Businesses across the world are adopting a data-centric operational model in order to thrive in this dynamic market and wade through the competition. Although the demand for ready to use data sets have increased by several folds in the last few years, getting access to such data remains a bottleneck for most companies. This is why we came up with DataStock, a web store where you can buy structured data sets from websites spanning across domains like Retail, Recruitment, Travel and Classifieds. These data sets are a result of high quality web scraping, refining and structuring, which means the data you get is of top notch quality. Here are some of the important applications of DataStock.
Identifying trends in your industry can be challenging without the right set of data. In this scenario where companies are proactively looking for data from web to identify the changing trends, DataStock can equip them with the right data for various analytical use cases. There are different kinds of analyses possible with the data from DataStock. Sentiment analysis, Historical trends mapping and Predictive analytics are some of the common applications that can help the business users.
1. Sentiment analyses
Sentiment analysis is used to understand the feelings, attitudes, emotions and opinions from words using natural language processing systems. Understanding customer sentiments on different products or services is extremely important if you are in the retail and hospitality industry. DataStock provides product data extracted from some of the popular Ecommerce, travel and restaurant portals on the web, which includes customer reviews and ratings. This makes it an ideal source of data for running sentiment analysis. Identifying the customer’s sentiments can be used to improve the product or offering, cataloging, and pricing. This can unravel hidden insights that can have a positive impact on business decision making.
2. Historical trend mapping and Predictive analytics
With huge amounts of data at your disposal, it’s easy to perform timeline analysis and run predictive analytics for a quick peek into the future. By analyzing the trends, companies can learn the state of the demand and supply of a particular product for a given period of time. Further, it can be used to find patterns from this to stock up the inventory. The results of predictive analytics are also useful in capitalizing on the upcoming marketing opportunities.
Machine learning is all about enabling machines to learn on their own by feeding them with training data sets. Provided with the training data, machine learning programs learn to do correlational tasks like classification, clustering, attribution etc. Here, the efficiency and power of the machine learning program will hugely depend on the quality of training data.
If the number of records in your data sets is limited, it can have a negative impact on the predictive power of your machine learning model. Teaching machines to perform complex tasks takes not just sophisticated frameworks, but also huge amounts of data. This is where DataStock can help you. With large data sets extracted from various types of websites from all over the web, DataStock can power extremely complex machine learning models with ease.
NLP or Natural language processing is used to equip machines with the ability to interpret and process natural languages used by humans like English as opposed to a computer language like Java or Python. As it’s difficult to determine a definite meaning for words or even sentences in natural languages, natural language processing is a vast and complicated field.
The meaning associated with a particular word in English would vary across different individuals. In order to comprehend this versatility of natural languages, NLP systems need natural text data. The data sets provided by DataStock include millions of records with customer reviews and can be used to build a text corpora for natural language processing. A text corpus is a large body of text, which can be used as the base while building a natural language processing system. With bigger data sets, machines can get better at understanding human languages, thus opening up a wide range of possibilities in artificial intelligence and robotics. Translation engines such as Google Translate already make use of natural language processing to handle the translation jobs.
The applications of Big Data are vast. Depending on your business model and creativity, you could come up with a completely new way to use the data provided
by DataStock. With the right tools and strategies, you could give yourself an edge in the competition and that’s what DataStock essentially aims to facilitate. You can head over to DataStock if you want to get your hands on the data sets now.