Data Science and Big Data Analysis

Data Science and Big Data Analysis

Big data analysis involves the study of large, varied data sets (termed as big data) to emulate conclusions and formulating trends that would help organizations to make better-informed decisions. What data analytics deals with at its core is to accumulate a wide range of data and apply certain methods to it to form a pattern or trend for further research work. Big data is generally driven by the state of the art technology along with several business models to assist the organization in making better decisions in the long run. Big data also helps in formulating better revenue opportunities, effective marketing, improved operational efficiency, and good customer service.

Big data analysts help to form a predictive approach when it comes to handling huge amounts of transactional data and reach veritable conclusions. This helps organizations deal with a huge amount of untapped data that is often ignored by conventional organizations. Data analysis involves components of both structured and unstructured data. Some of the most common sources of data for analysis come from clicks on the web, server logs, browsing history, surveys, emails, phone records, etc.

The history of Big Data can be traced the back to mid-1990s when huge surges in data being circulated were observed. By the early 2000s, developers and business organizations decided to increase the variety in the data that was being analyzed, and organizations followed the 3Vs of data- volume, velocity, and variety. Usually, unstructured data types do not go well with traditional data warehouses and opt for something that is based on relational structure. Large data warehouses, however, need continuous upkeep and need to be reviewed constantly to ensure the relevance of the data. Big data analytics nowadays are opting for software like Hadoop data lake that acts as a melting point for incoming data streams.

It is extremely crucial to have a sound data management system to facilitate effective data analysis. The data collected needs to be properly configured, organized, and partitioned. To help analyze data, better certain tools have been introduced that make the process more streamlined, like machine learning and deep learning. Another aspect is text mining and statistical analysis software, which helps a lot during business analysis. Big data, however, comes with its own sources of disadvantages like dealing with a huge amount of both internal and external data in addition to interference from third parties. Another potential pitfall includes the lack of internal data analysis sources and often hiring data scientists can be a costly affair for the company.

Given the amount of data that companies need to deal with on a daily basis can often lead to problems arising due to the data quality and proper governance of this data. The data architecture needs to meet the organization’s framework to lead to results that are satisfactory. It is quite a challenging proposition to actually come up with a team of good data analysts and the right mix of technologies to piece together everything so as to facilitate seamless functioning of the organization.

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