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dataMKT
A consumer data exchange platform designed to give users greater control, transparency, and value from their digital footprint.Year
2025
Team
Nick Lyons, Nirkhunan KuppuramSector
Consumer Tech
Discipline
Research
Strategy
Product Design
Overview
The idea for dataMKT came from a conversation in which it was mentioned,
I would love it if somebody is monetizing on this information on my behalf, why can’t I monetize it? Why can’t I participate in a marketplace on my own behalf? People take all sorts of jobs to make ends meet.
- heard during conversation
This conversation piqued the interest in understanding data, and the monetization that takes place in the marketplace, which not a lot of consumers are aware of, led to long hours of research in which 30 news articles, 10 peer-reviewed journals, and 2 historical books were reviewed. A detailed timeline of the project is introduced here.
Project timeline
Based on initial secondary research, a context was first formed to make
sense of the tool.
79% of Americans report being concerned about the way their data is being used by companies. Data creation and collection are exponentially increasing. Data brokers collect an average of 1,000 data points on each individual with an online presence.
At this point, we had clear roles defined as a part of the team developing the product,
My roles were outlined as:
To conduct comprehensive user research by gathering and analyzing data through both qualitative and quantitative methods to validate research hypotheses and test concepts. This involves performing dedicated user research studies to evaluate proof of concept, then carefully synthesizing the findings to inform design decisions.
Problem
There is immense value creation in the personal data market, but consumers are often excluded from any value capture. The data owned by consumers is used by data brokers and brands, with the consumer having very little control over their data and transparency of what happens with it.Solution
Design a digital product that helps consumers capture real-time value for their data. The product should also be able to provide adequate transparency and privacy controls to the consumer to decide who gets access to their data, why they get access, and for how long they get access to the consumer's data.
Research
The devised problem statement led us to our ‘How might we’ questions that would be the guiding pointers to build dataMKT - the consumer data exchange platform.
How might we help consumers understand and block invasive data collection before it happens?How might we enable consumers to see, control, and capture value from their personal data?How might we create collective bargaining power for consumers in the data marketplace?
To test the ‘How might we’s, I conducted user research interviews, testing all three hypotheses to understand the most viable concept to build on. The results were as follows:
Privacy-First Model
Hypothesis: Lead with privacy controls and transparency to build trust before introducing value exchange.
Testing Result: Users appreciated the approach but questioned sustainability. Without clear value proposition, engagement dropped after initial curiosity.
Rejected: Insufficient retention driver
Visibility-Control-Value Sequence
Hypothesis: Lead with data visibility, then provide control, then enable value capture.
Testing Result: This sequence created a natural progression. Users felt informed, empowered, and then motivated to engage. The visibility-first approach had trust before action.
Selected: Balanced trust-building with value deliveryTransaction-First Model
Hypothesis: Lead with monetization opportunities to drive immediate engagement.
Testing Result: Users felt this approach was manipulative—"selling" data without understanding what they were giving away created anxiety rather than empowerment.
Rejected: Created distrust and cognitive dissonance
To arrive at these results, I interviewed
4 industry experts
20 consumers
Age range 20-65
Industry experience of 20+ years
The results from the secondary and primary research paved the way for generating insights that would lead to building dataMKT. First of all, we created archetypes to understand the personas of the possible user groups that would be using dataMKT.
Insights
Archetypes graph
The created archetypes helped us create detailed personas of different user groups that include both regular and edge cases.
Egocentric Progressive
“... for me, that's a fair exchange of like giving out my personal information to get something in return.”
Values
- Transparency
- Fairness
- Adaptability
Motivation
Transactional - They are proactive in incorporating new value drivers into their lifestyle.
Description
These individuals are early adopters, quick to try new innovations in their work or personal life to optimize experience and performance.
Indifferent Believer
“I personally don't see any sort of liability issue or like safety issue with it.”
Values
- Convenience
- Virtue
- Trustworthiness
Motivation
Accessibility - They are motivated to engage with things that make their life easier.
Description
These individuals are more trusting and understand they’re not in control. They give away their personal information for ease and convenience.
Casual Steward
“I definitely, and not overly cautious, but definitely only give my data to brands I really trust. And yeah, usually like phone number, email, I'm pretty comfortable sharing. Sometimes I'll plug in like a fake last name or a fake birthday.”
Values
- Awareness
- Reputation
- Moderation
Motivation
Centrist - They are known to take measures for security, but not in extremes.
Description
These individuals have the ability to protect themselves, but are active participants. They may “game the system” by using their knowledge.Suspicious Bystander
“I don't like it, and I don't like that they take and gather all the data and sell it, and we don't have any control over it, but you can't do anything about it. ”
Values
Motivation
Confidentiality - They are motivated to keep private information private by principle.
