How Contextual Integration Can Empower Predictive Analytics

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Adding context to big data can make that data much more powerful and valuable.

Context with predictive analytics is the key differentiator for any successful recommendation. It is not only the quality, availability or price of the product, but the “context” (which is real time) that helps make the most appropriate recommendation to its users. A consumer can be put into different profiles for different purchases, and so, the real-time context, in which the consumer is carrying out the purchase, is very important to correctly make recommendations. (For more on predictive analytics, see How Predictive Analytics Can Improve Medical Care.)

What Is Context?

The world is becoming smarter and more interconnected with every passing day. Now, due to regular use of the internet, a huge amount of data is being produced every day, which is ever growing. Often, when we think about big data, we think about its huge size and the problems involved in its management. But that is not all, as this data can be used for improving the sales of different firms with the use of the contextual data created from huge amounts of big data.

Context is actually a piece of historic data about a certain object. The object can be anything, from different physical locations to people themselves. This data is extremely important as it can be used for analyzing different situations and then making relevant decisions. Context is essential for business as, without it, decisions can easily go awry. By using such information along with big data, businesses can learn more about the historic patterns and current trends. Thus, this type of data is useful for companies who want to make important decisions based on facts, and not guesses.

Why Is Context so Important?

Contextual data is extremely important as its correct analysis can heighten the productivity of many organizations and businesses. It can provide important information necessary to guide the plans of these organizations. Modern big data processing techniques can be used to process large amounts of information from either the internet or the real world. Such data can be used for the betterment of society by better prediction methods, which will allow more profits for businesses and smart solutions for consumers.

Such data can be made even more useful with the integration with machine learning techniques and artificial intelligence. In this way, the data can even be used for the accurate prediction of natural disasters like earthquakes, or for forecasting weather accurately. Businesses must continuously analyze new data in order to process new contextual information, in order to provide effective services to their customers. For this, they need to extract data from emails, smartphones and social media. They will also have to process all of this data in real time.

How Context Can Be Integrated With Predictive Analytics

Predictive analysis is not a very recent advancement – it was actually discovered many years ago. However, the newer techniques, utilizing the latest technology, are driving the movement forward more quickly than imagined and providing highly accurate predictions almost every time. The recent advancements in the field of information technology and artificial intelligence have made many businesses surpass their estimated profits, but it is possible to achieve even more.


This can happen by understanding the fact that data cannot be useful from only one angle. It has to be viewed through multiple angles, which can be done by creating an improvised profile of consumers as well. Here’s where contextual data comes in. The contextual data can be used to prioritize a particular aspect which can result in more profit. While normal records like transactional logs may not give very important information related to a subject, contextual data like behavior logs can give essential insights used for making accurate predictions.

How Contextual Integration Helps Successful Prediction

Many organizations analyze big data resources to find out more about the target entities and also use this information to make their business plans. For understanding this, we can use the simple example of social networking sites, on which the users generate a lot of information about their preferences and dislikes. These sites can be checked regularly for important behavioral data, which can be utilized to make real-time context analytics. More effective pattern-detection methods can also be used in such places where a large amount of data is being generated regularly.

Big data has a huge potential in helping predictive analytics. The information derived from contextual data is also very important for successful predictive analytics. However, for it to be truly effective, the organizations will need the knowledge, so as to properly apply a context to the big data. This will reduce the chances of an error. (For more on big data analytics, see Back to School With Big Data Analytics.)

The combination of big data and context analytics can be a powerful one which can help in the prediction of different outcomes and other factors. Some other advantages of using context analytics is that it enables the organization to use contexts for correctly modeling a solution for users and that it helps in making correct behavioral observations from such data.

Some Practical Implementations

There are many practical applications of contextual information. For example, recently an online computer parts seller called ReplaceDirect started using this service to effectively manage its budget while getting the maximum views and customers. This company used contextual information for the prediction of many items, like the most desirable keywords which would be used to search for their site and the best bidding prices on the most searched terms according to the data.

Some video-on-demand services also incorporate the use of such contextual information for predicting the most desirable movies to be shown to customers and the best time slots for maximum views.

Future of Contextual Integration

Contextual integration is very important for businesses which want to get the maximum profit with the use of predictive analytics. With the advent of more and more devices, more data will be generated which could be mined with the help of advanced data mining software. The data can then be quickly processed into useful contextual information.

Advanced data mining and processing techniques, which will be fully deployed in the near future, will be able to make better sense of the data and process large amounts of contextual data in near real-time. Precise modeling can also be done through this data. In the future, this data may also find application in many different areas other than business sectors, like finding the patterns of earthquakes to predict the next strike, or easily modeling an epidemic map.


The effective analysis of contextual information is an important trait that organizations will need to adapt and improve for successful deployment of any service and also for the prediction of an outcome. The data can also be integrated with a model to make it even more accurate. Contexts can also help in visualization modeling. Contextual information, if processed in real-time, can reveal very much about an entity, like whether its popularity has risen or fallen.

Contextual integration can also help customers to easily and quickly navigate to a desired place and get a desired service. In a similar way, organizations can navigate to the desired information more easily. This can help businesses achieve huge profits and result in higher customer satisfaction as well.


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Kaushik Pal
Technology writer
Kaushik Pal
Technology writer

Kaushik is a technical architect and software consultant with over 23 years of experience in software analysis, development, architecture, design, testing and training. He has an interest in new technologies and areas of innovation. He focuses on web architecture, web technologies, Java/J2EE, open source software, WebRTC, big data and semantic technologies. He has demonstrated expertise in requirements analysis, architectural design and implementation, technical use cases and software development. His experience has covered various industries such as insurance, banking, airlines, shipping, document management and product development, etc. He has worked on a wide range of technologies ranging from large scale (IBM…