The Slow Data Movement: My Hope for 2013

In 1986 Carlo Petrini founded the Slow Food Movement. This movement began as resistance to the opening of a McDonald’s fast food restaurant near the Spanish Steps in Rome. I’ve seen this affront to the old city and felt the disgust that must have emboldened Petrini to start an international movement. Slow Food was introduced as an alternative to fast food. It is based on the belief that much of the beauty and wholesomeness of food requires that we take time with it: time in producing it, time in preparing it, time in savoring it. The Slow Food Movement is one of a broader Slow Movement that focuses on many aspects of life. I learned only a few days ago that there is a Slow Reading movement that encourages people to slow down and appreciate what they read. It is not surprising that in our fast-paced world it is important to be reminded to slow down and embrace life with greater awareness and appreciation, lest we forget who we are and what makes life worth living. I believe that it is time to extend the Slow Movement to the realm of information technology. In this time of so-called Big Data, too much is being missed in our rush to expand. The entire point of collecting data—using information to better understand our world and then make more informed decisions based on that understanding—has been forgotten and is certainly not being achieved in our manic rush to throw more technology at a problem that can only be solved by making better use of our brains.

In the last few days you have no doubt read many predictions about the new year. I am loathe to make predictions. I find that most predictions made about technology fall into one of three categories: 1) statements of the obvious (e.g., people will increasingly rely on tablet devices), 2) marketing (e.g., our product will lead the way), and 3) outright guesses (e.g., Gartner’s recent prediction that by 2015 a total of 4.4 million jobs will be created worldwide to support Big Data). Rather than making predictions as a way to start the new year, I’d rather state my hopes. A rising appreciation for Slow Data and the practices that naturally arise from it is my hope for this year.

Big Data is usually defined in terms of the 3Vs: volume, velocity, and variety. Doug Laney of Gartner originally defined the 3Vs 12 years ago. When he wrote his original paper on the topic, the 3Vs were already old news. I remember reading Laney’s paper at the time and thinking that he did a good job of characterizing significant aspects of data that have been true since the advent of the computer. Actually, if we want to be historically accurate, we can date the beginning of Big Data to the year 1440 when Gutenberg invented the printing press. I believe that the advent of the printing press had a greater impact on the world of information in terms of volume, velocity, and variety than the advent of computers. Actually, long before the printing press the invention of writing had an even greater impact and before that the invention of language even greater. What’s happening with data today has its roots firmly planted in a long line of technologies that have allowed humans to disseminate information for ages. Technology increases data volume, velocity, and variety. The fact that these have increased at an exponential rate since the advent of the computer is well known and has been for years, yet packaged as Big Data and fueled by huge marketing budgets, this growth is suddenly being embraced as something new and unprecedented. Hurray data! Hurray technology! Three cheers for the technology vendors that are making a bundle selling incremental extensions of what they’ve been selling all along. While the world reaches for its wallet amidst the rising clamor, what’s important about data is being lost in the din.

I’d like to introduce a set of goals that should sit alongside the 3Vs to keep us on course as we struggle to enter the information age—an era that remains elusive. May I present to you the 3Ss: small, slow, and sure.


As data increases in volume, we should keep in mind that only a relatively small amount is useful. Data consists of a lot of noise and only a little signal. We must separate the signals from the noise, which we’ll never get around to doing if we spend all of our time boosting technology for data generation and collection, but not learning how to find and understand what’s actually meaningful and useful.


We’re in love with speed. Like many people, I love to drive fast. It’s a rush. Much of what I value in life, however, requires time. This is especially true of data sense-making and decision-making. Some of my favorite words were spoken by Lao Tzu, the founder of Taoism:

Muddy water, let stand, becomes clear.

These words have come to mind and thus to the rescue many times in my life. I recently read a new book titled Wait: The Art and Science of Delay by Frank Partnoy, which roots the benefits of waiting, pausing, taking a bit more time, in science. In the introduction Partnoy says:

The essence of my case is this: given the fast pace of modern life, most of us tend to react too quickly. We don’t, or can’t, take enough time to think about the increasingly complex timing challenges we face. Technology surrounds us, speeding us up. We feel its crush every day, both at work and at home. Yet the best time managers are comfortable pausing for as long as necessary before they act, even in the face of the most pressing decisions. Some seem to slow down time. For good decision-makers, time is more flexible than a metronome or atomic clock…As we will see over and over, in most situations we should take more time than we do.

Although some decisions in life are best made instantly based on intuition, this is only true if your intuitions were built on a great deal of relevant experience and the matter at hand does not lend itself to deliberation, such as a bear running towards you at full speed. These are the types of decisions that Malcolm Gladwell wrote about in Blink. Most non-routine decisions, especially those that change the courses of our lives, benefit from conscious, deliberate, analytical reasoning—what psychologists such as Daniel Kahneman call “System 2 Thinking.” In fact, Kahneman refers to these two modes of reasoning as thinking fast and slow.

