‘Big Data’ is a term used to refer to a collection of data that is huge in size and grows exponentially with time. The data set is large and complex, is difficult to store and process using traditional data management tools. For example, the New York Stock Exchange generates about one terabyte of new trade data per day. Also, more than 500 terabytes of new data get ingested into the databases of social media site Facebook, every day. 72 hours of video are added to YouTube every minute. Google receives over 2,000,000 search queries every minute. Data volumes are expected to grow 50 times by 2020. This data is mainly generated by uploading photos and videos, exchanging messages, adding comments, etc. Big Data is characterized by its massive volume, high velocity, variety, veracity, and complexity.
Big Data can be generated by machines, humans, and organizations. Machine-generated data refers to data generated from real-time sensors in industrial machinery or vehicles and is the biggest source of Big Data. Data comes from various sensors, cameras, satellites, log files, bioinformatics, activity tracker, personal health care tracker and many other senses data resources. Machines generate data in real-time and normally require real-time action. With human-generated data, we refer to the vast amount of social media data such as status updates, tweets, photos, videos, etc. Such data is generally unstructured. The organization generated data is highly structured in nature and trustworthy. It is in the form of records located in a fixed field or file and is generally stored in relational databases.
It is not the amount of data that is important, but what organizations do with it. Big data can be analyzed for insights that lead to better decisions and strategic business moves. It is used to enhance operational efficiency and serve customers better. Big Data, combined with high powered analytics can help business recalculate risk portfolios within seconds. It also helps detect fraudulent behavior and determine the root causes of failures and defects in near real-time. Big Data Analytics is crucial for the growth and success of a firm.
The computing power of Big Data Analytics enables us to decode entire DNA strings in minutes and will allow us to find new cures and better understand and predict disease patterns. Big Data Analytics is also used in the High-Frequency Trading space, where social media trends and news feeds are analyzed in split seconds to make buy and sell decisions. By examining customer data, Walmart can now predict what products will sell and car insurance companies can understand how well their customers actually drive. Enhancing customer satisfaction is one of the most sought after goals of Big Data Analytics today.
Although tools like NodeXL, RapidMiner and KNIME are available to analyze data, they have limitations with respect to data extraction and visualization. Other drawbacks include:
The inability to combine data that is not similar in structure or source and to do so quickly and at a reasonable cost
The inability to process the volume at an acceptable speed so that the information is available to decision-makers when they need it
More so, there is a shortage of people with the skills to bring together the data, analyze it and publish the results.
All the above-mentioned challenges can be addressed with a solution like Phrazor, an augmented analytics tool by vPhrase that automates data analysis and report generation. The reports contain graphical elements coupled with comprehensive narratives to aid quick understanding, and thus faster decision making.
For instance, Phrazor studied real-time television channel performance data and generated the following report:
Big Data is at the foundation of all the megatrends that are happening and it is up to us to leverage the power of analytics to march forward and realize our business goals.