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Data is a Design Problem

October 20, 2015
Martin Soler

"Big Data."

It’s a trendy catch phrase that marketing people throw around to show they’re onto something. The beauty of this term is that it’s so broad, so elusive and so hard to fathom that most people don’t even try.

I should know, I’m a Chief Marketing Officer for a hotel data and analytics company.

In working on data problems, listening to excellent speakers from great data companies, and talking to potential users (hotels in my case), I’ve found there is a huge divide between the vague fantasy of “Big Data” and the practical application of it, costs included.

There is more data now than ever before, and that’s a trend guaranteed to continue. As people add more computing units to their lives, these produce more data, which in theory will give more insight and thus inform better decisions… Well, that’s the theory.

Just because you have the data, doesn’t mean it will automatically give you the right actionable information.

How will knowing the number of times one switched on the AC or lights make hoteliers better? How will knowing the number of times someone shared their breakfast on Instagram make anything better?

When I look at some of the Big Data projects that exist it seems like the brief was: Here’s a sea of data — let’s “use” it.

Imagine a Pixar movie being done that way: Here are a bunch of computers and some illustrators — make a movie. It wouldn’t work. Or consumer electronics: Here are a bunch of transistors and batteries — make something. That’s not enough. It all comes down to Design.

The problem with Big Data, especially hotel big data, isn’t whether you use it, but how and why you use it. The results you get from data, like any other resource—because it is a resource, not a tool—is entirely dependent on the way you use it, how you process it. When you consider data this way, you start to think of it in terms of its purpose. What can I use this data for? What can it tell me? What is its best use?

In this way, we approach data in terms of design. And ultimately, Big Data has a design problem: You have it. Great. But how and why will you use it?

That’s why we’ve tailored the classic design playbook, 10 Principles of Good Design by Dieter Rams, for our modern data purposes. We’ve found it immensely helpful in understanding how we look at data, why we look at data, and what is the best use for it.

I hope you’ll find it helpful, too:

10 Principles of Good Design by Dieter Rams

Good data is innovative

Good data is innovating constantly. As technology improves, data is curated better, is correlated easier, and develops meaning.

Having the data itself or having a cool dashboard isn’t enough —  you still have to use the tools skillfully.

Good data makes a product useful

Good data can’t only be about marketing and backend/analytics-type stuff. If it doesn’t make the product (hotel) more useful it will not help. Good data should make hotels better and more useful to the guests.

Good data is aesthetic

Good data should be presented in an aesthetic manner. It must be pleasing to the user and not add mental strain. Graphic design and layout are no longer a luxury, they are the norm. Ugly and boring data adds friction to the use and thus wastes the opportunity for improvement.


Good data makes a product understandable

Using data shouldn’t require a PhD, it should be self-explanatory and the conclusions should come naturally. It should tell you what it means with minimal effort.


Good data is unobtrusive

We have seen the infographics and beautiful data visualizations. Aesthetics shouldn’t overpower purpose. Information should always be easily accessible, while pleasing to use.


Good data is honest

Unfortunately most presented data isn’t. Many people love to tweak the data. So it is even more crucial that good data just give the facts. If they aren’t pretty, it tells management what to fix. If they are great, it shows what is successful.

Good data is long-lasting

Data should remain valuable next year and in ten years. If it only is useful to you in the next 24 hours then it probably isn’t that valuable.


Good data is thorough down to the last detail

A lot of data isn’t thorough. Yet it’s fundamental that data is fully considered and understood before being used. This will influence how successful any project will be.


Good data is environmentally-friendly

This one is less relevant — but still applicable. Beyond care for the environment, data must be used virtuously. Does its use reflect your company values? Does using this data take you closer to accomplishing the mission statement?


Good data is as little data as possible

People need the right data at the right time. Most of us are conditioned to ask for “everything” then actually use a tiny percentage of that. So get the right data; Quality over quantity.

We recently published a "Everything you should know about hotel data but didn't dare to ask" article that I strongly recommend you read. You can see it here A Guide to Hotel Data and Analytics