Big Data Predictive Analysis

977 Words 4 Pages
Predictive analytics can be used which allows businesses to extract big data and analyze it to better understand customers. This analysis is a game changer for businesses because it allows them to make predictive models that will help them serve their customers better. This business strategy is important as it provides the data they need to make predictions for the future and it gives them the ability to modify when results are affected. In order to take this strategy into consideration, firms must provide their insights about customers, adapt to business practices and engage with customers. If firms don’t use predictive information to change the future, then they are just wasting their time (Gualtieri & Curran, 2015, p. 2). There are predictive …show more content…
Analyzing it is important because it allows a firm to make better decisions regarding productivity and creating value towards their products and services. It is the foundation of competition, which leads companies to exceed their competitors. They influence on data driven strategies to help them compete and make innovation possible. An example of Big Data can be gathering data from sensors installed in products to conclude how consumers use them. This creates the incentive to create new services and product designs in the future. Big data predictive analytics comes from data mining, which is the use of data to predict future outcomes based past data. This is also a game changer for businesses because it is pretty relevant to use and helps them better serve their customers. Data mining are complex analysis methods to take advantage of the generated data. It is important to provide information on future trends as it contains predictive tools that are useful for managers in a firm. These tools can display their models into applications that need further insights. There are six steps that firm can follow in order to take predictive analytics into account. The first one is identifying data from many sources using the predictive tools to help them explore the data. The second is preparing the data and integrating it into an examining data set. The third is creating a predictive model, which include machine-learning algorithms to run it. A list of data subsets can be used to help run the model. The fourth is evaluating the model to see if it runs accurately by predicting the data subsets. The fifth is deploying the model in applications to deliver insights that are actionable. The last one is monitoring the effectiveness of the model by rerunning new data through the algorithms. In the other hand, Hadoop is needed to be able to process and store all of the

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