Information Analytics alludes to the method of collecting, organizing, translating, and extricating valuable experiences from the raw facts and figures within the gigantic sums of information generated by trade on a day-by-day premise. The duty of the data analyst is to decipher the burgeoning information into useful discernments and after that into important data, helping the organizations to create keen choices based on information rather than depending on instinctual.
In today’s world, the accumulation and storage of the massive amount of data streaming into an organization’s databases have become a concern. Volumes have increased to millions and millions of gigabytes. Companies have moved far from just directories, datasheets, and documents.
But at the end of the day, it is almost impossible to analyze and interpret all this data. This data includes not only text but videos, photos, sound recordings, and sensor data.
The data streaming into an organization’s data warehouses is increasing at an exponential pace and is expected to grow by up to 50 zettabytes by 2020. A zettabyte is over 931 million gigabytes! Each and every action we do online leaves a digital trace. Every single action we make when we go online such as online shopping, chatting with friends through social media applications, or using GPS-equipped smartphones generates data that businesses mine for information. We basically leave digital footprints with every action we take digitally involving the use of the internet.
The amount of data generated is growing rapidly and this requires the use of advanced technology and tools specifically designed for the analysis and interpretation of this enormous amount of data. This is where data analytics come to the rescue. The thorough and in-depth analysis of this “Big Data” requires the use of data analytics software such as Python, SAS, R, and Hadoop which were developed specifically for handling Big Data.
Data analysis using these tools with mathematical and statistical algorithms will further assist an organization with developing good decision-making processes and allow it to respond to customer queries rapidly, resulting in an increase in goodwill for the organization. The high accuracy in Big Data analysis also ultimately helps the organizations to increase profits and lower costs.
Developing new products based on ongoing market trends can be done only when Big Data is accurately and precisely analyzed. Customer satisfaction matters a great deal if a business wants to be successful and popular with its target audience. The market trends and customer preferences must be analyzed properly and specifically so as to develop products that are trendy and acceptable to the target audience. The data findings may even assist companies in taking advantage of new market revenue opportunities and ultimately improve customer satisfaction, thereby enhancing the operating efficiency and profitability of the company.