The Long View | Big Data Will Change All Aspects of the Fashion Industry

Big data is set to fundamentally change the way we understand the world and make decisions. BoF speaks with Viktor Mayer-Schönberger and Kenneth Cukier, authors of Big Data: A Revolution That Will Transform How We Live, Work and Think to understand the forces at play and the implications for fashion.

Viktor Mayer-Schönberger and Kenneth Cukier | Source: Courtesy Photo

LONDON, United Kingdom — For thousands of years, people have been gathering and analysing data to understand and organise the world. But in the analogue age, collecting and processing data was costly and time-consuming, meaning that statisticians and other information analysts were generally limited to working with small data samples.

Digitisation set the stage for a transformative shift. From Google search queries and GPS signals to transaction records and social media updates, today’s information society emits an estimated 2.5 quintillion bytes of data every day. At the same time, increased computing power, cheap storage and new data-crunching technologies have given us the ability to analyse a far larger volume of information than ever before, extracting insight and creating new forms of value in ways that stand to radically change the way consumers, businesses and governments operate and interact.

Fundamentally, ‘big data’ is about making predictions. Indeed, it’s now possible to leverage large amounts of messy, real-world data to build predictive models that can find patterns, establish correlations and infer probabilities with enough accuracy to help us do things like pick the best moment to buy a plane ticket, foresee the spread of deadly infections or identify emerging fashion trends.

To find out more, BoF spoke with Viktor Mayer-Schönberger, professor of internet governance and regulation at Oxford University, and Kenneth Cukier, data editor at The Economist, about the power of big data and what it means for the business of fashion.

How do you define big data?

Big data describes our recent ability to collect and analyse much more data about a particular issue than ever before to gain new insights and offer innovative products and services. It will affect all sectors of our economy and all aspects of our existence, from business to healthcare to education.

To give a sense of how much more data we have these days, consider that when a new major telescope facility begins operations, it tends to collect as much information in the first week as was collected in the entire history of astronomy up until that point. And then it does it again, and again, and again, week in and week out, until a new telescope goes online and we see another step-change in the amount of information that is gathered. Or, think of biotechnology. It took ten years and $1 billion to sequence the human genome in 2003. Today, a single lab can do that in two to three days at a cost of less than $3,000.

How does big data change the way we understand the world?

We currently understand the world through hypotheses ideas on how exactly a piece of the world works that we have tested against a small amount of data because collecting data has been so costly and time-consuming. That, of course, limits us in how many ideas we test, as we often have to re-collect the data for a slightly different idea. It makes understanding hard and slow. In contrast, with big data, we can have algorithms test hundreds of millions of hypotheses automatically against an often large dataset.

For example, Google was able to track the outbreak of the seasonal flu using search terms alone. They didn’t do that by guessing which terms would best correlate with the flu. They get 3 billion queries a day. Trying to think through what the right terms might be (fever? sneeze? cough medicine?) would be a fool’s errand. Instead, Google took the 50 million most common search terms and ran them through 450 million mathematical models to determine which search queries could be used to predict flu outbreaks.

It meant giving up our innate preference for causality and placing our trust in correlations that is, knowing what, but not why. We don’t know if Google’s model works because someone goes online when they’re feeling ill they may be overhearing sniffles in the cubicle next to them. We don’t know and we don’t need to care. To track the flu with searches, correlation was good enough.

What are the implications for business? In your book, you describe data as “the oil of the information economy.” Can you unpack that?

Data has always been useful for businesses. It enables economic transactions and helps supply meet demand. After all, price is data, as much as certain product qualities and transaction terms. But until recently, data was seen largely as the lubricant that greases the machine of commerce.

In the age of big data, data itself becomes the good that’s being traded. This shift happens as we realise that the value of data is not exhausted when it’s used for the purpose it was collected. Rather, we can use data for novel, additional purposes that nobody thought about when it was collected.

Who would have thought, for instance, that search terms sent to Google can be repurposed to predict the spread of the flu?

