Predictive Analysis

Organizations are no more asking for just analysis of their data. Traditional Business Intelligence tools have been doing that for all for a long time now.

What we are exploring is getting more useful insights from our data. Organizations are looking for visualization tools and predictive analysis to explore data in new ways and discover new patterns. Big data analysis is not only restricted to processing large volumes of data on a software, it needs equivalent hardware processing capabilities too.

Predictive Analysis is the practice of extracting information from existing data to determine patterns and predict the future outcome & trends. It helps forecasting what might happen in the future with an acceptable level of reliability and includes what-if scenarios and risk assessment. 

Applied to business, predictive analysis models are used to analyze current data and historical facts in order to better understand customers, products and partners and to identify potential risks and opportunities for a company. It uses a number of techniques, including data mining, statistical modeling and machine learning to help analysts make future business forecasts.

Who is a Data Scientist?

Simple words: A practitioner of data science is a data scientist. They apply their skills to achieve a broad spectrum of end results. They have the ability to find and interpret rich data sources, manage large amounts of data despite hardware, software and bandwidth constraints, merge data sources together, ensure consistency of data-sets, create visualizations to aid in understanding data, build mathematical models using the data, present and communicate the data insights/findings to specialists and scientists in their team and if required to a naive audience.

Data science is emerging to meet the challenges of processing very large data sets i.e. "Big Data" consisting of structured, unstructured or semi-structured data that large enterprises produce. A domain at center stage of data science is the explosion of new data generated from smart devices, web, mobile and social media. Data science requires a versatile skill-set. Many practicing data scientists worldwide specialize in specific domains such as the fields of marketing, medical, security, fraud and finance.