The five Cs

Five framing guidelines to help you think about building data products.

By DJ Patil, Hilary Mason and Mike Loukides
July 24, 2018
Trust Trust (source: Terry Johnston on Flickr)

What does it take to build a good data product or service? Not just a product or service that’s useful, or one that’s commercially viable, but one that uses data ethically and responsibly.

We often talk about a product’s technology or its user experience, but we rarely talk about how to build a data product in a responsible way that puts the user in the center of the conversation. Those products are badly needed. News that people “don’t trust” the data products they use—or that use them—is common. While Facebook has received the most coverage, lack of trust isn’t limited to a single platform. Lack of trust extends to nearly every consumer internet company, to large traditional retailers, and to data collectors and brokers in industry and government.

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Users lose trust because they feel abused by malicious ads; they feel abused by fake and misleading content, and they feel abused by “act first, and apologize profusely later” cultures at many of the major online companies. And users ought to feel abused by many abuses they don’t even know about. Why was their insurance claim denied? Why weren’t they approved for that loan? Were those decisions made by a system that was trained on biased data? The slogan goes, “Move fast and break things.” But what if what gets broken is society?

Data collection is a big business. Data is valuable: “the new oil,” as the Economist proclaimed. We’ve known that for some time. But the public provides the data under the assumption that we, the public, benefit from it. We also assume that data is collected and stored responsibly, and those who supply the data won’t be harmed. Essentially it’s a model of trust. But how do you restore trust once it’s been broken? It’s no use pretending that you’re trustworthy when your actions have proven that you aren’t. The only way to get trust back is to be trustworthy, and regaining that trust once you’ve lost it takes time.

There’s no simple way to regain users’ trust, but we’d like to suggest a “golden rule” for data as a starting point: “treat others’ data as you would have others treat your own data.” However, implementing a “golden rule” in the actual research and development process is challenging—just as it’s hard to get from short, pithy oaths and pledges to actual practice.

What does it mean to treat others’ data as you would treat your own? How many data scientists have actually thought about how their own data might be used and abused? And once you know how you’d like to see your data (and others’ data) respected, how do you implement those ideas? The golden rule isn’t enough by itself. We need guidelines to force discussions with the application development teams, application users, and those who might be harmed by the collection and use of data.

Five framing guidelines help us think about building data products. We call them the five Cs: consent, clarity, consistency, control (and transparency), and consequences (and harm). They’re a framework for implementing the golden rule for data. Let’s look at them one at a time.


You can’t establish trust between the people who are providing data and the people who are using it without agreement about what data is being collected and how that data will be used. Agreement starts with obtaining consent to collect and use data. Unfortunately, the agreements between a service’s users (people whose data is collected) and the service itself (which uses the data in many ways) are binary (meaning that you either accept or decline) and lack clarity. In business, when contracts are being negotiated between two parties, there are multiple iterations (redlines) before the contract is settled. But when a user is agreeing to a contract with a data service, you either accept the terms or you don’t get access. It’s non-negotiable.

For example, when you check in to a hospital you are required to sign a form that gives them the right to use your data. Generally, there’s no way to say that your data can be used for some purposes but not others. When you sign up for a loyalty card at your local pharmacy, you’re agreeing that they can use your data in unspecified ways. Those ways certainly include targeted advertising (often phrased as “special offers”), but may also include selling your data (with or without anonymization) to other parties. And what happens to your data when one company buys another and uses data in ways that you didn’t expect?

Data is frequently collected, used, and sold without consent. This includes organizations like Acxiom, Equifax, Experian, and Transunion, who collect data to assess financial risk, but many common brands also connect data without consent. In Europe, Google collected data from cameras mounted on cars to develop new mapping products. AT&T and Comcast both used cable set top boxes to collect data about their users, and Samsung collected voice recordings from TVs that respond to voice commands. There are many, many more examples of non-consensual data collection. At every step of building a data product, it is essential to ask whether appropriate and necessary consent has been provided.


Clarity is closely related to consent. You can’t really consent to anything unless you’re told clearly what you’re consenting to. Users must have clarity about what data they are providing, what is going to be done with the data, and any downstream consequences of how their data is used. All too often, explanations of what data is collected or being sold are buried in lengthy legal documents that are rarely read carefully, if at all. Observant readers of Eventbrite’s user agreement recently discovered that listing an event gave the company the right to send a video team, and exclusive copyright to the recordings. And the only way to opt out was by writing to the company. The backlash was swift once people realized the potential impact, and Eventbrite removed the language.

