We are living in an age of technological data innovations, transforming every aspect of our existence right from the time we pick our mobile handsets to know the traffic congestion on our office route or listen to our favorite music till the time we sleep and put our phones to rest. Every search on Google, every ‘like’ on a Facebook page, every item we purchase online, every job search we do on a job portal, is but an addition in the volume of data. This surge in data has shaken us out of our personal silos and grouped us all as part of one big collection- Homo sapiens, the makers as well as users of data.
What is Big Data?
In simple words, Big Data refers to large or hefty volume of data which can be both, organized and unorganized. This data rather enormous data is later scrutinized, analyzed, and studied to make meaningful inferences and predictions which fuel the further growth of business, help in framing policies and also drive research in fields which need enhancement. Earlier, data was looked upon only as a supporting domain to the core business but now it has become a separate entity essential for the sustenance of any business.
Who are Big Data Analysts?
Any data is futile without the proficiency and the expertise to analyze it. The major question here is not how much data each business has access to, but what use they put this data to. Hence, arise the need for trained Big Data Analysts or Data Analysts- people who can analyze or assess data volumes to deduce pattern, do calculations and use statistical, technical and analytic expertise to help businesses make real, factual and data backed decisions and formulate business strategies.
Relationship between Big Data, Data Analytics and Data Science
Big data is a term that refers to the large volumes of data – both structured and unstructured. Data Analytics is the process of investigating raw data with the rationale of drawing inferences and patterns from data sets. Whereas Data Science is an interdisciplinary domain dealing with tools, techniques and processes of digging out conclusions and patterns from data. It encompasses all spheres of preparation, cleansing, and analysis of data. This is best explained by the following diagram.
• Root cause analysis of failures and problem areas.
• Informed Decision making
• Development of new and better goods and services
• Identifying buying habits of consumers.
• Increasing cost effectiveness
• Reducing turnaround time for events
• Better Risk management