Introduction Of Data Mining I - Cis 508

We are at the forefront of an information revolution. Firms are increasingly relying on data to drive strategic and business decisions. Upstart companies are giving market leaders a run for their money by tapping into advanced data analytics, statistical modeling and open source software. Multi-billion dollar companies like Uber and Airbnb have shown a groundbreaking data-driven way to connect its products & services to their customers. We are living in a world where every customer touch point with a digital device is being recorded and monetized. Every business has to reinvent, transform, and adapt its products and services to meet the demand of its digital savvy customers or face the risk of becoming obsolete.

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

The book is all about the things that make one to be a smart consumer of cutting-edge analytics, facilitating to frame the judgement, questioning about the information and the procedure, operating to comprehend the consequences, and using them to progress results for his or her business. Even though it sounds direct, I directly acknowledged it as a deceptively single-minded set of purposes of the book. The writers planned the first half of the book about an analytics outline that entails of six stages: problem acknowledgment, evaluation of previous results, displaying, data assortment and data examination, and outcomes demonstration and action. This planned method to discerning about analytics is one of the most important notions that Davenport

Technology is another segment that will have a major impact on Best Buy in the near future. Predictive analysis, social media, and multichannel retailing are trends that are most important in Best Buy’s general environment. Knowing your customers is a critical part to any industry, and predictive analysis is becoming one of the best ways to accomplish that objective. By correctly using technology to control the high volume and availability of data, companies can understand customers’ wants. A core competency of both Amazon and Netflix predictive analysis; based on previous purchases or views

An example of how data mining is conducted and used to benefit business can be explained in the following scenario:

In an uber globalized market of today, companies are faced with challenges in each and every step of their business. Our analytics and research services are geared towards giving those companies that extra edge over the competition. We process and analyze terabytes of data and break down all the fuzz and chatter around it to give our customers meaningful insights about their competition and the market they are engaged in.

Web analytics are profitable for understanding customers as organizations can obtain points of interest with reference to

Secondly, predictive analytics not only use statistical algorithms and forecasts on historical data but also anticipate likely situations to plan ahead. The means of these predictive models differs depending on their behaviour and future events but the aim is to go beyond reporting what has happened in providing the best assessment of what will happen. Data mining is an important component that tends to identify trends, patterns and relationship. They largely depend on statistical models and multivariate analysis techniques. Thereby decision making can be streamlined and new insights can be revealed leading to better decision making. Organisations are adapting to predictive analytics for competitive advantage. They are used in identifying trends, understanding customers, enhance business performance, predict behaviour and drive decision making. (SAS and Nyce)

Given how turbulent global economies are and the industries that compete within them, it is understandable to see analytics and Big Data continually increase in popularity throughout enterprises globally. The greater the level of turbulence in economic conditions, the more reliance on technologies, processes and systems that are adept at mitigating risk. The continual investment in analytics is setting a solid foundation for completely redefining how businesses manage the decision making process. It is also changing forever how businesses manage customer expectations relative to experiences, and how these factors are all combined to manage the new product development and introduction (NPDI) process.

There is a lot of hype recently around big data and its potential. When leveraged correctly big data can provide many great benefits for organizations in almost any industry. Some organizations currently have a lot of big data they have gathered throughout their life but have no current way of leveraging this data. One of the ways companies are able to leverage all of their big data is through predictive analytics.

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

Data is being produced at a huge rate and 90% of the data which exists today were produced in the last two years. Thus, it is difficult to manage big data which are extremely large, structured/ unstructured data sets analyzed to find trends, associations, reports, etc. The biggest challenge today is to find the quickest and the most inexpensive tool to analyze the big data which consists of emails, videos, pdf, audio files, and tweets. Predicting future with being able to access and store real time data is the future of BI and big data analytics (Jamack, 2012). When BI reports are run using the data and queries, information is retrieved and it is called Descriptive Analytics. When the dataset is further analyzed and drawn inferences using statistical methods like correlation or regression then it is called Diagnostic Analysis. Based on this information when the possible outcomes are predicted it is called Predictive Analytics. Finally, Predictive analytics uses previously tested or predicted models which are put into a reiterated process to produce an anticipated outcome. Big data technology combines all of these analytics, along with being fast and efficient in handling real time data (Payandeh, 2013). Business Intelligence consists of different tools to make better informed decisions with the data they have. Traditional BI were focused on the OO of the OODA loop (Observe, Orient, Decide, Act) but the modern BI needs to directly integrate the Decide and Act since there is a

Predictive Analytics. This type of business analytics is to answer the question “What is likely to happen?”. The process of predictive analytics is identifying past patterns and using statistical models and forecast techniques to understand the future. The output of the predictive analytics, for example is, which age range of customers that will apply for credit cards in the next 12 months, which income range of the customers that will use the electronics banking facilities for next 2 years.