Thursday, 11 June 2015 14:02

Latest Trends in Data - Machine Learning & Predictive Analysis Featured

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Machine learning is a subfield of Computer Science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. Machine Learning is the study and creation of algorithms that can make predictions on data. Machine Learning can also be used to process an organization’s data and conduct predictive analysis. Two terms are important here “Machine Learning” and “Predictive Analysis”. In this blog we’ll explore these two concepts and their application.

Machine Learning

Machine Learning is a broader concept which includes classification of data, clusterization, supervised learning, unsupervised learning, building predictive modeling algorithms etc.  The application of Machine Learning are endless from self-driving cars, speech recognition to effective web search.  Many compare Machine Learning to Statistics. While statistics focusses on inference utilizing probabilistic models, Machine Learning is about predicting the outcome of the data provided. 

One of the applications of Machine Learning in the RDMS world is to uncover the relationships among the tables. There are algorithms that you can use to uncover the relationships and gradually improve based on user’s feedback. An important benefit of this is to bridge the lack of documentation for the databases. Once you’ve understood the data and the relationships, you can get started on data mining & conducting predictive analysis.

What is Predictive Analysis & why do you need it?

Predictive Analysis is the practice of extracting information from your existing data, determining patterns and then predicting the future outcomes or trends. Real-time and historical data is used for this purpose. It can enable firms to identify and respond to future opportunities better and quicker. It can identify the target customers, their behavior pattern, and time to communicate with them and thus the messaging to the prospective customers can be improved. Companies like Amazon and Netflix have used it brilliantly to create value for their customers and themselves. Predictive Analysis is for example useful for marketing and risk management.

Some common benefits/use cases of using Predictive Analysis are:

1.      Incorporating sentiment in marketing analytics

2.      Customer demand analysis and forecasting

3.      Social analytics/sentiment (what is customer sentiment compared with competing products?)

4.      Price optimization and analytics

5.      Social network analysis (identifying the most influential product advocates)

6.      Next best offer

7.      Financial fraud detection

8.      IT security

Companies in healthcare, insurance, banks and marketing groups are heavily using Predictive Analysis.

Before you analyze and predict, you need to understand the data. Large organization are often confronted with undocumented data sources. Databases were created long ago (typically 15 – 30 years) and since then they have grown over time. The documentation and any related information was either not done or has been lost over the years. In the absence of documentation it takes longer to understand the complexity of the database in order to start any meaningful analysis. To overcome the lack of documentation, Machine Learning comes in handy.

At ROKITT, we’ve set out to solve such challenges. We utilize concepts like Machine Learning & Predictive Analysis to create innovative products which can help solve complex problems with ease. To learn more about our innovative products please contact us at This email address is being protected from spambots. You need JavaScript enabled to view it.


Image courtesy of Stuart Miles at



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