In addition to an accurate model,another important part of the processis presenting the results. Informationvisualization, especially for hugedatasets, is a creative task of its ownthat requires not only understandingthe problem domain, but also humancognitive capabilities and restrictions.
Good visualizations communicatethe information quickly and clearly,and make complex systems, statuses,and trends understandable at a glance.Badly designed visualizations can leavethe user more confused than before,tell nothing, or, even worse, lead theuser to draw incorrect conclusions.There is a plentitude of bad examplesavailable, produced even by those whospecialize in visualizations. Thereare even websites dedicated to badexamples of data visualization.
This is why we always emphasizeunderstanding the phenomena—“mining the essential”—beforestarting the visualization design. Thegoal is to convey meaning, not justto visualize discrete data points, nomatter how well laid out. We think thatthe importance of visualizing meaningwill increase significantly in the yearsto come.
But how, then, should we bestutilize big data in decision makingand prediction? Blindly trusting thedata itself may be a perilous route,regardless of how well it is presented.We may well expect that big dataanswers our “what” questions, andthen need to work out ways to alsoanswer why. This is where we proposeputting people back in the driver’s seat.
HOW TO GET BOTH
WHAT AND WHY
We as humans love stories. We arevery good at understanding stories andcreating them from distinct pieces ofevents, people, and places. Our livescan best be described as stories. Weare also very good at telling stories. Infact, storytelling is an efficient meansof exchanging information betweenhuman beings—especially informationrelated to business decisions orscientific discoveries. We learn fromeach other’s experiences and distributethose learnings as stories in one formor another.
Storytelling, or human perceptionof reality, is something that big data isnot good at. Depending on the context,you may also call it hunch, experience,
or instinct. Nevertheless, it is a matterof interpreting reality through ourexternal and internal contexts, pastand present.
This is why we propose a twofoldapproach. We propose utilizing datawhere it is at its best, and a humanapproach where it’s better suited.
We will get both what and why. Or,better yet, we can combine the two.
Results from a big data analysis maybe considered part of your currentcontext—one additional source formaking informed decisions.
We strongly recommend fusing bigdata decision making with qualitativeresearch methods that rely on humanperception of reality. With these twomethods combined, our understandingof the world will take a significant leap.
We get the world full of data alongwith human perspective, trust, andlearning.
In some cases, we may start with thebig data approach to identify possiblesolutions and then work on the resultswith qualitative methods to figure outwhy. Or, alternatively, we may startwith qualitative research to identifyfuture trends or human behavioralpatterns, and then use big data tovalidate them. Either way, the twodifferent methods support each othersignificantly.
Obviously, when there is not yetany data, you can’t use big data for anyprediction. A typical data-sparse caseis product development, where thefeatures of a new product are planned.
You may utilize big data to a certaindegree, for example to figure out thepast behaviors of the future productusers, to analyze competitor products,and the like. This data may greatlyhelp to reveal otherwise undetectablepatterns, and is invaluable in thehands of an experienced researcher.
However, it takes a human to actually
talk with people face-to-face to find
out what their expectations and future
wishes and needs are. Further, it takes
a human to translate the vague human
expressions—coupled with results
from big data analysis—into concrete
product features and functionalities,
and a human designer to make a
realization of a new product or
feature that matches both the data
and human aspects. There is no big
data machinery that can make such
predictions, interpretations, and
Let’s now get back to the originalquestion of the Web-store entry-pagedesign we raised at the outset. Thisscenario highlights the importanceof combining both what and why.
The data clearly says that search andfiltering are essential, and the humanperception and experience tell theimportance of appealing design, brandidentity, and uniqueness. Therefore,while redesigning the site, you mayutilize both worlds. You can makesearch and filtering as easy to accessand use as possible, but you should notsacrifice the other equally essentialaspects while doing so. You may eveninnovate completely new ways ofproviding easy access to search andfiltering along the way (this is a typicalexample of creating new: The datatells you where the problem is andprovides you with reason to innovate).
Making decisions solely based on pastdata, without the human perspective,creative input, and some risk-taking,will not make you a leader.
The above example is as simple asany, and not much research is neededto extract the human perception in thiscase. However, you may once have toface an arbitrarily complex matter inan otherwise similar condition. Then,analyzing the data, validating findings,and identifying new directionswith qualitative research is highlyrecommended. Taking a leap of faith bytrusting only the data can be avoided.
This is the future of decisionmaking we would like to see: Combinebig data analysis with qualitativeresearch. This is our “what and why”approach.
Juha Lehikoinen is co-founder andchairman at Leadin Oy, one of the fastest-growing user experience service agenciesin Europe. His research interests includeunderstanding human-data interactionsand interrelations, and augmented realityinteractions. He is the lead author of PersonalContent Experience (John Wiley & Sons, 2007).→
Ville Koistinen is principal designer atLeadin, working on design assignmentsfrom the industrial Internet to automotive.His interests include design for challengingenvironments and contexts, and special usergroups.