Data Mining: What is Data Mining? Overview Generally, data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information - information that can be used to increase revenue, cuts costs, or both. Data mining software is one of a number of analytical tools for analyzing data. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Continuous Innovation Although data mining is a relatively new term, the technology is not. Companies have used powerful computers to sift through volumes of supermarket scanner data and analyze market research reports for years. However, continuous innovations in computer processing power, disk storage, and statistical software are dramatically increasing the accuracy of analysis while driving down the cost. Example For example, one Midwest grocery chain used the data mining capacity of Oracle software to analyze local buying patterns. They discovered that when men bought diapers on Thursdays and Saturdays, they also tended to buy beer. Further analysis showed that these shoppers typically did their weekly grocery shopping on Saturdays. On Thursdays, however, they only bought a few items. The retailer concluded that they purchased the beer to have it available for the upcoming weekend. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could make sure beer and diapers were sold at full price on Thursdays. Data, Information, and Knowledge Data Data are any facts, numbers, or text that can be processed by a computer. Today, organizations are accumulating vast and growing amounts of data in different formats and different databases. This includes: operational or transactional data such as, sales, cost, inventory, payroll, and accounting nonoperational data, such as industry sales, forecast data, and macro economic data meta data - data about the data itself, such as logical database design or data dictionary definitions Information The patterns, associations, or relationships among all this data can provide information. For example, analysis of retail point of sale transaction data can yield information on which products are selling and when. Knowledge Information can be converted into knowledge about historical patterns and future trends. For example, summary information on retail supermarket sales can be analyzed in light of promotional efforts to provide knowledge of consumer buying behavior.
CarMax faces challenges from several fronts that could threaten to disrupt their growth plans and their position as a disruptor in the used car market. The biggest challenge they face is being able to continuously secure a study supply of high quality used cars, due to the extremely competitive nature of the used car market. CarMax offers cutting edge technology to help the company identify buying trends, pricing trends, and consumer preferences down to the zip code that gave them a large competitive advantage, as “data mining” has matured and competitors have developed their own software tools, eroding the competitive advantage to CarMax.
There are two types of data. They are unstructured and multi-structured. Unstructured data comes from information that isn’t organized or easily interpreted by traditional databases or data models. This is usually in text format.
Big Data is changing the arena for big businesses. Big Data is the technology trend that has made it possible for businesses to better understand their markets. Big Data is the new natural resource, the new “oil.”
A database is a structured collection of data. Data refers to the characteristics of people, things, and events. Oracle stores each data item in its own field. For example, a person's first name, date of birth, and their postal code are each stored in separate fields. The name of a field usually reflects...
One of the forecasting techniques typically used by organizations is the historical analogy. Chase et al. (2005), define that historical analogy "ties what is being forecast to a similar item" (p. 514). This technique is used when the company is planning to launch a new product to market. Since there is no data available for the new product, the organizations try to compensate the uncertainty by using data from product with similar characteristics. Similarly, the market research technique also uses data collection to forecast demand. The data collection is primarily done through direct surveys and interviews. Companies use this technique to be able to come up with better products than the existing ones. The uncertainty of what customers want or dislike is reduce by collecting data directly from them. It is common for organizations to hire external companies to conduct this investigation and to provide the forecast. Since the external organizations are solely dedicated to the forecasting business; they usually provide adequate and accurate information.
This method is something that many organizations are relying upon today. In fact, well over 30% of organizations state that they rely on data analysis for the majority of their marketing
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
Data mining has emerged as an important method to discover useful information, hidden patterns or rules from different types of datasets. Association rule mining is one of the dominating data mining technologies. Association rule mining is a process for finding associations or relations between data items or attributes in large datasets. Association rule is one of the most popular techniques and an important research issue in the area of data mining and knowledge discovery for many different purposes such as data analysis, decision support, patterns or correlations discovery on different types of datasets. Association rule mining has been proven to be a successful technique for extracting useful information from large datasets. Various algorithms or models were developed many of which have been applied in various application domains that include telecommunication networks, market analysis, risk management, inventory control and many others
Currently, businesses want to use the information effectively for competitive advantage to make better decisions that improve and optimize business processes, predict the market dynamics accurately, optimize forecasts to adequately maintain resources to name a few reasons.
HAND, D. J., MANNILA, H., & SMYTH, P. (2001).Principles of data mining. Cambridge, Mass, MIT Press.
Big data will then be defined as large collections of complex data which can either be structured or unstructured. Big data is difficult to notate and process due to its size and raw nature. The nature of this data makes it important for analyses of information or business functions and it creates value. According to Manyika, Chui et al. (2011: 1), “Big data is not defined by its capacity in terms of terabytes but it’s assumed that as technology progresses, the size of datasets that are considered as big data will increase”.
These databases allowed companies to better understand what customers were buying regularly, what they spent and what they did. (Ruchi, 2014) The earliest definition of this was "Database Marketing is an interactive approach to marketing, which uses the individually addressable marketing media and channels (such as mail, telephone and the sales force): to extend help to a company 's target audience; to stimulate their demand; and to stay close to them by recording and keeping an electronic database memory of the customer, prospect and all commercial contacts, to help improve all future contacts and to ensure more realistic of all marketing."
One of the first steps to becoming a competitor is the widespread use of modeling and optimization. Instead of following basic statistical information, it is wise to look for ways to enhance profitability. To become successful at this, organizations use both internal and external information retrieved from outside sources for a vivid understanding of their consumers. Secondly, an enterprise approach is necessary. Through this approach, employees become proactive at finding out what items or processes are effective.
The dynamics of our society bring many challenges and opportunities to the business world. Within the last decade, hundreds of jobs have emerged particularly in the technology sector to help keep up with the ever-changing world and to compete on a larger and better scale than the competition. Two key job markets and the basis of this research paper are business intelligence or BI and data mining or DM. These two fields play a very important role in small to large companies and are becoming higher desired sectors within the back offices of the workplace. This paper will explore what the meaning of BI and DM really is, how they are used and what we can expect as workers and learners of the technology and business fields for the future.
Humans can expand their knowledge to adapt the changing environment. To do that they must “learn”. Learning can be simply defined as the acquisition of knowledge or skills through study, experience, or being taught. Although learning is an easy task for most of the people, to acquire new knowledge or skills from data is too hard and complicated for machines. Moreover, the intelligence level of a machine is directly relevant to its learning capability. The study of machine learning tries to deal with this complicated task. In other words, machine learning is the branch of artificial intelligence that tries to find an answer to this question: how to make computer learn?