What Is Big Data?
“Big data.” It seems like the phrase is everywhere. The term was added to the Oxford English Dictionaryin 2013 and appeared in Merriam-Webster’s Collegiate Dictionary in 2014. Now, Gartner’s just-released 2014 Hype Cycle shows “big data” passing the “peak of inflated expectations” and moving on its way down into the “trough of disillusionment.” Big data is all the rage. But what does it actually mean?
A commonly repeated definition cites the three Vs: volume, velocity, and variety. But others argue that it’s not the size of data that counts, but the tools being used or the insights that can be drawn from a dataset.
To settle the question once and for all, we asked more than 40 thought leaders in publishing, fashion, food, automobiles, medicine, marketing, and every industry in between how exactly they would define the phrase “big data.” Their answers might surprise you! Take a look below to find out what big data is:
- John Akred, Founder and CTO, Silicon Valley Data Science
- Philip Ashlock, Chief Architect of Data.gov
- Jon Bruner, Editor-at-Large, O’Reilly Media
- Reid Bryant, Data Scientist, Brooks Bell
- Mike Cavaretta, Data Scientist and Manager, Ford Motor Company
- Drew Conway, Head of Data, Project Florida
- Rohan Deuskar, CEO and Co-Founder, Stylitics
- Amy Escobar, Data Scientist, 2U
- Josh Ferguson, Chief Technology Officer, Mode Analytics
- John Foreman, Chief Data Scientist, MailChimp
- Daniel Gillick, Senior Research Scientist, Google
- Vincent Granville, Co-Founder, Data Science Central
- Annette Greiner, Lecturer, UC Berkeley School of Information
- Seth Grimes, Principal Consultant, Alta Plana Corporation
- Joel Gurin, Author of Open Data Now
- Quentin Hardy, Deputy Tech Editor, The New York Times
- Harlan Harris, Director, Data Science at Education Advisory Board
- Jessica Kirkpatrick, Director of Data Science, InstaEDU
- David Leonhardt, Editor, The Upshot, The New York Times
- Hilary Mason, Founder, Fast Forward Labs
- Deirdre Mulligan, Associate Professor, UC Berkeley School of Information
- Sharmila Mulligan, CEO and Founder, ClearStory Data
- Sean Patrick Murphy, Consulting Data Scientist, Co-Founder of a stealth startup
- Prakash Nanduri, Co-Founder, CEO and President, Paxata, Inc
- Chris Neumann, CEO and Co-Founder, DataHero
- Cathy O’Neil, Program Director, the Lede Program at Columbia University
- Brad Peters, Chief Product Officer, Chairman at Birst
- Gregory Piatetsky-Shapiro, President and Editor, KDnuggets.com
- Jake Porway, Founder and Executive Director, DataKind
- Kyle Rush, Head of Optimization, Optimizely
- AnnaLee Saxenian, Dean, UC Berkeley School of Information
- Josh Schwartz, Chief Data Scientist, Chartbeat
- Peter Skomoroch, Entrepreneur, former Principal Data Scientist, LinkedIn
- Anna Smith, Analytics Engineer, Rent the Runway
- Ryan Swanstrom, Data Science Blogger, Data Science 101
- Shashi Upadhyay, CEO and Founder, Lattice Engines
- Mark van Rijmenam, CEO/Founder, BigData-Startups
- Hal Varian, Chief Economist, Google
- Timothy Weaver, CIO, Del Monte Foods
- Steven Weber, Professor, UC Berkeley School of Information
- John Myles White
- Brian Wilt, Senior Data Scientist, Jawbone
- Raymond Yee, Software Developer, unglue.it
Founder and CTO, Silicon Valley Data Science
“Big Data” refers to a combination of an approach to informing decision making with analytical insight derived from data, and a set of enabling technologies that enable that insight to be economically derived from at times very large, diverse sources of data. Advances in sensing technologies, the digitization of commerce and communications, and the advent and growth in social media are a few of the trends which have created the opportunity to use large scale, fine grained data to understand systems, behavior and commerce; while innovation in technology makes it viable economically to use that information to inform decisions and improve outcomes.
While the use of the term is quite nebulous and is often co-opted for other purposes, I’ve understood “big data” to be about analysis for data that’s really messy or where you don’t know the right questions or queries to make — analysis that can help you find patterns, anomalies, or new structures amidst otherwise chaotic or complex data points. Usually this revolves around datasets with a byte size that seems fairly large relative to our frame of reference using files on a desktop PC (e.g., larger than a terabyte) and many of the tools around big data are to help deal with a large volume of data, but to me the most important concepts of big data don’t actually have much to do with it being “big” in this sense (especially since that’s such a relative term these days). In fact, they can often be applied to smaller datasets as well. Natural language processing and lucene based search engines are good examples of big data techniques and tools that are often used with relatively small amounts of data.
Big Data is the result of collecting information at its most granular level — it’s what you get when you instrument a system and keep all of the data that your instrumentation is able to gather.
