Our latest research report, for EDF Energy, is live.
With people feeling overwhelmed by choice and information, and technology only accelerating this trend, we see a growing opportunity for businesses to develop ‘Navigator Brands’ – brands that work on behalf of their customers,…
Navigator trend to simplify your life from @canvas8
Twelve Types of Social Media Users
Which type of user are you? Informer is my type.
Fight for earth poster. Powerful
Those are unfolded wallets by Combina - Alexe Popescu. Wall decoration at Autor jewelry fair.
qFrom The Doors: The Illustrated History
Book by Danny Sugerman (1983)
when foreigners ask you what is happening in piata constitutiei for new year`s eve
Get to know Bucharest, our delicious hometown
Data and data sets are not objective; they are creations of human design. We give numbers their voice, draw inferences from them, and define their meaning through our interpretations. Hidden biases in both the collection and analysis stages present considerable risks, and are as important to the big-data equation as the numbers themselves. […] data scientists should take a page from social scientists, who have a long history of asking where the data they’re working with comes from, what methods were used to gather and analyze it, and what cognitive biases they might bring to its interpretation (for more, see “Raw Data is an Oxymoron”). Longer term, we must ask how we can bring together big data approaches with small data studies — computational social science with traditional qualitative methods. We know that data insights can be found at multiple levels of granularity, and by combining methods such as ethnography with analytics, or conducting semi-structured interviews paired with information retrieval techniques, we can add depth to the data we collect. We get a much richer sense of the world when we ask people the why and the how not just the “how many”. This goes beyond merely conducting focus groups to confirm what you already want to see in a big data set. It means complementing data sources with rigorous qualitative research.
— Kate Crawford in a HBR blogpost about Big Data biases. (via betaknowledge)
Avoid biases of big data through qualiative research