No matter how fast data is generated and transmitted, the act of data sense-making, which must precede its use, is necessarily a slow process. We must take time to understand information and act upon it wisely. Speed will in most cases lead to mistakes. Bear in mind the wise parable of the tortoise and the hare.


Even though we can collect data about everything imaginable, variety is not always a boon. More choices are only helpful if 1) we need them, and 2) we have the time and means to consider them. Otherwise, they do nothing but complicate our already overly complicated lives. In an effort to remain sane, I spend a fair amount of time limiting my choices. For instance, I don’t participate in Twitter, text messages, Facebook, or even the professional social networking service Linked-In, because I already face enough interaction with people as it is. By restricting myself mostly to email correspondence and direct face-to-face conversations, I maintain the level of human interaction that works for me. I’m not suggesting that these services are bad, but they don’t suit me. The next time that you’re in a grocery store browsing the toothpaste section, ask yourself if the variety of products arranged in daunting rows is useful. Wouldn’t just a few good choices make life better?

Life and our world are rich in variety. This is a good thing. Data consists of a collection of facts about life and the world. Only a subset of those facts will be useful to you. The same is true for an organization. Just because you can collect data about something doesn’t mean you should. In fact, given all the data that you’ve already collected, wouldn’t it make sense to spend more time making use of it rather than getting wrapped up in the acquisition of more? When you recognize an opportunity to do something useful with data, that’s when it becomes sure. As people and organizations of limited resources, shouldn’t we spend our time identifying what’s useful and then actually using it?

Data is growing in volume, as it always has, but only a small amount of it is useful. Data is being generating and transmitted at an increasing velocity, but the race is not necessarily for the swift; slow and steady will win the information race. Data is branching out in ever-greater variety, but only a few of these new choices are sure. Small, slow, and sure should be our focus if we want to use data more effectively to create a better world. I doubt that the 3Ss will ever become the rallying cry of a mighty movement, but those who heed them will become the true heroes of the information age. When the dust settles, we’ll see that it was people who took the time with a limited collection of the right data who solved the problems of our age.

Take care,

12 Comments on “The Slow Data Movement: My Hope for 2013”

By Bill Droogendyk. January 3rd, 2013 at 2:17 pm

“The entire point of collecting data — using information to better understand our world and then make more informed decisions based on that understanding — has been forgotten and is certainly not being achieved in our manic rush to throw more technology at a problem that can only be solved by making better use of our brains”.

So right! Thank you Steve.

By David Gerbino | @dmgerbino. January 4th, 2013 at 5:21 am

Steve, great article. The data part of my collegiate education was very similar to your ‘hope’. My early data mentors were also rooted in your camp. It was never the volume of data but always the golden nuggets that we need to be working with.

We all need to take a step back, breathe and focus on the data that as you say is “useful.”

Thank you for writing this article.

By Scott. January 4th, 2013 at 7:21 am

Great article. So many times wrong decisions are made because not enough time is spent in making sense of the data or because there is too much “non-data” data that muddies the analysis.

By Ian Oliver. January 4th, 2013 at 8:31 am

An excellent article, it really is about time that emphasis on relevant data capture and processing be made.

“Are we in effect embodying the ideals of nouvelle cuisine as applied to data? A rejection of excessive complication, reducing the processing to preserve the natural information content, the freshest and best possible ingredients, smaller data sets, modern processing techniques, innovation and invention as being drivens because of the data collection (and not because they might happen if we collect data) – the analogies between slow food, nouvelle cuisine and slow data are abundant.”

By Davide Mauri. January 4th, 2013 at 8:38 am

Great article I agree with you completely. I would also add, just to make it more clear, that “slow” also goes along with the idea of “quality”. A huge amount of bad quality data is much worse than smaller, high-quality correct data. Unfortunately companies have some difficulties to admit that a lot the data they have has a very low quality…inconsistent and incorrect very often. No surprise that there is much more hype around Big Data than Master Data Management. It seems that people hope that the Big Data “thing” will clean up or turn data into valuable information just by magic.

By Dirk Remacle. January 7th, 2013 at 2:28 am

I was looking for a diet against all this Big Data hype. Think I just found one.
Big Data = fastfood of Analytics, interesting thoughts … see also this post of Thomas Christiansen about “the importance of the Why3 :

The context is Agile & Agile BI, but there certainly is a S³ angle as described above:

Small (=Relevant)
Slow (=thorough)
Sure (=dependable)

By Daniel Zvinca. January 7th, 2013 at 2:56 am

The article is a great attempt to restore the importance of the quality of data interpretation against the ability of large volume data processing using a nice analogy.

The amount of significant attributes able to influence business decisions grows very slow during time. This is part of serious investigation performed by specialists, this is not at all in ratio or in competition with the increased needs of data storage.

On the other hand, the volume of data might be a concern if the tools were designed or described as “all data real time analyzer” which would lead to a wrong conclusion that several terabyte of data are aggregated on fly on any user query. Or even worse, that everybody can get to a solid conclusion just by being able to interrogate large volume of data bypassing the usual cognitive process.

