Big data has become the revolution of Information Technology which is transforming industries around the world
Big data is a combination of technology and data that integrates, reports and accesses all available data filtering, reporting and correlating insights achievable with previous data technologies. It explains data processing surpassing the human scale.
There exist three trends which enabled the Big Data revolution are:
1) Rapidly increasing the amount of data available
2) Accelerating data storage capacity and computing power at low cost
3) Evolution in Machine Learning approach to analyze convoluted datasets
With the amount of collected data and published information increasing exponentially, it is now expected that 90% of the total data around the globe today has been created alone in the past two years. This huge amount of data is presumed to increase the collected digital universe of data from trillion gigabytes in the past to 44 zettabytes by the end of 2024. The IOT phenomenon driven by incorporating of networked sensors into household appliances, gathering of data through sensors fixed in smartphones and decreasing the price in satellite technologies add support for further stimulation in the collection of modern alternative data sources.
Accelerating data storage capacity and computing power at low cost
The purpose of parallel or distributed computing and enhanced storage capacity has been publicized through remote, distributed access to the assets. This advancement is also specified as Cloud Computing. It is now expected that by 2020, over 1/3rd of the available data either exist or move to the cloud. Even one web search on the search engine is said to be answered over coordination across 1000 computers.
Open source frameworks for shared cluster computing (i.e. splitting a difficult task among multiple machines and calculating the results) similar to Apache Spark which has become more popular, even though technology vendors provide distantly connected which are classified into categories like Platform-as-a-service (PaaS), Software-as-a-service (SaaS) or Infrastructure-as-a-service (IaaS). While these shared access of isolated resources has dramatically minimised the hurdles to the doorway for accomplishing large-scale analytics and data processing, hence giving rise to alternative data based strategies for a broad collection of both quantitative and fundamental investors.
Machine Learning approach to analyze convoluted datasets
There were many important advancements in the field of recognition of patterns and approximation of functions. These analytical methods which are also defined as ‘Machine Learning’ and are elements of wider disciplines of Computer science and Statistics. Machine Learning skills give rise to analysis of vast and unstructured datasets and planning of trading strategies.
With reference to the methods of traditional machine learning (which are defined to be advanced statistics), there is a lot of focus aimed at investment applications of Deep Learning(and analysis based method that depends on multiple layered neural networks), besides Reinforcement Learning(a unique approach that support algorithms to find and explore most advanced strategies)
Whereas neural networks are present around for decades, it was only in the past decades that they found a broad application across businesses. The previous years were the widespread adoption smart gadgets and services like Amazon Echo, Apple siri and Google Home, which depend solely on Algorithms of Deep Learning. This growth of advancements in ML algorithms in solving problems is heavily captivating investment managers to implement the same algorithms.
About the Author
Saikumar is a content writer who is currently working for Mindmajix. He is a technical blogger who likes to write content on emerging technologies in the software industry. He can be reached through LinkedIn and Twitter
