Learn 6 useful Differences Between Big Data Vs Predictive Analytics

 Difference Between Big Data and Predictive Analytics

As one of the most “hyped” terms in the market today, there is no consensus as to how to define Big Data and Predictive Analytics.

Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation. Big Data has emerged as an important area of interest of study and research among practitioners and academicians. The exponential growth of data is fueled by the exponential growth of the internet and digital devices. Advancement in technology is making it economically feasible to store and analyze huge amounts of data. Big Data includes a mix of structured, semi-structured and unstructured real-time data originating from a variety of sources.

Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about the future, or otherwise unknown events. Predictive Analytics provides a methodology for tapping intelligence from large data sets. Many visionary companies such as Google, Amazon etc. have realized the potential of Big Data and Analytics in gaining competitive advantage. These techniques provide several opportunities like discovering patterns or better optimization algorithms. Managing and analyzing Big Data also constitutes few challenges – namely size, quality, reliability and completeness of data.

Head to Head Comparisons Between Big Data Vs Predictive Analytics (Infographics)

Below is the Top 6 Comparision Between Big Data Vs Predictive Analytics

Key Differences between Big Data Vs Predictive Analytics

1. Architecture

Big Data has to do with the quantity of data, typically in the range of .5 terabytes or more, where the capacity of relational database systems starts to degrade so the need of cloud-based pipelines like AWS and data warehouses are the needs of the hour. On the other hand, Predictive analytics has to do with the application of statistical models to existing data to forecast likely outcomes with the churned data sources.

2. Target Problem

“Big Data” describes the data itself, and the challenge of managing it, while “Predictive Analytics” describes a class of applications for the data, regardless of quantity. So, both of them represents mutually exclusive entities.

3. Social Media Use Cases

Social Media has proven to be the best use for both Big Data and Predictive Analytics. But both of them serve as a sequential chain to each other. As Social Media data comes from multiple sources but eventually gets into an MDM(Master data Management) which can be built via Big Data technologies only on which the Predictive Analytics and other algorithms can be fired to give the outcomes. This new type of data management solution bears the trademark of highly scalable, massively parallel, and cost-effective.

4. Technology Ecosystem in Big Data and Predictive Analytics 

The sweet spot for Big Data Platforms and Predictive Analytics, for instance, is dealing with high-value transactional data that is already structured, that needs to support a large amount of user and applications that ask repeated questions of known data (where a fixed schema and optimization pays off) with enterprise-level security and performance guarantee. So to deal with them we have different tools and technologies.

For Big Data,

AWS, Apache HDFS, Map Reduce/Spark, Cassandra/HBase.

For Predictive Analytics,

R, Statistical methods, forecasting, regression analysis, Data Mining, Data Warehouses.

Big Data Vs Predictive Analytics Comparision Table

Conclusion – Big Data Vs Predictive Analytics

Big data and Predictive Analysis, both are here and they are here to stay. Despite the hype, Big Data and Predictive Analytics does offer tangible business benefit to organizations. It enables enhanced insight, decision making, and process automation. There is also a so-called paradigm shift in terms of analytic focus. That is a shift from descriptive analytics to predictive analytics. The combination of Big Data and Predictive Analytics in all domains has the great potential to positively affect decision support and operations such as cost management systems and resource allocation.

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