Introduction 2. Applications of Big Data, Data Mining and Predictive Analytics For the development of Big Data, Data Mining and Predictive Analytics applications, several methodologies and techniques routed to the control and post-analysis of info-data have been generated in different fields. Those methodologies and techniques allow a better use of info-data to solve a specific problem. Some fields, in which Big Data has developed, both in public and private, are health and science, economics, and business and management. Taking these into account, we can define and classify the following applications: Figure1. Applications of Big Data, Data Mining and Predictive Analytics 2.1 Marketing and Sales Application The tools of data warehousing, Data Mining and customer relationship management (CRM) have enhanced businesses’ ability to create and build relationships with customers. For sales managers, Data Mining can evaluate sales performance by product types, distribution channels, and geographical regions. Combined with other variables, such as demographics or buying behavior, this data can also be used to predict which products are likely to perform well in certain markets (Hair, 2007). For retailers, Big Data is derived from many sources: points of sale, radio frequency identification (RFID) devices, online clickstream patterns, etc. With the help of Predictive Analytics models, this data becomes useful for various decisions in inventory control, store layout, merchandise assortment, and so on (Hair, 2007). For advertisers, Data Mining can turn into a valuable tool with the emerging new media of internet, blogs, podcasts and search ads (as opposed to the traditional media, such as television, radio, or newspapers). The incre... ... middle of paper ... ...r and Fawcett, 2013). 2.4 Services Operation Analytic Applications Big Data, Predictive Analytics and Data Mining have other important applications that do not embody direct impact over managerial strategy in a company; nonetheless, they represent a significant tool in society. These include the successful use of Big Data in astronomy (e.g., the Sloan Digital Sky Survey of telescopic information), politics (e.g., a political campaign focused on people most likely to support a candidate based on social networks or web searches) (Murdoch and Detsky, 2013), and education, where Data Mining offers educational institutions additional approaches to improve graduation rates of students, students' success and learning outcomes, through prediction, cluster analysis, association and classification by info-data informatics tools (Beikzadeh, Phon-Amnuaisuk, and Delavari, 2008).
RBC Financial Group uses a customer relationship management (CRM) strategy that provides a variety of services for a variety of clients. The strategy allows for individual customers to trust RBC and develop a personal relationship with each and every client. One major factor that allows CRM to operate effectively is the use of technologies and analytics to help classify each client’s financial situation. These customer profitability-based techniques allowed RBC to categorize their clients into A, B, and C groups so that the sales teams could optimize their efforts in catering to these different clients. This strategy holds the following strengths: optimizing sales efforts to different customers, easily accessible electronic sales leads, centralized and standardized financial decisions, and building personalized and sustainable customer relationships. There are a few weaknesses to the system though including the complexity in predicting future positions of companies despite the use of analytics as well as the complexity in creating consistency when using these
According to Lisa Arthur, big data is as powerful as a tsunami, but it’s a deluge that can be controlled. In a positive way it provides business insights and value. Big data is data that exceeds the processing capacity of conventional database systems. It is a collection of data from traditional and digital sources inside and outside a company that represents a source of ongoing discovery and analysis. The data is too big, moves to fast, or doesn’t fit the structures of the database architecture. Daily, we create 2.5 quintillion bytes of data. In the last couple years we have created 90% of data we have in the world. This data comes from many places like climate information, social media sites, pictures or videos, purchase transaction records, cell phone GPS signals, and many more places. From the beginning of recorded time through 2003 users created 5 billion gigabytes of data. 2011, the same amount was created every couple days. 2013, we created that same amount every ten minutes. Some users prefer to constrain big data into digital inputs like web behavior and social network interactions. The data doesn’t exclude traditional data that is from product transaction information, financial records and interaction channels.
In all, there is an increasing use of big data by a number of different organizations, industries, and people. Many governments, researchers, scientists, and businesses are using big data to obtain information to further their goals. This obtainment of information by these parties is deemed by many as controversial and detrimental to the privacy of individuals. The debate about the use of big data will become more important as more and more information is gathered from a variety of sources. As the more data is collected the less private individual information will be. The topic of big data will grow in popularity as technology advances and more and more data becomes available to a number of different parties.
