Predictive Analytics : A Gold Mine

The increasingly popular terms “big data” and “predictive analytics” have become so widely discussed that, in many ways, they lack a cogent meaning – even within the financial industry. The Capstone team used the literature review as an opportunity to scan the field for defining characteristics, resulting in a suggested guide for how the Federation should articulate these concepts. Generally speaking, big data is about the growing availability of data, as well as the tools needed to analyze the information. Predictive analytics refers to the analysis of big data which can help uncover patterns, correlations and trends that were otherwise unknown to the organization. Predictive analytics is also known as

Having data is not valuable but using data is. Analytic insights are changing the way corporates strategize and also redefining customer expectations. Analytics is the new differentiator between success and failure in the cut throat e-commerce and internet services based industry. The huge proportions of data generated from the increasing number of smart phones, the social networks and the ever more penetrating internet are automating customer centric marketing and other services. The idea is to predict what a customer may want to buy even before the customer realizes what they need. The techniques to achieve these results are broadly classified as Predictive Analytics.

The analytics team could then start to analyze the data using data mining and business intelligence techniques. All three types of business analytics: descriptive, predictive, prescriptive analytics techniques should be utilized. The goal of the analysis would be to look at indicators and correlations that lead to incidents occurring and try to determine ways to help prevent these occurrences in the future. Identify proactive ways to change behaviors and actions will be important.

The need to find a way to handle big data leads to data mining. Most researchers defined data mining similarly to Swain (2016). Swain described data mining as using technological processes to analyze big data and find unsuspected relationships among variables for future use. He continues to state that data mining technologies can find value from billions of gigabytes of data gathered from various sources. Huang, Lu, & Duan (2012) add to Swain’s definition by noting that, as opposed to typical statistical studies, data mining uses computational methods that allow the study to look

With “big data” and analytics being a hot topic as of late, drawing headlines like “The Sexiest Job of the 21st Century: Data Analyst” (Morris, 2013), it’s clear that this isn’t just a fad passing through. Data analytics has been around for a while now, but it is finally getting the recognition in deserves by many businesses. More businesses are seeing the value in using their data to make better business decisions, and one method of doing that is through using predictive analytics. According to Teresa Meek, “Predictive analytics can improve retailers’ pricing, inventory control, customer service, and ultimately, their bottom line” (2015). This paper is going to focus on looking at the different types of predictive analytics in relation to the retail industry. After highlighting the techniques and different software available to do predictive analytics, different retail businesses will be examined on how they are currently using predictive analytics as well.

With the increased and widespread use of technologies, interest in data mining has increased rapidly. Companies are now utilized data mining techniques to exam their database looking for trends, relationships, and outcomes to enhance their overall operations and discover new patterns that may allow them to better serve their customers. Data mining provides numerous benefits to businesses, government, society as well as individual persons. However, like many technologies, there are negative things that caused by data mining such as invasion of privacy right. This paper tries to explore the advantages as well as the disadvantages of data mining. In addition, the ethical and global issues regarding the use of data mining

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)

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.

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

All in all, the notion of predictive analytics in a more advanced manner would probable change the way e-platforms work. Few of the currently implemented predictive analytics examples.

In today’s complex business environment, the field of data analytics is growing in acceptance and importance. It is playing a critical role as a decision-making resource for executives, especially those managing large companies. To shed more light on how companies are taking advantage of analytics, “Deloitte Analytics” commissioned The Analytics Advantage, the first in an annual series of surveys focusing on the state of analytics readiness at leading corporations and what the future holds.

This group project gives a report on data analytics. It consists of definitions of data analytics. It also defines Business Intelligence and explains the difference between Business Intelligence and data analytics. It also examines benefits, challenges and the implementation challenges of data analytics. In addition this project Analyze the means of Data Analytics to provide a competitive advantage to businesses.

Data analysis and predictive modeling with all its scope, has already reached to the very center of our operative organization. Currently, to improve the efficiency of operations, all companies, government bodies, institutions or industries are making decisions guided by predictive stats. In addition, with more data, enhanced hardware, increased knowledge, and analytical advancement, we see extensive promise towards predictive analytics. For example, predictive analytics based on our biometric values differentiate from an imposter while starting engine of our car, Music app plays song depending upon your mood level, etc. Of course, these specifics are just speculations; predictive analytics yet need to be explored fully. However, we are confident to see growth and improvement in the field.

The market is witnessing an unprecedented shift in business intelligence (BI), largely because of technological innovation and increasing business needs. The latest shift in the BI market is the move from traditional analytics to predictive analytics. Although predictive analytics belongs to the BI family, it is emerging as a distinct new software sector.

Data mining is the process of automatically discovering useful information from large data repositories [2]. The rapid advances in technologies led to accumulation of vast amount data. As the number of organizations grows day by day the breeding of new technologies and new styles of data also increases. It is difficult to handle them and store them in an efficient manner and of course retrieval of data is extremely challenging [3]. Nowadays we cannot use traditional methods to explore the data analysis because of the size of dataset. So it is very important to make study on data analysis. Most of the technologies are blended with data mining or we can say that data mining is vital and indispensible concept for every technology. Traditional machine learning algorithms like decision trees or artificial neural networks are examples of embedded approaches [9][10].Data mining tasks are mainly divided into predictive and descriptive. Predictive refers to predict the particular attribute based on other attributes. Descriptive task is to derive patterns like correlations, trends, clusters, trajectories and anomalies. Association analysis is used to discover patterns from correlated data and the output of analysis is represented using implication rules. Cluster analysis is the method of partitioning the datasets into different clusters and each cluster data is strongly correlated with intra-manner and inter clusters shows strong repulsion. There are several clustering methods but it