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One of the most important reasons of predictive analytics is to learn which is achieved by employing today's most advanced analytics.
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With the help of advanced and predictive analytics future demand can be forecasted with a high reliability and thus alternative business strategies can be assed, which also helps in implementing optimization solutions.
Customers who are most likely to end their contracts are predicted by means of advanced analytics, which takes into consideration an attrition model. Such models assists the companies to cross-sell and in this way customers can be retained by providing services, products, and other targets. So, it is natural that the companies deploying advanced analytics to make out their data becomes successful. Advanced analytics act as an approach which is business focused and involves some techniques for building up of models and simulations, by the help of which realities and future states are understood and scenarios are also created. For the improvement of business performance advanced analytics takes into account approaches like data mining, applied analytics, predictive analytics, statistics, etc. Given a business situation traditional business intelligence has the capability to make us understand and answer where, why etc. However with advanced analytics we can obtain an idea about the likely outcomes and also get deeper answer for the question why. Even if sometimes advanced analytics is not so successful in predicting the future, models are provided by it to judge likelihood of the events. Advanced analytics assists to make an improvement over business decisions. This is because advanced analytics permits companies to remain aware of the outcomes that are going to happen in the near future.
Starting from operational applications, for eg. fraud detection to strategic analysis, for eg. customer segmentation, advanced analytics have a number of applications. For better understanding of business trends and customer behavior advanced analytics provides scores, predictions, descriptions and profiles and thus advanced analytics play a very vital role. It is common to encounter problems when advanced analytics is implemented. Most of the difficulties lie in the utilization and management of the large and vast volumes of data. Nowadays business are storing and gathering more data, much more data than in earlier times. During customer transactions, for supporting marketing, development, and inventory, new data is created. For enlarging existing business data additional data is purchased. In analytics one of the main and vital elements is the amount of data that is stored. More the data, which are analyzed and processed, the better will be the result, better in the sense that the advanced analysis will be more promising in predicting future behavior and also finding patterns.
But, cost of building analytic models increases with the increase in complexity and volume of data. The companies having vast data volumes face a challenge to obtain their data in the form such that the business information can be extracted through it, and this takes place before modeling. In analytic development it takes a large amount of time to prepare the data. In most of the cases, the data is first extracted, after that subset of that data is taken into account for creating analytic data set, and subsets are merged, joined together, transformed and aggregated.
Real time analytics takes the challenge to be implemented. One of the challenges is to reduce the latency between data creation. And this is recognized with the help of analytics processes.