Big data is the field that analyzes and systematically extract valuable information from, or otherwise deal with large scale datasets which are too large or complex to be dealt with traditional data-processing application software. Data with a high number of rows offer greater statistical power, while data with a higher number of attributes may lead to a higher False Positive Rate. Big data Challenges include Recording data, data storage, data analysis, search, sharing, transferring, visualization, querying, updating, information privacy and data source.
Below are some of the use cases where we are doing research and development.
For any company, the goal is to maximize their profit and reduce the cost of the product at the same time. If the price of the product is too high the product will sell poorly, reduce the net return. But if the price is too low, they may leave money on the table. Big data analytics allows you to see which price points have returned the best overall results using various historic market conditions.
Big data analytics allows e-commerce companies to clean and enrich product data for a better search experience on both desktop and mobile devices. With the use of predictive analytics and machine learning to predict customer needs through log data, then personalize their feed with most likely to buy order to generate maximum revenue.
Recommendation engines have become so common on the web, from social media to online retailers and streaming services everyone has implemented their version of recommendation system based on the user's web surfing pattern. It is important for the companies to know their target audience and sell their product to the right customer who will buy.
When forecasting demand, we can go beyond than just historical data and growth assumptions. Using big data analytics, we can make more accurate and meaningful predictions.
Various Big Data packages that we support are HDFS, HBASE, HIVE, MONGO DB, PIG, YARN, MICROSOFT AZURE ML.