Facebook users who played Cambridge Analytica’s “This Is Your Digital Life” game may have understood that they were giving up their data; after all, they were answering questions, and those answers certainly went somewhere. But did they understand how that data might be used? Or that they were giving access to their friends’ data behind the scenes? That’s buried deep in Facebook’s privacy settings.

Even when it seems obvious that their data is in a public forum, users frequently don’t understand how that data could be used. Most Twitter users know that their public tweets are, in fact, public; but many don’t understand that their tweets can be collected and used for research, or even that they are for sale. This isn’t to say that such usage is unethical; but as Casey Fiesler points out, the need isn’t just to get consent, but to inform users what they’re consenting to. That’s clarity.

It really doesn’t matter which service you use; you rarely get a simple explanation of what the service is doing with your data, and what consequences their actions might have. Unfortunately, the process of consent is often used to obfuscate the details and implications of what users may be agreeing to. And once data has escaped, there is no recourse. You can’t take it back. Even if an organization is willing to delete the data, it’s very difficult to prove that it has been deleted.

There are some notable exceptions: people like John Wilbanks are working to develop models that help users to understand the implications of their choices. Wilbanks’ work helps people understand what happens when they provide sensitive medical and health data to a service.

Consistency and trust

Trust requires consistency over time. You can’t trust someone who is unpredictable. They may have the best intentions, but they may not honor those intentions when you need them to. Or they may interpret their intentions in a strange and unpredictable way. And once broken, rebuilding trust may take a long time. Restoring trust requires a prolonged period of consistent behavior.

Consistency, and therefore trust, can be broken either explicitly or implicitly. An organization that exposes user data can do so intentionally or unintentionally. In the past years, we’ve seen many security incidents in which customer data was stolen: Yahoo!, Target, Anthem, local hospitals, government data, and data brokers like Experian, the list grows longer each day. Failing to safeguard customer data breaks trust—and safeguarding data means nothing if not consistency over time.

We’ve also seen frustration, anger, and surprise when users don’t realize what they’ve agreed to. When Cambridge Analytica used Facebook’s data to target vulnerable customers with highly specific advertisements, Facebook initially claimed that this was not a data breach. And while Facebook was technically correct, in that data was not stolen by an intruder, the public’s perception was clearly different. This was a breach of trust, if not a breach of Facebook’s perimeter. Facebook didn’t consistently enforce its agreement with its customers. When the news broke, Facebook became unpredictable because most of its users had no idea what it would or wouldn’t do. They didn’t understand their user agreements, they didn’t understand their complex privacy settings, and they didn’t understand how Facebook would interpret those settings.

Control and transparency

Once you have given your data to a service, you must be able to understand what is happening to your data. Can you control how the service uses your data? For example, Facebook asks for (but doesn’t require) your political views, religious views, and gender preference. What happens if you change your mind about the data you’ve provided? If you decide you’re rather keep your political affiliation quiet, do you know whether Facebook actually deletes that information? Do you know whether Facebook continues to use that information in ad placement?

All too often, users have no effective control over how their data is used. They are given all-or-nothing choices, or a convoluted set of options that make controlling access overwhelming and confusing. It’s often impossible to reduce the amount of data collected, or to have data deleted later.

A major part of the shift in data privacy rights is moving to give users greater control of their data. For example, Europe’s General Data Protection Regulation (GDPR) requires a user’s data to be provided to them at their request and removed from the system if they so desire.


Data products are designed to add value for a particular user or system. As these products increase in sophistication, and have broader societal implications, it is essential to ask whether the data that is being collected could cause harm to an individual or a group. We continue to hear about unforeseen consequences and the “unknown unknowns” about using data and combining data sets. Risks can never be eliminated completely. However, many unforeseen consequences and unknown unknowns could be foreseen and known, if only people had tried. All too often, unknown unknowns are unknown because we don’t want to know.