Another example is Farecast, which was acquired and became part of Microsoft’s Bing Travel. Farecast told people whether the price they were quoted for an airplane ticket was likely to go up or go down, empowering consumers by letting them know if they should buy right away or wait. It worked by crunching data on previous airfares. In fact, it processed 200 billion flight price records that amounted to almost every seat, on every plane, for every route, every day for an entire year across all commercial aviation in the United States. That’s a lot of data with which to base it’s prediction. Farecast saved travellers a lot of money and Microsoft eventually bought the company for over $100 million.

But the key is this: Farecast’s brilliance was to take information generated for one purpose selling tickets and apply it for another. The data had become a raw material, a vital economic input. That’s what we mean when we say that data is the oil of the information economy.

Big data is often discussed in the context of technology companies like Google and Microsoft. How can retailers harness the power of big data? Who is doing this well?

Internet companies are some of the first to use big data because they almost viscerally understand the importance of data. Their businesses are often founded on it. But many others can harness the success of big data. What’s crucial is being able to either collect or access relevant data easily.

Take large retail chains: through loyalty cards and other methods they are able to collect a staggering amount of information about what people buy — brands, sizes, types, colours, styles — and when and where they buy. This data is used for transaction and payment, restocking and inventory management, and, in the best of cases, for sales promotions and coupons. But much more could be done, for instance, by analysing and optimising what products are being displayed next to each other, or close to the check-out counter, and when.

To cite one example, Walmart discovered that before a major hurricane, not only did sales of storm supplies spike, but so did sales of Poptarts. Who would have thought? But by seeing the correlation, they could act on it by placing Poptarts at the front of the stores next to the flashlights and batteries, thus making shopping easier for customers and boosting sales.

In trend-driven product categories like fashion, accurately predicting consumer demand is a complex matter. Historical sales data never results in consistently better commercial decisions, while traditional forecasting tools are slow and unscientific. Can big data help?

So far, predicting consumer demand for fashion has been the domain of self-styled ‘experts,’ focus groups and relatively unsophisticated models based on ‘small’ data. Collecting and then analysing actual preferences from potential customers was just too costly and hard to do. This is changing.

As we collect and analyse far more data about people’s interactions, individual preferences will become much better known, more comprehensively and in greater detail than ever before. That provides valuable insights for the fashion industry, from what products might perform best, in general, down to what will likely sell well in which store locations, what products are successful when placed next to each other and how to optimise retail experiences.

How else can big data give fashion companies a competitive edge? How can big data analysis inform activities like identifying creative talent, product development and marketing?

Big data will be used to predict customer preferences. Of course, this does not mean that innovative design and original ideas are being replaced by numbers. Rather, the numbers can help designers identify in which direction to go, where to push harder and how to excel in satisfying customers.

And the predictive insights of big data are not limited to understanding customers only. It will enable brands to pick the most promising creative talent earlier and with a better success rate.

Marketing and advertising will become more efficient, too. For instance, advertisers today rarely know how well billboards are working, because we have little actual data about how many people look at these ads. As we collect data about the human gaze just think of Google Glass we’ll be improving advertising, too.

In a sector as subjective and seemingly unpredictable as fashion, what are the limitations of big data? Is there still room for intuition?

Absolutely. Intuition is central. After all, analysing customer preferences would not easily have revealed that people wanted to buy cars before they were invented; they might have, instead, just wanted a faster horse, to paraphrase Henry Ford. But in the age of big data, intuition will not compete with data about what customer preferences are. Rather, human intuition will be needed precisely because data can never tell the full story and surprise and serendipity are central to human nature.

How might big data change the fashion industry in the years to come?

Every aspect of the business will change, from what colour will be in next season to how to make clothing that fits different body types and how to optimise supply chains.

Viktor Mayer-Schönberger and Kenneth Cukier are the authors of Big Data: A Revolution That Will Transform How We Live, Work and Think, published by Eamon Dolan/Houghton Mifflin Harcourt.

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8 comments

  1. May I direct you to Evgeny Morozov’s work. His keynote at the Seven on Seven conference a couple weeks ago is apt here as well.

    “Every aspect of the business will change, from what colour will be in next season to how to make clothing that fits different body types and how to optimise supply chains.”

