Big Data: More Predictive Analytics

Big Data-A Base For Predictive Analytics

Big Data combined with business intelligence have given birth to the concept of predictive analytics. Businesses collect huge volumes of data these days that may be regarding various amounts of new customers, social listening, markets, clouds, or performance data related to their products. It is through predictive analytics that we are able to gain advantage from this data by either getting insight into this data or using it stay ahead in this very competitive market.

The businesses use predictive analytics for a number of different purposes; predictive marketing is one of them. It is also used in data mining for the application of artificial intelligence and machine learning algorithms that help discover new statistical patterns and optimize the performance of different business processes.  Big data is actually computers learning from its behaviors that happened in the past to perform certain business processes in a better way and deliver such insights that are truly helpful for the organization to function better. Now, we will shed light upon some big data techniques through which predictive analytics, together with big data, is helping save money and time for the organizations. But before that, it is important to take a look at what predictive analytics and bbig data actually are:

What is meant by Predictive Analytics?

Predictive analytics is not a very straight concept. Rather, it consists of some statistical techniques and a number of data analysis technologies like big data that are categorized under one banner of predictive analytics. The most important technique of predictive analytics is regression analysis. It is vastly used to predict the related values of correlated, multiple variables based on a certain assumption. This whole analysis is about finding similar patterns in a huge amount of data to project probability.

Organizations collect a huge amount of real-time data which is then used by predictive analytics to predict future events. The theory is that predictive analytics basically help enterprises to utilize their historical data to move towards more of a forward-looking perspective. This can be best explained through the example of the loyalty programs. Predictive analytics help predict what promotions or products a customer might be interested in by analyzing his past buying behavior. It helps organizations to gain the insight into customers’ interest and customize their products and promotions accordingly.

The basic power of predictive analytics is drawn from a variety of different technologies and methods, including big data, statistical modeling, data mining, machine learning and also some mathematical processes. Organizations use predictive analytics tools and models to recognize trends and forecast events and behaviors that may occur over a particular period of time. The time can be as specific as a certain millisecond, minute, day or even a year in the future. Due to the reliability and precision of results of predictive analytics tools and techniques along with all the other advantages that it offers, they are gaining high popularity in the market and are being adopted by a vast number of organizations only due to use of big data.

Big Data and Predictive Analytics

We often see that Predictive analytics is associated with the term Big Data. This is because the whole concept of predicting future events is based on detecting patterns in huge amounts of real-time or mostly, historical data. The engineering data are extracted from instruments, sensors, and all the connected system that exists in the world, whereas business applications use big data related to their customers, transactions, market, and sales for predictive analytics.

For the extraction of value from the big data, organizations apply various algorithms on this data using Spark and Hadoop like tools. The data sources used here include log files, transactional databases, videos, images, audios, sensors, etc. Machine learning algorithms are then used mostly on data that is combined from several sources to detect patterns and forecast future events. Some examples of machine learning algorithms used for this purpose are linear as well as nonlinear algorithms, support vector machines, neural networks, and decision trees are due to big data.

Why predictive analytics is important?

The competition in the market is increasing day by day. So to sustain your position in the industry, it has become important for the organizations to make use of predictive analytics tools and models by using big data. These tools help them to get an edge over other products and services in these crowded markets only because of big data.

Predictive analytics help equipment manufacturers to innovate their hardware. Product developers can make use of predictive capabilities to increase the value of the existing solutions. It can also help in reducing equipment failures and operating costs when used in predictive equipment maintenance. Big data helps forecast the energy needs of the equipment that proves to be very useful in their maintenance.

Examples of Predictive Analytics

Predictive Analytics help organizations of very diverse domains such as healthcare, finance, automotive, pharmaceuticals, manufacturing, and aerospace.

  • FINANCE: Predictive analytics help in developing models related to credit risks.
  • AEROSPACE: It helps in improving air-craft time. Also, to reduce their maintenance costs, predict energy needs, and compare its performance with oil, fuel, and lift off.
  • AUTOMATIVE:  Companies are making use of sensor data from all the connected vehicles to develop driver assistance technology. These autonomous vehicles use predictive analytics to build and adapt driver assistance algorithms.
  • MEDICAL DEVIES: Pattern detecting algorithms are used in the healthcare department to spot COPD and asthma.
  • ENERGY PRODUCTION: Predictive analytics are also used to forecast the future electricity demands and prices. This forecast is based on historical trends, plant availability, weather, and seasonality.

Predictive analytics is really evolving in the retail industry. The potential it holds is vast and simply cannot be defined.  Many startups have been using predictive analytics models and other big data techniques to work on financial transaction risk and fraud analysis. Big data powers up these models as they are entirely based on variety of huge amount of data. It is said that only the surface of predictive analytics has just been scratched. It is capable of evolving the entire domain of artificial intelligence with its predictive analytics models and tools. As we will dig deeper into this field, endless possibilities will be discovered and we shall say Big Data is the need of today's world.

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