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Even more disturbing in some respects are the ways in which the seemingly innocuous term Big Data has been used to justify unethical practices, such as gleaning information from our private emails to support targeted ads—a practice that Google is only now abandoning. Data has always been big and getting bigger. Data has always been potentially valuable for informing better evidence- based decisions. On the other hand, data has always been useless unless it can inform us about something that matters. Even potentially informative data remains useless until we manage to make sense of it. How we make sense of data involves skills and methods that have, with few exceptions, been around for a long time. Skilled data sensemakers have always made good use of data. Those who don’t understand data and its use mask their ignorance and ineffectiveness by introducing new terms every few years as a bit of clever sleight of hand. The definitions of Big Data that I’ve encountered fall into a few categories. Big Data is. SAS. This definition fails in several respects, not the least of which is its limitation to business data. The fundamental problem with definitions such as this, which focus primarily on the size of data as the defining factor, is their failure to specify how large data must be to qualify as Big Data rather than just plain data. Large data sets have always existed. What threshold must be crossed to transition from data to Big Data? This definition doesn’t say. Here’s a definition that attempts to identify the threshold: Big Data is a phrase used to mean a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. Vangie Beal, Webopedia. Do you recognize the problem of defining the threshold in this manner? What are “traditional database and software techniques”? The following definition is slightly less vague: Big data means data that cannot fit easily into a standard relational database. Hal Varian, Chief Economist, Google(Source Note: All of the definitions that I quote that are attributed to an individual, independent of a particular publication, appeared in an article written by Jennifer Dutcher of the U. C. Berkeley School of Information titled “What is Big Data?” on September 3, 2. In theory, there are no limits to the amount of data that can be stored in a relational database. Databases of all types have practical limits. People have suggested technology- based volume thresholds of various types, including anything that cannot fit into an Excel spreadsheet. All of these definitions establish arbitrary limits. Some are based on arbitrary measures. Big data is data that even when efficiently compressed still contains 5- 1. Vincent Granville, Co- Founder, Data Science Central. So, if you are accustomed to 1,0. Excel tables, a simple SQL Server database consisting of 5,0. Big Data. Such definitions highlight the uselessness of arbitrary limits on data volume. Here’s another definition that acknowledges its arbitrary nature: Big data is when. John Foreman, Chief Data Scientist, Mail. Chimp. Some definitions acknowledge the arbitrariness of the threshold without recognizing it as a definitional failure: The term big data is really only useful if it describes a quantity of data that’s so large that traditional approaches to data analysis are doomed to failure. That can mean that you’re doing complex analytics on data that’s too large to fit into memory or it can mean that you’re dealing with a data storage system that doesn’t offer the full functionality of a standard relational database. What’s essential is that your old way of doing things doesn’t apply anymore and can’t just be scaled out. John Myles White. What good is a definition that is based on a subjective threshold in data volume? The following definition acknowledges that, when based on data volume, what qualifies as Big Data not only varies from organization to organization, but over time as well: Big data is data that contains enough observations to demand unusual handling because of its sheer size, though what is unusual changes over time and varies from one discipline to another. Scientific computing is accustomed to pushing the envelope, constantly developing techniques to address relentless growth in dataset size, but many other disciplines are now just discovering the value — and hence the challenges — of working with data at the unwieldy end of the scale. Annette Greiner, Lecturer, UC Berkeley School of Information. Not only do these definitions identify Big Data in a manner that lacks objective boundaries, they also acknowledge (perhaps inadvertently) that so- called Big Data has always been with us, for data has always been on the increase in a manner that leads to processing challenges. In other words, Big Data is just data. There is a special breed of volume- based definitions that advocate “Collect and store everything.” Here is the most thorough definition of this sort that I’ve encountered: The rising accessibility of platforms for the storage and analysis of large amounts of data (and the falling price per TB of doing so) has made it possible for a wide variety of organizations to store nearly all data in their purview — every log line, customer interaction, and event — unaggregated and for a significant period of time. The associated ethos of “store everything now and ask questions later” to me more than anything else characterizes how the world of computational systems looks under the lens of modern “big data” systems. Josh Schwartz, Chief Data Scientist, Chartbeat. 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