Data Scientist, Brooks Bell
As computational efficiency continues to increase, “big data” will be less about the actual size of a particular dataset and more about the specific expertise needed to process it. With that in mind, “big data” will ultimately describe any dataset large enough to necessitate high-level programming skill and statistically defensible methodologies in order to transform the data asset into something of value.
You cannot give me too much data. I see big data as storytelling — whether it is through information graphics or other visual aids that explain it in a way that allows others to understand across sectors. I always push for the full scope of the data over averages and aggregations — and I like to go to the raw data because of the possibilities of things you can do with it.
Big data, which started as a technological innovation in distributed computing, is now a cultural movement by which we continue to discover how humanity interacts with the world — and each other — at large-scale.
Big data refers to the approach to data of “collect now, sort out later”…meaning you capture and store data on a very large volume of actions and transactions of different types, on a continuous basis, in order to make sense of it later. The low cost of storage and better methods of analysis mean that you generally don’t need to have a specific purpose for the data in mind before you collect it.
Data Scientist, 2U, Inc
[Big data is] an opportunity to gain a more complex understanding of the relationships between different factors and to uncover previously undetected patterns in data by leveraging advances in the technical aspects of collecting, storing, and retrieving data along with innovative ideas and techniques for manipulating and analyzing data.
Chief Technology Officer, Mode Analytics
Big data is the broad name given to challenges and opportunities we have as data about every aspect of our lives becomes available. It’s not just about data though; it also includes the people, processes, and analysis that turn data into meaning.
I prefer a flexible but functional definition of big data. Big data is when your business wants to use data to solve a problem, answer a question, produce a product, etc., but the standard, simple methods (maybe it’s SQL, maybe it’s k-means, maybe it’s a single server with a cron job) break down on the size of the data set, causing time, effort, creativity, and money to be spent crafting a solution to the problem that leverages the data without simply sampling or tossing out records.
The main consideration here, then, is to weigh the cost of using “all the data” in this complex (and potentially brittle) solution versus the benefits gained over using a smaller data set in a cheaper, faster, more stable way.
Senior Research Scientist, Google
Historically, most decisions — political, military, business, and personal — have been made by brains [that] have unpredictable logic and operate on subjective experiential evidence. “Big data” represents a cultural shift in which more and more decisions are made by algorithms with transparent logic, operating on documented immutable evidence. I think “big” refers more to the pervasive nature of this change than to any particular amount of data.
Big data is data that even when efficiently compressed still contains 5-10 times more information (measured in entropy or predictive power, per unit of time) than what you are used to right now. It may require a different approach to extract value.
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.
Big data has taken a beating in recent years, the accusation being that marketers and analysts have stretched and squeezed the term to cover a multitude of disparate problems, technologies, and products. Yet the core of big data remains what it has been for over a decade, framed by Doug Laney’s 2001 three Vs, Volume, Velocity, and Variety, and indicating data challenges sufficient to justify non-routine computing resources and processing techniques.
Big data describes datasets that are so large, complex, or rapidly changing that they push the very limits of our analytical capability. It’s a subjective term: What seems “big” today may seem modest in a few years when our analytic capacity has improved. While big data can be about anything, the most important kinds of big data — and perhaps the only ones worth the effort — are those that can have a big impact through what they tell us about society, public health, the economy, scientific research, or any number of other large-scale subjects.
What’s “big” in big data isn’t necessarily the size of the databases, it’s the big number of data sources we have, as digital sensors and behavior trackers migrate across the world. As we triangulate information in more ways, we will discover hitherto unknown patterns in nature and society — and pattern-making is the wellspring of new art, science, and commerce.
To me, “big data” is the situation where an organization can (arguably) say that they have access to what they need to reconstruct, understand, and model the part of the world that they care about.Using their big data, then, they can (try to) predict future states of the world, optimize their processes, and otherwise be more effective and rational in their activities.
Big data refers to using complex datasets to drive focus, direction, and decision making within a company or organization. This is done by deriving actionable insights from the analysis of your organization’s data.
Big Data is nothing more than a tool for capturing reality — just as newspaper reporting, photography and long-form journalism are. But it’s an exciting tool, because it holds the potential of capturing reality in some clearer and more accurate ways than we have been able to do in the past.
Big data is just the ability to gather information and query it in such a way that we are able to learn things about the world that were previously inaccessible to us.
Associate Professor, UC Berkeley School of Information
Big data: Endless possibilities or cradle-to-grave shackles, depending upon the political, ethical, and legal choices we make.
[Big data means] harnessing more sources of diverse data where “data variety” and “data velocity” are the key opportunities. (Each source represents “a signal” on what is happening in the business.) The opportunity is to harness data variety [and] automate “harmonization” of data sources to deliver fast-updating insights consumable by the line-of-business users.
Consulting Data Scientist and Co-Founder of a stealth startup
While “big data” is often large in size relative to the available tool set, “big” actually refers to being important. Scientists and engineers have long known that data is valuable, but now the rest of the world, including those in control of purse strings, understand the value that can be created from data.
