Predictive Analytics Has Positive And Negative Impact

The ability to compete on analytics is made possible by certain qualities some companies possess which allows them to collect and use immense amounts of data in a way that differentiates the success and practices of those companies amongst any other businesses. Davenport and Harris (2007) define analytics as “extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions” (p. 7). Therefore, to be able to compete on analytics, a firm must not only use the data to extrapolate and execute strategies and models in order to drive business, but also to use that data better and smarter than their competitors. This requires forward thinking and continual developments of current analyses and practices. With regard to Davenport and Harris’s criteria and concepts on the ability to compete on analytics, Old Navy LLC’s practices will be analyzed to find whether the company is able to compete, is a competitor, and how it competes, if at all.

In the past, leaders often relied on their intuition and pursued a hypothesis driven approach to strategic decision making. Field of data science has entirely shifted this paradigm. The advent of machine learning and pattern recognition techniques, in conjunction with the growth of cloud storage and parallelized computational capabilities has given business leaders enormous flexibility to boil the ocean and make decisions entirely based on data.

The data analytic process is one in which a large amount of information is collected using software specifically geared towards collecting, identifying and storing information for use by the company. The information is gleaned from different forums, with social media being the most rich and useful. The information is then quickly sorted and organized for use by the collecting agency (Turban, Volonino, Wood, & Sipior, 2002, p. 6). The use of data analytics really took flight in 2010 when different companies offered software that enabled a company to implement their own data analytics. This led to better marketing campaigns, improved customer relations and it gave companies using the software a bigger advantage over their competitors (Savitz, 2012).

The concept of analytics-based decision marking takes into consideration industry knowledge and business analytics which includes four acts (Bartlett, 2013). The four acts help prepare analytics to support an anticipated decision and illustrate a process for analytics-based decision making. As a result, the role of statistical thinking, the interaction, and relationship between industry knowledge and analytics, as well as where things can go wrong in a real business problem can be revealed (Bartlett, 2013). In addition, moving from one act to another seamlessly, jumping back from and forth from one act to another or simply following some other route is easy to do in a real business problem. This annotated bibliography will examine articles that contain resources that support the concepts contained in the four acts.

Forecasting analytics will enable SYSCO to make appropriate upfront decisions and monitor customers as well as the industry. Extraction and data mining are also useful tools that will positively affect SYSCO’s decision-making process. Lastly, consulting support and employees’ training will facilitate the implementation of the new software in the company. For all these reasons, the use of BI at SYSCO can create a competitive advantage of the company in the industry. However, this competitive advantage depends on the competition – do the competitors use a similar software or by chance the same and do they already have a strong position in the market? Outperforming for example “U.S Food Service”, SYSCO’s main competitor, might be arduous if that company relies on a similar software and already has an eminent role in the

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 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

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die is a

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

Predictive Analytics is an important requirement for our Industry and LOB applications, and BI solutions. It is a significant market - $2.0B 2014 (IDC), in which we have a minor presence.

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

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.

In the recent years it has been a trend to use predictive analytics in various fields. The reason to use predictive analytics is to give a lucid view of the

Predictive Analytics: A Gold-Mine Yet To Be Exploited To Its Zenith Akanksha Pandey Information Technology Department, VESIT, Mumbai-74, India. Abstract 1. Introduction

Business thrive when they have the most accurate, up-to-date, and relevant information at their disposal. This information can be used for a plethora of pertinent markers in small and large businesses, relating to accounting, investments, consumer activity, and much more. Big data is a term used to describe the extremely large amounts of data that floods a business every day. For decades, big data has been a growing field, facing controversy on many levels, but as of late, it has been a major innovator in the challenge of making businesses more sustainable. Big data is often scrutinized for its over-generalization and inability to display meaningful results at times. When applied correctly, data analysis can bring earth-altering information to the table.