Consideration of advances in big data technology has shown that it has potential to enhance the government’s analysis capability in areas such citizen-centric service delivery. It is evident that big data also provides insights into social networks and relationships as well as allowing for the development of predictive models for a number of applications. Of interest more broadly to agencies, big data analysis may provide profound insights into a number of key areas of society including health care, medical and other sciences, transport and infrastructure, education, communication, meteorology and social
Big Data is a term used to describe the large volume of data whether structured or unstructured that inundates a given operation on a daily basis (http://www.SAS.com). Big Data consists of data sets that are so huge and complex that the customary data processing applications would not adequately handle them. Of late, the concept of Big Data has been used to describe the use of predictive analysis, user behaviour analytics and other complex data analytics techniques for the extraction value from data. The concept of Big Data can be understood through the description of the three V’s as advanced by Doug Laney, who is an industry analyst. First, Big Data can be understood in terms of Volume, whereby organizations collect large data from a variety
Predictive and Text Analysis on Big Data – Being able to forecast data and analyse critical information for the company.
Every interaction your company has with a customer or supplier likely generates a data trail and this data provides a wealth of information for marketers. Extracting that information and getting it into usable shape requires sophisticated data mining tools. One example of this technology is the used by police departments to identify patterns in crime. We will define, explain and discuss main aspects of data mining. Also its benefits and negative issues.
Data mining is a field that is a combination of numerous other fields such as the database research, artificial intelligence and statistics. Data mining involves looking for patterns in vast amounts of data as a part of knowledge discovery process. (Huang, Joshua Zhexue, Cao, Longbing, Srivastava, Jaideep, 2011) contains numerous papers that are solely dedicated to discussing the advancements that have been made in the field of data mining and knowledge discovery. A lot of people have performed a thorough research on all that has been done in data mining and the future possibilities that are soon to be implemented practically. The research not only covers the history and the reasons that led to various advancements being made but they also cover the detail models of the proposed solutions to deficiencies in existing systems.
There are various kinds of definitions about what data mining is. The authors in [1] define data mining as “the process of extracting previously unknown information from (usually large quantities of) data, which can, in the right context, lead to knowledge”. Data mining is widely used in areas such as business analysis, bioinformatics analysis, medical analysis, etc. Data mining techniques bring us a lot of benefits. Business companies can use data mining tools to search potential customers and increase their profits; medical diagnosis can use data mining to predict potential disease. Although the term “data mining” itself is neutral and has no ethical implications, it is often related to the analysis of information associated with individuals. “The ethical dilemmas arise when data mining is executed over the data of an individual” [2]. For example, using a user’s data to do data mining and classifying the user into some group may result in a variety of ethical issues. In this paper, we deal with two kinds of ethical issues caused by data mining techniques: informational privacy issues in web-data mining and database security issues in data mining. We also look at these ethical issues in a societal level and a global level.
Understanding consumer behaviour allows companies to engage in relationship marketing techniques. Relationship marketing is a key element in E-CRM strategy. There are many tangible benefits to a company; reduces price sensitivity, there is a lower need for price discounting and it creates market referrals.
THURAISINGHAM, BHAVANI. (2003). Web Data Mining and Applications in Business Inteligence and Counter-Terrorism.Taylor & Francis.http://www.myilibrary.com?id=6372.
...ch Reips. ““Big Data”: Big Gaps of Knowledge in the Field of Internet Science.” International Journal of Internet Science 7.1 (2012): n. pag. Web. 16 Mar. 2014.
Companies are beginning to move their CRM application out of data centers and onto the cloud making CRM less expensive and easier to expand. (Shein, 2009) Technology advances are also allowing companies to begin to take better advantage of big data, combing internal data with social media and mobile to deliver more business value. (Goodwin, 2013) In the future, more devices will be connected to the Internet. Cars, buildings, bodies and many other things will be connected through sensors and it is expected that this increase in information will continue to drive the changes in CRM and how it is used to support sales, marketing and customer service. (Sartain,
Big data technology deals noteworthy contributions yet also delivers amazing challenges to World Wide Web growth. The big data exploration progresses have resulted in economic forecasts such as advanced decision-making in key development areas like that of health care, employment, economic throughput, crime, security, and natural disaster and reserve management
Data visualization software also plays an important role in big data and advanced analytics projects. As businesses accumulated massive troves of data during the early years of the big data trend, they needed a way to quickly and easily get an overview of their data. Visualization tools were the solution. When a data scientist is writing advanced predictive analytics or machine learning algorithms, it is necessary to visualize the outputs and monitor results to ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical