Today, we’re experiencing a growing torrent of big data. Data for our retail purchases, Internet searches, social-media posts, and even our commutes to work reside somewhere. Not only do we cast a shadow on the ground when we walk in the sunlight, we all have data doppelgängers that show both our current state and the history of our lives. Our own data interacts with the data of other people—such as those who buy the same books on Amazon that we do or our friends on social media. All of this data interacts with the companies with whose products and services we engage.
Through machine learning and artificial intelligence, organizations can use big data to predict our next actions—sometimes even better than we can predict them ourselves. The implications of big data are enormous—enabling us to view suggested products while on a retailer’s Web site, receive recommendations to connect with people who we might know on social-media sites, and benefit from smart IoT devices that gather data from us and those who are similar to us, then act accordingly. Organizations in the healthcare and financial arenas use big-data systems to spot potential adverse events, while also pinpointing scenarios that can bring increased profits and positive outcomes. Read More
Despite all the talk about data-informed design, there is not much agreement on what data really means for a product or service’s user experience. That might be because teams don’t yet have a shared language for talking about data, or because access to data is uneven or siloed, or perhaps because team members have different goals for the use of data.
At its core, data-informed design can be difficult to define, because there is not even agreement on what counts as data. We tend to think in dichotomies: quantitative and qualitative, objective and subjective, abstract and sensory, messy and curated, business and user experience, science and story. But the more I work with data and the more familiar I become with the data-science community, the more inclusive my definition of data becomes. Read More
Algorithms drive the stock market, write articles—but not this one—approve loans, and even drive cars. Algorithms are shaping your experience every day. Your Facebook feed, your Spotify playlists, your Amazon recommendations, and more are creating a personalized window into a world that is driven by algorithms. Algorithms and machine learning help Google Maps determine the best route for you. When you ask Siri or Cortana a question, algorithms help shape what you ask and the information you receive as a response.
As experience designers, we rely more on algorithms with every iteration of a Web site or application. As design becomes less about screens and more about augmenting humans with extended capabilities, new ideas, and even, potentially, more emotional awareness, we need algorithms. If we think of experience designers as the creators of the interface between people and technology, it makes sense that we should become more savvy about algorithms. Read More