I recently attended a presentation by the City of Chicago’s Chief Technology Officer, Brett Goldstein who discussed the city’s desire to create a smarter city. The desire is to create a more open and transparent municipal government.
All government business is built on data transactions. The goal for the City of Chicago is to create the best data standards using open source technologies that can then be clustered and contextualized for interpretation and action. This can be done by municipal governments creating better applications to become more efficient and responsive and for citizens to adapt data to local needs. This was not as much a discussion of analytics and the meaning of data, but how data should be structured that allows for the greatest flexibility of citizens to take data files and apply them to real-world issues facing neighborhoods or the city as a whole.
IBM has been using the term smarter planet to describe a world where our environment and its objects and actions are captured by IBM to find patterns and gain insights in order to increase the performance of the planet. To most, this sounds magical and even slightly disconcerting. However from an experience design and user experience standpoint, this concept will have far ranging implications on current assumptions and interaction models.
Experience Design and User Experience models have focused on cognitive and workload issues of users and how they interact with digital systems to ease their burdens and frictions which degrade the value of digital experiences. The focus is on human to computer interactions and indirectly how it affects human to human interactions. This includes the clustering and representation of features and functions and the cognitive processes that support their utility value.
However, the role of data and computer to computer interactions are shaping more and more human to computer interactions. This is important as search, personalization, and customization are being shaped by historical data patterns between one person and a digital system, or large numbers of users that are aggregated into classes and their sum total of patterns affect the choices and results that are served to one user.
A large part of the public internet is fed by a series of databases structured by organizations accessed through front-end websites and applications by their markets. Data streams slosh back and forth between users and servers. Based on several variables such as class, location, time, date, and social networks data is structured back to the user which is served up as a series of choices for the user to act upon through event handling. “Information from all the consumer devices, in addition to data from billions of sensors and Web-crawling robots, is crunched in these supercomputing clouds, creating a Big Data revolution full of business opportunities and dangers.” Source
- Some examples of data that is used to shape human to computer interactions through computer to computer interactions are :
- Unique event data that the customer shares about himself or herself
- Data that is based on specific search, or customer queries
- Aggregate data that the company has on a specific customer over time
- Aggregate data that the company has on a class of customers (which can include social media as a class, or series of classes)
- Purchased data that may include geolocation, US Census, zip code, or other specific criteria
- Data that the company uses to market products and services based on business need
- Data that a company uses to market specific products and services at particular times
Selected choices continually refine a databases ability to be more relevant. However, business systems create computational algorithms, or sequential unambiguous instructions to be followed by a computer to further make user experiences beneficial to the customer and the company. Based on aggregate patterns and the needs of a business, users will be offered choices, usually called intelligent offers like what is found on Amazon that displays Customers Who Bought This Item Also Bought or If you buy this, you may want to consider purchasing . . . So data automation and using algorithms for digital systems to self-learn based on aggregate interactions with a system continually affects the end user experience.
In contrast, Apple’s new release of their own mapping system which is a critical component to smartphone core functionality demonstrates how data does become the user experience. Apple’s desire to have their own mapping system is due to being at odds with Google’s mapping software because of the value of the data. David Pogue in his Bits blog outlined the problems facing Apple’s maps :
“Every time you use Google’s maps, you’re sending data from your phone to Google. That information — how you’re using maps, where you’re going, which roads actually exist — is extremely valuable; it can be used to improve both the maps and Google’s ability to deliver location-based offers and advertising.” He went on to say that ” . . . when the overall data set is that huge, even half a percent of faulty data means a lot of flaws. And the trouble is, you never know when you’re going to encounter one. One wild goose chase, and you’ll find it hard to trust the software again.” Source
The wrong data provides the wrong result, which then impacts the usefulness of the representations that a user relies on to make a decision.
Experience Design focuses on creating a holistic experience from a cognitive and cultural standpoint; from how anticipation is built, to the most important interactions users have that create the right perceptions from benefits derived from the experience. It is a form of cognitive design and engineering. User Experience focuses on how to take experience design goals and determine how best this can be achieved from users interactions with digital systems. Much of these activities are possible because of data and data that is bundled together to provide relevance to a user’s cognitive and workload performance can be viewed as a service.
Service Design is the activity of planning and organising people, infrastructure, communication and material components of a service in order to improve its quality, the interaction between service provider and customers and the customer’s experience. Source
One may ask do users of digital systems think that their online actions are being affected by data exchange and modeling? For most, their answer would be an abstract recognition that data plays some role in what is being served back to them. If you ask the same question to a user experience team, there will be recognition in the abstract that data does play a role, but not specific understanding of how data plays a role in elements and choices that are served up to a user to make decisions and complete specific tasks.
Collaborating as a senior information architect on large enterprise systems, many user experience groups only tangentially discuss data when developing user experience models, workflows and user interface elements through specific screen types – and only in specific tactical ways emphasizing the interaction. I have been only in a few meetings when data models are discussed, but not in a way that clearly connects data to how it will impact the user experience or vice versa. This has always intrigued me as my experience has informed me that if the data model is not understood or the dynamics of data interactions are not understood, how does one know that the desired user-experience is being achieved?
Returning to Brett Goldstein’s presentation, he stated that data structure and interpretation is multidisciplinary in order to come to a series of meanings that reinforce one another. The challenges to data is reducing the number of data islands that are stove-piped but continually transact; data warehousing architecture; performance management, data mining and prediction. The data mining and prediction is the most important goal in order to improve digital systems and the performance of user experience models.
The opportunity to experience design is engaging with project teams on defining and architecting data models that support a desired series of system epics, stories and use cases. Also the variability of data patterns that affect individual personalization as well as group personalization is important to understand how these will affect the choices a user will be offered – or will affect what a user will be able to do with a digital system.