According to The Global Language Monitor, \u2019Big Data\u2019 is the most confusing Tech Buzzwords of the Decade thus far. According to them, -\n\n\u201cBig Data is the biggest buzzword. It has been called the key to new waves of productivity growth, essential to the US place in global economics, and more. Now if only we could agree on exactly what this means and how we get there. (By the way, consider yottabytes: a quadrillion gigabytes. Hint: Just think a lotta bytes.)\u201d\n\nThe meaning of Big Data has become more diffuse as it has grown in popularity. According to Raj Bhatt of Knowledge Foundry, \u201cThe most important trend is the increasing hype\/confusion around Big Data analytics. Many companies and people have their own definition of Big Data \u2013 leading to a lot of confusion about what qualifies as a Big Data solution.\u201d\n\nAccording to another study by Mzinga, 42 percent of respondents are unfamiliar with big data technologies. There remains a great deal of confusion regarding what the term Big Data really means. In this article, we try to address some of the myths and confusion around Big Data.\n\nLot of Data is not Big Data\n\nMost of the people get carried away by the term Big and define big data as simply a lot of data. But it\u2019s not just that. \u201cWe define a problem as a Big Data problem only if the size of the data, the short timeframe for a solution, and the diversity of the data necessitate a distributed NoSQL-based architecture\u201d, says Raj.\n\nAsk an educated audience and a plethora of definition would arise, ranging from large data sets and data warehouses to big code for analytics and BI.\u00a0 Some even see big data as hardware and large applications. To keep the definition simple, there has been a growing consensus in the industry to define Big Data, by three Vs-\n\n\n Volume \u2013 the amount of data has to be large, in petabytes not just gigabytes\n Velocity \u2013 the data has to be frequent, daily or even real-time\n Variety \u2013 the data is typically (but not always) unstructured (like videos, tweets, chats)\n\n\nYet, the confusion around big data continues with expansion of V\u2019s to include veracity, viscosity, virality and even going till 16 V\u2019s.\n\nHadoop is not Big Data\n\nOver time, "Hadoop" has become synonymous with the term "big data". A lot of people associate big data with Hadoop when it is just one element and one capability that's required to address the big data problem. And there are various other applications that can easily substitute Hadoop.\n\nA part reason for this confusion is because the majority of conversation around big data is driven largely by the information technology community and centering primarily on technology, as opposed to the line of business community.\n\nIts Y2K again\n\nThere\u2019s almost a Y2K natured fear around how Big Data would grow to be a monster and would eventually become uncontrollable by our existing technologies. Today an apocalyptic styled urgency in technology community has set in to create quick solutions around big data.\n\nAmongst all this chaos, the main beneficiaries i.e. organizations are quite. Today the conversation around big data is more centered on \u2018how we solve it\u2019 rather than defining the problem to be solved. Organizations would continue to grapple the perplexity around big data until the disarray settle down to a more established business solution rather than technology itself.