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Top1. Introduction
Data Science, therefore its tool, Data Analytics are new terminologies that connote emerging fields in twenty-first century. They refer to quantitative techniques and processes that are used to enhance the productivity of an organization through extracting knowledge from its data molded into meaningful forms to discover patterns. Organizations collect data from their customers, business partners, social media and enterprise interactions. Such data can be processed and then categorized as per the company needs, and analyzed to extract implicit and potentially useful information.
In this Internet era, with ever increasing interactions among participants, size of the data is increasing so rapidly such that the information available to us in the near future is going to be unpredictable. Modeling and visualizing such data is one of the challenging tasks in data analytics field. In early days, analysis of data was performed with the help of huge number of skilled manpower whereas nowadays data analytics and visualization tools are used to extract useful information much faster. Still, running high-speed data analytics on massive amounts of data and deriving pertinent information in time is one of the biggest issues in today’s competitive environment.
Data analytics is the process of extracting knowledge, trends, and useful information from huge data repository. While often used interchangeably with the term “business intelligence,” they are different. Business intelligence is the way in which a company can use data to improve business and operational efficiency whereas data analytics involves improving ways of making intelligence out of that data before acting on it; i.e. one can slice and dice the data to extract insights that allows leveraging the data to give the organization a competitive advantage.
Since the data generated from hundreds of millions of devices, things like wearable tech, smartphones, and anything that’s part of the Internet of Things (IoT), is increasing rapidly, improving skills and means to analyze it are imperative. From collection processes to organizing and communicating techniques such as modeling and visualization, a large range of modes of operations are involved. In addition, it requires a large team of skilled analysts to process data and to discover potential information from it. Today there are a number of enterprise level tools for running high speed data analytics on massive amounts of data, as well as publically available free tools like Google Analytics that offer every business and entrepreneur the opportunity to incorporate data analytics while making effective decisions.
Therefore, a Data Analyst converts raw data into meaningful knowledge and that can be used to improvise business process. Data can be collected from variety of sources such as company sales, consumers, retailers, business processes adopted, company advertisements, shares market investment, supply chain management and production. The bigger the organization, the larger the data and more important is the need to utilize data analysis. All this can get extremely complicated for the untrained eye. This is where a Data Analytics professional steps in in order to capture data, aggregate it, wrangle with it, and convert it into a format that is easy to visualize, analyze, and report. A data analyst is helpful for:
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Gathering data from multiple data sources;
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Consolidating the data;
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Producing and preparing detailed reports;
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inspecting patterns, correlations, trends; and
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Providing discovered pattern for business process improvement.
With this overall idea of what a data analyst does, it should be clear that data analytics helps organizations harness their data and utilize it to identify new opportunities, leading to smarter business moves, more efficient operations, higher profits and happier customers through following ways:
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Cost reduction,
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Faster, better decision making, and
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New products and services.