constantly moving from one place to another. What is this information, who is it about, and who is using it will be discussed in the following paper. The collecting, interpreting, and determination of use of this information has come to be known as data mining. This term known as data mining has been around only for a short time but the actual collection of data has been happening for centuries. The following paragraph will give a brief description of this history of data collection.

Companies and organizations all over the world are blasting on the scene with data mining and data warehousing trying to keep an extreme competitive leg up on the competition. Always trying to improve the competiveness and the improvement of the business process is a key factor in expanding and strategically maintaining a higher standard for the most cost effective means in any business in today’s market. Every day these facilities store large amounts of data to improve increased revenue, reduction of cost, customer behavior patterns, and the predictions of possible future trends; say for seasonal reasons. Data

"Data mining is a rather new term for a challenge that has been growing for many years: how to scan very large databases to retrieve the high level conceptual information of the greatest interest" (Lindsay). With the advances in data acquisition and storage technologies, the problem of how to turn measured raw data into useful information becomes a important one. Having reached sizes that defy even partial examination by humans, the data volumes are literally swamping users. For example, large US retail chains now mine their data bases with sophisticated data mining programs to look for general trends and geographic clustering in purchases that are not easily visible in the huge multitude of products and sales.

Data mining simply explained is automated sorting and analyzing of large amounts of data, searching for patterns and correlations. On a

Future Uses of Big Data. Big data is just beginning to evolve. Sooner rather than later it will become something else entirely. Because of this it is important to invest into it now. If a company does not invest into it now, it will risk falling behind, and not being able to catch up.

In order for business to harness big data, we must first look at how big data is created and stored. Computers throughout the world obtain data through their hardware and software. The end results of this collection of data as of 2014 is 11.2 zettabytes. Only one half percent of the 11.2 zettabytes of data is structured and utilized today. This means that most data is not valuable because it is not sorted. A business cannot utilize big data unless it is structured in a way to help a business reach a goal. A way for the data to become useless is through data mining. Data mining is the practice of examining large databases in order to generate new information. This new information is practical and structured.

The hunger for analyzing data to improve delivered needs and to better meet quality measures is spurring a revolution in all the industries like Healthcare, Manufacturing Industry, Insurance Domain, etc. Considering any Industry, the providers are demanding better respective IT systems that allow Information Management and data analytics professionals to filter through large amounts of data and turn it into "information" that can change the business and function of the industry.

Technology has transformed the way companies use data. Companies need more data to streamline their marketing campaigns and target their products for the right audience. But it isn’t just about gathering information – companies also need to be able to use this information and integrate it to their service.

Data mining can be used to model crime detection problems. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. About 10% of the criminals commit about 50% of the crimes. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. One of the most challenging problems facing crime analysts is that of identifying “crime series” which are sets of crimes committed by the same individual or group. Detecting series’ of crime can be an important step in predictive policing, as knowledge of an ongoing pattern can be of paramount importance towards stopping it. This easy to implement data mining framework works with the geo spatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for RAW.

Big data when mined properly can give accurate trends of what products will sell better at what times and to whom these products will sell better to. Big data and data mining when used together properly can significantly boost the decision making and business processes in a business by using management of information systems. Big data can be used for many different things but after it is used and patterns are drawn out of it, the data can be stored in a data warehouse. A data warehouse is a database that stores current and historical data of potential interest to decision makers throughout the company (Laudon, 2013, pp. 225). Databases need to be implemented in companies so they can manage their different customers, sales, suppliers, products, and employees and keep all of their data separate. Databases contain lots of big data but to a human all this data is useless so they need to mine it. Data mining is one of the many complimentary services to big data because it is needed to find patterns and relationships in large databases and