All businesses today is inundated with data, be it transportation, healthcare, manufacturing, food or retail. They are struggling with managing multiple big data sources. Combining these types of disparate data sources is the next evolution in business intelligence. It should have ability to forecast and deliver predictive insights from large, varied and rapidly changing data sets. However, retrieving meaningful message from the vast data is not easy. Most companies have built an effective data collection system, but very few of them possess the capabilities to retrieve the powerful information from the large scale data even after applying some of the most advanced commercial data analysis systems. The retail industry has experienced some significant challenges and opportunities in recent years. One such challenge is proliferation of digital media that assists makes customers test and compare products in one store and then buy them elsewhere, this phenomenon is popularly known as ‘show-rooming’. Maintaining customer acquisition, retention and loyalty is another demanding task. As against this there is incredible opportunity due to penetration and usage of mobile and internet technology. Global competition has forced retailers to engage in real time analysis of enormous data that is generated on a daily basis through various consumer interactions. The use of big data analytics, and reporting can generate insights that will improve the profitability for retailers. The purpose of the paper is to provide overview of big data analytics in various stages of retail process - tracking emerging popular products, foretelling of sales and future demand through predictive simulation, explore its application in Price optimization, targeted promotions and overall enhancement in customer experience and suggest future developments in marketing, consumer profiling and predictive analytics for retail business.