The Real Time Big Data Analyzer might lead to a wrong conclusion that steroids can dramatically improve the ELO coefficient of a chess player. Or even worse, that everyone who can lift X00 kilo can automatically win the World Chess Championship title. I am not saying that is not possible that the World Chess Champion to be able to lift X00 kilo. I’m just saying that somebody can become World Chess Championship without extreme power body building, but with the right skills and training.

I have to go to buy some weight lifting equipment now, it is possible just by buying it to be able to better understand the big data matter. Or not.

By Matthew O’Kane. January 7th, 2013 at 3:56 am

Great post Steve. I certainly think that, in the case of the important one-off decisions, it makes sense to think slowly and apply the 3Ss. To draw on the thinking fast/slow analogy further, though, I think you are missing all of the smaller operational decisions that companies need to make. For example, I wouldn’t want Google to apply the 3Ss to every search I send them – I want them to use as much data as possible to give me the best results in a short time frame. Also, I quite like the fact that my bank uses my transaction history to ensure it is always me using my credit card at point of sale and not a fraudster.
I believe these smaller operational decisions are where Big Data really comes into focus. I agree that some vendors have simple repackaged under the latest buzz word (but hasn’t this always been a problem?) but there really are some game changing technologies out there that are truly deserving of the Big Data name. Let’s continue to hold vendors accountable to demonstrate how their technology will improve our companies but let’s not throw the baby out with the bath water at the same time…

By James Lytle. January 7th, 2013 at 9:04 am

Bravo. It seems to me the growing boast of data insight through more powerful tools is a distraction to our most powerful and relevant tools to data interpretation: people. I don’t mean to oversimplify, but because you can capture and aggregate mountains of data doesn’t mean the ability to act on that information will get viewed, interpreted, and disseminated any better. I think those dollars would yield far better results if companies invested in the education and motivations of their people, their most powerful distributed network for data insight. That’s where the potential energy lies.

By David Leppik. January 8th, 2013 at 11:06 am

To amplify Matthew O’Kane’s point, human brains are actually working in a fast, habitual mode most of the time. The daily routine: driving to work, buying gas, greeting your coworkers– these are all done unconsciously, even though we have the illusion of conscious control.

Where we have conscious control is in creating habits or (sometimes) overriding the routine. (Often even the overrides are habitual, with some novel stimulus, such as a fire alarm.) That is a slow, deliberative, and costly process; so we avoid it when possible.

Google works much like the brain: humans carefully tinker and tweak the ranking and caching algorithms, slow processes run in batch mode, and then front-end servers have under 100ms to calculate a result. (And according to a Google Tech Talk I watched recently [ ], it takes 10ms for one of Google’s computers to call out to another computer, which severely restricts the number of CPUs involved.)

Slow data has a place, but so does snap decisions. Business Intelligence is in the slow place: determining the policies and procedures for making front-line decisions. Big data is mainly useful in the fast place: a customer has just entered your store, how do you greet him/her? Big data turns back into small, personal data when you start talking about individual front-line employees, personalized web pages, etc.

In my professional experience, you get the most value by giving large numbers of people exactly the data that’s relevant to them, immediately when it’s useful to them. For example, training employees by giving them immediate feedback. But to do that you need to have the slow, deliberative process to determine what feedback is meaningful and how to present it usefully. And more important, you need to know when convenient measurements are misleading or create perverse incentives.

By Stephen Few. January 14th, 2013 at 3:33 pm


We have conscious control (System 2 thinking) in more cases than creating habits and overriding routines. Any time that we are deliberately thinking about something, we are engaged in conscious and slow System 2 thinking. System 1 thinking (unconscious, intuitive), such as snap judgments, do have a place, as Malcolm Gladwell pointed out in his book “Blink.” The trick is to rely on System 1 thinking for activities that it handles best (e.g., swerving when a cat runs out in front of your car or hitting a baseball) and to rely on System 2 thinking for activities that it handles best (e.g., analyzing data).

In what sense is Big Data “mostly useful” for fast, intuitive, System 1 thinking? I don’t understand this at all. System 1 vs. System 2 thinking has nothing to do with Big Data vs. data by any other name. What does Big Data have to do with greeting a customer who just entered your store? What does Big Data have to do with “giving large numbers of people exactly the data that’s relevant to them”?

How are you defining Big Data? What is it about giving people relevant information involves Big Data rather than just plain data?

By Frederik Pruijn. February 11th, 2013 at 2:24 am

Excellent and inspiring article. I can relate much what you wrote. For example, I have a (free) LinkedIn account that I use but I don’t have a mobile phone. As a cancer research scientist I would say that the proliferation of scientific journals and the pressure to publish combined with the relative ease of producing mountains of data has not resulted in increased quality of scientific papers and that there certainly is a lot of ‘noise’. One book that I will read, one day, is The Art of Procrastination; procrastination is often associated with time-wasting, which is unnecessarily negative in my opinion. Nicholas Carr also referred to Gutenberg in Is Google Making Us Stupid?

I have acknowledged this blog in one of mine and I will check out your blog from time to time.