Data’s headline appearances throughout 2018 distorted people’s understanding of the value exchange between data owner (you) and data user (organizations).
Expectations around how much people’s personal data is worth became falsely inflated, and the mystery surrounding how it’s used became a cause for concern. Moving forward, organizations must design for transparency, so that consumers can trust that they’re pursuing only the data they need to build new products and services, and that they’re using and storing that data responsibly.
- Communications & Technology
- Banking & Insurance
- Health & Public service
- Automotive & industrial
- Life sciences
- Retail & consumer goods
What’s going on?
Customers’ trust in organizations gathering and using their data was already dented.
In 2018, the greater visibility of data use heralded by the new General Data Protection Regulation (GDPR) and risk of misuse highlighted by Cambridge Analytica left many feeling vulnerable and exploited.
In a survey for IBM, 75 percent of respondents said they will not buy a product from a company – no matter how great the product – if they don’t trust that company to protect their data, while 60 percent ranked a potential war less concerning than cybersecurity.
Cracks started to appear in the long-held assumption that customers happily share data with the organizations they deal with so long as they get better and more personalized products and services in return.
Yet, while organizations’ appetite for data gathering, warehousing, analytics and related services continued unabated, a host of new entrants saw an opportunity to get involved in the fast-growing data-trading marketplace by meeting people’s growing demand for ways to better control and monetize their data.
CyberVein, for example, has developed a blockchain-based system enabling people to sell their own data, joining the growing number of other companies already doing the same, including Wibson and Ocean Protocol.
By the close of 2018, such developments had left people’s understanding of the data value exchange out of sync with organizations’. While many people assume their individual data set is desirable in its own right, organizations actually want it most when it’s part of aggregated data.
In 2019, organizations must clearly show the payback for users sharing their data, drawing a straight line from the act of sharing to receiving relevant products and services in return. They’ll need to demonstrate what’s in it for the customer, ensuring that the data value exchange is fair to them.
If organizations design for transparency, they will be able to rebuild trust, allowing individuals to have faith that their relationships with those organizations are mutually beneficial.
Transparent design means clearly demonstrating the value both data owner and data user can gain and designing products and services to give the individual greater control.
Trust and transparency will offer competitive advantage to those who maintain them, opening up new opportunities to attach “trustability” scores to all sources of data and information. The New York City Open Data for All initiative is a great example. NYC’s aim is to improve the accessibility, transparency and accountability of NYC government. By publishing all of the data sets produced by the city’s agencies and organizations, it helps New Yorkers use, learn about and ultimately benefit from the city data.
Expect a shift from “data maximalism” to “data minimalism” as organizations strive toward only the data they need for their products and services. Minimal viable data will be the new trend in product design.
Algorithmic fairness will continue to be an issue of utmost importance. As virtually all organizations will continue to rely on algorithms for key business decisions, they must work even harder to guard against algorithmic bias. Public transparency won’t be enough – they’ll also need to develop tools that open up the artificial intelligence “black box” to investigate potential bias in data sources, as Accenture is doing with its algorithmic fairness tool.
It won’t be long before companies start habitually sharing data, and there will be a move to create data exchanges, or open data APIs, as has happened in open banking. Once accustomed to sharing data, businesses will then form around these larger data sets.
What you should do
Set expectations, and live up to them
Empower people to know how, where and why their data was used in your personalization framework, and make clear what they will get in return. Gone are the days when users would willingly hand over all their information without clear reason or payback.
Embrace “data minimalism.”
Ensure your data strategy follows the minimal viable data pattern and collects only what’s needed to drive the service. Closely align your data collection strategy to your business objectives. Collection, measurement and tailoring of services are intrinsically linked.
Allow people to act when data about them is wrong by designing transparency and enabling people to recalibrate algorithms. Prove that what you get out of using their data doesn’t outweigh the value they get from sharing it.
Quick question: How do you feel about this trend?