    If we accept that fashion is purely a business model, then perhaps the implications of big data outlined here ring true. If we approach fashion as a larger social and anthropological force, then these vague futures, in which the idea of big data revolutionizes fashion, fall flat.

    On ‘how to make clothing that fits different body types,’ it seems that tailors and designers have been handling this just fine for the last few centuries, and indeed it is a question of the human body and three dimensional space at work here, not data correlations with machine learning principles applied to it.

    I am excited to see how fashion makes use of technology, but using these out-of-the-box solutions from technologists working outside the industry is not the answer. I see so often with technology today that we accept swiftly codified perspectives in even our *approach* to problem-solving. I recently attended the Fashion & Technology symposium at FIT here in New York, and a panelist there highlighted the importance of understanding fashion from a cultural perspective as well as from a systemic one.

    Yes, let’s utilize big data where it can be sensibly applied, and let’s also develop heretofore unforeseen ways of approaching how fashion and technology relate.

    ... from Brooklyn, NY, United States
  2. Very insightful. I’d be interested to know specific resources, software platforms that brands can use right now for market analysis, sales performance, forecasting, etc.

    Nadia from United States
  3. Great interview, and it clearly articulates the importance of not blindly going on instinct, but finding ways to support creativity with a bit of science.

    @… when it comes to the fit issue, it’s something that designers and tailors struggle with at multiple points. Fits.me just raised over $7 million to take it on with a bit of data, but this is an example of something that’s changed dramatically enough that design alone won’t solve it. Along the supply chain, things often go wrong and designers can often end up with a production batch that’s off from the sample they approved. With deadlines impending, they don’t have time to reproduce the order, so that goes to stores and websites where the armhole that was cut a half an inch too small causes a fit issue that leads to higher return rates, or excess inventory. At any point in the process, there are various pieces of data that could make a difference that centuries old techniques can’t fix.

    @Nadia, EDITD is one of the better data driven resources for fashion available, there are other companies like Trendalytics (in beta) that use online data to back trend forecasts. In the next few months, the stealth project that I’m working on will also enter that space.

    YM Ousley from United States
  4. Big data plays to the lowest common denominator, it is the infinite focus group. Big data cannot speak to innovation or creativity.

    Frank Illes from Las Vegas, NV, United States
  5. I would love to hear/read the thoughts of someone who appreciates big data like these guys do, but also really understands the unique animal that is the fashion industry. For me, the answers to the last four questions fell short. As … says, fashion is more than a business model but a cultural and anthropological phenomenon as well. Although I do not think anyone has the answer as to how fashion and technology can and will relate, and to what extent (although I imagine it to be of massive potential) – I’d love to hear more from someone(s) who understands and appreciates both realms.

    mu11e from San Francisco, CA, United States
  6. Great article! I fully agree! I work for Fashionbi (http://fashionbi.com), and a social media analytics firm – we thrive on big data, there is so much that you can learn from it! I guess it depends on what kind of person you are or what aspect of work you are doing, which is why maybe some people are averse to it. But as a person working in the marketing industry, understanding your consumers needs and what they respond to is exactly the work you are doing. Big data helps you accomplish this effectively, with less time and effort wasted!

    Ria Fernandez from Rizal, Philippines
  7. Vikram nice article on Big Data. Designed by data scientists, HPCC Systems is an open source data-intensive supercomputing platform to process and solve Big Data analytical problems. It is a mature platform and provides for a data delivery engine together with a data transformation and linking system. The real-time delivery of data queries of the Roxie component is a big advantage for marketers needing to take action from data insights. More info at http://hpccsystems.com.

    DataH from Pompano Beach, FL, United States
  8. “Not everything that can be counted counts, and not everything that counts can be counted.”

    This quote by Albert Einstein allows us to understand and approach big data with more depth, consciousness, and right angle, especially in fashion industry. Big data is not there to rule your business, its marketing and strategic directions; big data follows and provides insights into holistic, well-rounded vision. Abuse or wrong use may lead to a jam of incomprehensible initiatives and engagements.

    Ellina Watanabe from Clichy, Île-de-France, France