Co-Founder, CEO and President, Paxata, Inc
Everything we know spits out data today — not just the devices we use for computing. We now get digital exhaust from our garage door openers to our coffee pots, and everything in between. At the same time, we have become a generation of people who demand instantaneous access to information — from what the weather is like in a country thousands of miles away to which store has better deals on toaster ovens. Big data is at the intersection of collecting, organizing, storing, and turning all of that raw data into truly meaningful information.
At Aster Data, we originally used the term big data in our marketing to refer to analytical MPP databases like ours and to differentiate them from traditional data warehouse software. While both were capable of storing a “big” volume of data (which, in 2008, we defined as 10 TB or greater), “big data” systems were capable of performing complex analytics on top of that data — something that legacy data warehouse software could not do. Thus, our original definition was a system that (1) was capable of storing 10 TB of data or more and (2) was capable of executing advanced workloads, such as behavioral analytics or market basket analysis, on those large volumes of data. As time went on, diversity of data started to become more prevalent in these systems (particularly the need to mix structured and unstructured data), which led to more widespread adoption of the “3 Vs” (volume, velocity, and variety) as a definition for big data, which continues to this day.
“Big data” is more than one thing, but an important aspect is its use as a rhetorical device, something that can be used to deceive or mislead or overhype. It is thus vitally important that people who deploy big data models consider not just technical issues but the ethical issues as well.
Chief Product Officer, Chairman at Birst
In my view, big data is data that requires novel processing techniques to handle. Typically, big data requires massive parallelism in some fashion (storage and/or compute) to deal with volume and processing variety.
The best definition I saw is, “Data is big when data size becomes part of the problem.” However, this refers to the size only. Now the buzzword “big data” refers to the new data-driven paradigm of business, science and technology, where the huge data size and scope enables better and new services, products, and platforms. #BigData also generates a lot of hype and will probably be replaced by a new buzzword, like “Internet of Things,” but “big data”-enabled services companies, like Google, Facebook, Amazon, location services, personalized/precision medicine, and many more will remain and prosper.
As our lives have moved from the physical to the digital world, our everyday tools like smartphones and ubiquitous Internet, create vast amounts of data. One of the best interpretations of the “big” in “big data” is expansive — whether you are a Fortune 500 company who just released an app that is creating a torrent of user data about every click and every activity of every user or a nonprofit who just launched a cellphone-based app to find the closest homeless shelters that are now spewing forth information about every search and every click, we all have data. Dealing with this so-called big data requires a massive shift in technologies for storing, processing, and managing data — but also presents tremendous opportunity for the social sector to gather and analyze information faster to address some of our world’s most pressing challenges.
There is certainly a colorful variety of definitions for the term big data out there. To me it means working with data at a large scale and velocity.
I’m not fond of the phrase “big data” because it focuses on the volume of data, obscuring the far-reaching changes are making data essential to individuals and organizations in today’s world. But if I have to define it I’d say that “big data” is data that can’t be processed using standard databases because it is too big, too fast-moving, or too complex for traditional data processing tools.
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.
Big data originally described the practice in the consumer Internet industry of applying algorithms to increasingly large amounts of disparate data to solve problems that had suboptimal solutions with smaller datasets. Many features and signals can only be observed by collecting massive amounts of data (for example, the relationships across an entire social network), and would not be detected using smaller samples. Processing large datasets in this manner was often difficult, time consuming, and error prone before the advent of technologies like MapReduce and Hadoop, which ushered in a wave of related tools and applications now collectively called big data technologies.
Big data is when data grows to the point that the technology supporting the data has to change. It also encompasses a variety of topics relating to how disparate data can be combined, processed into insights, and/or reworked into smart products.
Big data used to mean data that a single machine was unable to handle. Now big data has become a buzzword to mean anything related to data analytics or visualization.
Big data is an umbrella term that means a lot of different things, but to me, it means the possibility of doing extraordinary things using modern machine learning techniques on digital data. Whether it is predicting illness, the weather, the spread of infectious diseases, or what you will buy next, it offers a world of possibilities for improving people’s lives.
Big data is not all about volume, it is more about combining different data sets and to analyze it in real-time to get insights for your organization. Therefore, the right definition of big data should in fact be: mixed data.
Big data means data that cannot fit easily into a standard relational database.
I’m happy to repeat the definition I’ve heard used and think appropriately defines the over[all] subject. I believe it’s Forrester’s definition of Volume, Velocity, Variety, and Variability. A lot of different data coming fast and in different structures.
Professor, UC Berkeley School of Information and Department of Political Science
For me, the technological definitions (like “too big to fit in an Excel spreadsheet” or “too big to hold in memory”) are important, but aren’t really the main point. Big data for me is data at a scale and scope that changes in some fundamental way (not just at the margins) the range of solutions that can be considered when people and organizations face a complex problem. Different solutions, not just ‘more, better.’
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.
The joke is that big data is data that breaks Excel, but we try not to be snooty about whether you measure your data in MBs or PBs. Data is more about your team and the results they can get.
Big data enchants us with the promise of new insights. Let’s not forget the knowledge hidden in the small data right before us.
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