Description
These individuals have the ability to protect themselves, but are These individuals don’t like being taken advantage of when it comes to their personal data—but will if it benefits them.Privacy Monitor
“I'm really trying actively, like, not to share this information. Like if I'm using signal or something, like, that's where it becomes a concern. Data breaches, passwords, the idea that someone can access, you know, like my personal chats, which is like a really big concern.”
Values
Motivation
Vigilance - They are highly sensitive to possible risks.
Description
These individuals don’t like being exposed and are concerned about their privacy. They think twice before consenting.
User journey map
Based on the user journey map that we designed, we derived the following insights. We started brainstorming clusters of ideas and created affinity maps of ideas that would become features of dataMKT.
Our qualitative and quantitative data gave us valuable insights that we synthesized in coherence with our user journey map findings and affinity mapping to zero down on the following four pillars that would shape dataMKT as a product and also as a pioneer in the consumer data market that puts the consumer and their data at the center, creating value for the,m also ensuring control and transaprency.
Choice
Users value the ability to make choices regarding their data. This includes data sharing (data point vs survey), compensation, opting in/out. The user decides how data is used.
Trust
Users were concerns about the credibility of a platform that monetizes data. They worry about scams, misuse, and ethical implications, and need reassurance before engaging.
Visibility
Users require visibility into how their data is being used and how Data Dam works. They want to be informed on history of actions, recency of information, and definition of terms.Personalization
By allowing users to relate platform features to their daily routines, like tracking purchases, managing subscriptions, or consolidating spending history.
Design
All synthesized data were used to develop the design for dataMKT. We created low-fidelity to high-fidelity mockups to prototype test with our users and experts to get feedback that would make the platform more robust, easy to use, and flawless in user experience.
Information Architecture
High fidelity screens
The high-fidelity mockups helped us run a prototype test for the platform for the following objectives:
Measure how easily users can navigate the Ledger platform and understand the primary functions without external assistance.
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Testing Core User Journeys
Assess the intuitiveness of managing data-sharing permissions, exploring offers, completing surveys, and reviewing transaction history.
- Identifying Usability Issues
Detect pain points, confusion moments, and errors that impact task success, comprehension, and decision-making confidence.
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Gathering Feedback on Design
Collect user impressions on visual style, terminology choices, and whether the platform inspires trust.For the objectives, we tested three different workflows of the platform:
- Reviewing & Adjusting Data Sharing Settings
Navigating to settings, reviewing active data-sharing companies, revoking permissions, and adjusting data type sharing preferences.
- Exploring Offers & Increasing Data Value
Exploring marketplace offers, evaluating earning potential, completing value-boosting activities like surveys, and adjusting offer preferences.
- Viewing Data Receipts & Managing Shared Data
Accessing the data transaction history, requesting a copy of shared data, and revoking or deleting shared data from selected companies.
Learnings and Reflections
What Worked Well
- Advocate, but speak the same product language as your users
Our initial technical terminology created unnecessary barriers. Simplifying language without dumbing down concepts was the key to accessibility. This taught me that strategic design includes language design—the words we choose shape how users perceive control and agency.
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Value must be made visible
Users can't care about something they can't see. Making data value tangible through the dashboard transformed an abstract concept into something worth protecting. This reinforced that for users to engage with their data, they must first see its value quantified in a simple, direct way.
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Control is the ultimate feature for building trust
Elevating privacy controls from hidden settings to the main dashboard was counterintuitive but transformative. It signaled that we prioritized user agency over platform convenience. This taught me that in trust-sensitive domains, transparency isn't just a feature—it's the foundation of the entire experience.
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Participation requires a marketplace
The real-time marketplace validated that users want agency, not just awareness. Creating tangible paths to value—not just visibility of value—was essential for sustained engagement. This showed me that strategic design must connect insight to action.
What I'd Do Differently
- Earlier stakeholder validation with brands
We focused heavily on consumer research, but validated the brand side later in the process. Earlier engagement with marketing executives would have uncovered data format preferences and pricing sensitivities, potentially accelerating our monetization model.
- Quantitative validation alongside qualitative
With only 8 usability test participants, our insights are directionally valuable but not statistically significant. A larger sample size with A/B testing on the key hypothesis (transaction-first vs. visibility-first) would have given us more confidence in our strategic choices.
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Long-term engagement modeling
Our testing validated initial comprehension and trust, but we didn't prototype mechanisms for sustained engagement beyond the first week. Understanding what drives return visits over months would be critical for actual launch planning.
BBroader Strategic Implications
This project reinforced that consumer empowerment in data markets isn't just an ethical imperative—it's a business opportunity. As third-party cookies deprecate and privacy regulations tighten, the data economy needs new models for first-party data exchange. Our research validates consumer willingness to participate when value is visible and control is genuine.
The challenge for any platform in this space will be balancing competing interests. Consumers want maximum value and control. Brands want affordable, quality data. The platform needs sustainable revenue. Success requires designing business models, not just interfaces—understanding how incentive structures align or conflict, and making strategic tradeoffs accordingly.