Predictive Analytics : Predictive Analysis

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

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)

Utilizing different techniques for data mining is extremely important for what may work for ne may not work for the other. With the

Business intelligence can provide companies with accurate data that can be analyzed to support business strategy therefore enabling companies to better predict effectiveness of their business goals and ultimately result in a business profit. “Business intelligence is already in use in many organizations today, by finance departments to analyze financial performance, sales and marketing to identify customer trends, and operations to enhance the efficiency of supply chains. Using real data helps them answer who, what, where, why and when of related performance” (Rylander,2009).

The company I am with is beginning to use analytics more and more. We are on step 1 of the five step process, so we still have a lot to learn and set goals on. We have just started using them so that we can compete with the “top dogs” of the bank data processors. I believe that if we didn’t use analytics one of the big competitors would come in and try to buy out the company. We are looking into ways to help use analytics so that we can strengthen our position in the service and show that we can handle any size community bank. We have created a Business Intelligence department to look into ways that we can improve products, forecast what a bank may buy when signing a contract, and to see how to pitch our regulatory compliance products to other companies to help them with their compliance.

Prescriptive analytics, on the other hand refers to the exploration of these business activities happening in the future. Predictive analytics is used in the exploration of business activities that are more likely to happen in the near future in order to find relationships in the data that couldn’t otherwise be found with the use of descriptive analytics. These different analytics mostly focus on larger data, but can also be used with smaller data as discussed in Chapter 17.

Organizations are dealing with a tsunami of data from businesses, humans, and now machines through the Internet of Things. Generating timely insights and predictions has become crucial to capitalize on fast-moving business opportunities. With business analytics now focused on the future, predictive and prescriptive analytics are taking a front seat. As a Lead Assistant Manager with EXL Services Decision Analytics, I have gained essential skills in model development and monitoring while enabling the business to predict and mitigate losses. I aspire to extend my knowledge on multiple technical and business domains through an intense MS program.

Throughout the semester, we have learned different aspects of Big Data analytics and their practicalities. Forecasting and prediction are another important parts of data analytics. Advanced forecasting analytics is playing a vital role in the age of Big Data, such as predicting crime activities, weather changes, electric power generations, or personalizing marketing campaigns.

Predictive analytics is a technology that makes use of the current and existing database to produce a trend and predict future outcomes. In short, it does not tell you what will happen in the future.

There are four types of business analytics capabilities, descriptive analytics, diagnostic analytics, predictive analytics and prescriptive analytics. A data- driven business will approach maturity when the business can successfully take advantage of all four types of business analytics capabilities in its decision- making processes. A business can become more well-organized, insightful and profitable with

Business Analytics and the other job roles connected to this area has increasing job trend in the last few years, newer and newer jobs appearing in the market, which was not exist 5-8 years ago. Since I am working in this area, and also an interesting area, I would like to introduce this to the audience.I am interested in this topic not only because I am working in this area, also because as a non-statistician person I still could learn many ways of simulating forecasting future events, and in many cases it is fun, or at least interesting that we can solve the same business problem with many different techniques!

“Why Big-Data Is a Big Deal”. Big-data is a logo used to describe a massive volume of both structured (is information already managed by the organization in relational databases ) and unstructured data (is information that is unorganized and does not fall into a pre-determined model) that is so large it is difficult to process using traditional database and software techniques. In most companies, the volume of data is too big or it moves too fast or it exceeds current processing capacity. Despite these problems, big-data has the potential to help companies improve operations and make faster, more intelligent decisions.

Machine learning has been gaining popularity in healthcare because of its ability to use existing mathematical models and apply them to new instances of an established concept in other data. This ability to automatically identify patterns in data is one of the major reasons for the potential of machine learning in healthcare settings—as well as its ability to fill in the gaps of expert knowledge, adjust for exceptions, and efficiently handle massive amounts of data [1].

Analytics, quantitative or statistical models to analyze problems may be applied to a variety of business problems, including customer management, supply chains, financial performance and many other. It is the back bone of any organization which helps in effective decision making. Today, it has creeped into each and every industry and has created a “perish or persist” scenario to the industries.

Advanced analytics makes it possible for planners and managers to know well ahead of time when additional inventory may be needed to maintain delivery timetables or combat production bottlenecks due to new constraints in production programs. Advanced analytic capabilities allow planners to then deploy the necessary container management strategies to ensure the right parts are on the production floor at the right time. Reducing the number of moves a company must make to ensure the fluidity of planned production programs is a core driver in decreasing operational costs and maximizing profitability.