Due to potential issues around the use of data, laws and policies have been put in place to protect specific groups: for example, the Children’s Online Privacy Protection Act (COPPA) protects children and their data. Likewise, there are laws to protect specific sensitive data sets: for example, the Genetic Information Nondiscrimination Act (GINA) was established in 2008 in response to rising fears that genetic testing could be used against a person or their families. Unfortunately, policy doesn’t keep up with technology advances; neither of these laws have been updated. Given how rapidly technology is being adopted by society, the Obama Administration realized that the pace of the regulatory process couldn’t keep up. As a result, it created the roles of the U.S. chief technology officer and chief data scientist. The Obama administration also established more than 40 chief data officers and scientists across the federal government. The result has been to make sure the regulatory process fosters innovation while ensuring the question of potential of harm is asked regularly and often.

Even philanthropic approaches can have unintended and harmful consequences. When, in 2006, AOL released anonymized search data to researchers, it proved possible to “de-anonymize” the data and identify specific users. In 2018, Strava opened up their data to allow users to discover new places to run or bike. Strava didn’t realize that members of the U.S. military were using GPS-enabled wearables, and their activity exposed the locations of bases and patrol routes in Iraq and Afghanistan. Exposure became apparent after the product was released to the public, and people exploring the data started talking about their concerns.

While Strava and AOL triggered a chain of unforeseen consequences by releasing their data, it’s important to understand that their data had the potential to be dangerous even if it wasn’t released publicly. Collecting data that may seem innocuous and combining it with other data sets has real-world implications. Combining data sets frequently gives results that are much more powerful and dangerous than anything you might get from either data set on its own. For example, data about running routes could be combined with data from smart locks, telling thieves when a house or apartment was unoccupied, and for how long. The data could be stolen by an attacker, and the company wouldn’t even recognize the damage.

It’s easy to argue that Strava shouldn’t have produced this product, or that AOL shouldn’t have released their search data, but that ignores the data’s potential for good. In both cases, well-intentioned data scientists were looking to help others. The problem is that they didn’t think through the consequences and the potential risks.

It is possible to provide data for research without unintended side-effects. For example, the U.S. Internal Revenue Service (IRS), in collaboration with researchers, opened a similar data set in a tightly controlled manner to help understand economic inequality. There were no negative repercussions or major policy implications. Similarly the Department of Transportation releases data about traffic fatalities. The U.K. Biobank (one of the largest collections of genomic data) has a sophisticated approach to opening up different levels of data. Other companies have successfully opened up data for the public benefit, including LinkedIn’s Economic Graph project and Google Books’ ngram viewer.

Many data sets that could provide tremendous benefits remain locked up on servers. Medical data that is fragmented across multiple institutions limits the pace of research. And the data held on traffic from ride-sharing and gps/mapping companies could transform approaches for traffic safety and congestion. But opening up that data to researchers requires careful planning.

Implementing the 5 Cs

Data can improve our lives in many ways, from the mundane to the amazing. Good movie recommendations aren’t a bad thing; if we could consolidate medical data from patients around the world, we could make some significant progress on treating diseases like cancer. But we won’t get either better movie recommendations or better cancer treatments if we can’t ensure that the five Cs are implemented effectively. We won’t get either if we can’t treat others’ data as carefully as we’d treat our own.

Over the past decade, the software industry has put significant effort into improving user experience (UX). Much of this investment has been in user-centric approaches to building products and services that depend on the data the collective user base provides. All this work has produced results: using software is, on the whole, easier and more enjoyable. Unfortunately, these teams have either intentionally or unintentionally limited their efforts to providing users with immediate gratification or the ability to accomplish near-term goals. “Growth hacking” focuses on getting people to sign up for services through viral mechanisms. We’ve seen few product teams that try to develop a user experience that balances immediate experience with long-term values.

In short, product teams haven’t considered the impacts of the five Cs. For example, how should an application inform users about how their data will be used, and get their consent? That part of user experience can’t be swept under the rug. And it can’t mean making it easy for users to give consent, and difficult to say “no.” It’s all part of the total user experience. Users need to understand what they are consenting to and what effects that consent might have; if they don’t, the designer’s job isn’t done.

Responsibility for the 5 Cs can’t be limited to the designers. It’s the responsibility of the entire team. The data scientists need to approach the problem asking “what if” scenarios that get to all of the five C’s. The same is true for the product managers, business leaders, sales, marketing, and also executives.

The five C’s need to be part of every organization’s culture. Product and design reviews should go over the five Cs regularly. They should consider developing a checklist before releasing a project to the public. All too often, we think of data products as minimal viable products (MVPs: prototypes to test whether the product has value to users). While that’s a constructive approach for developing and testing new ideas, even MVPs must address the five Cs. The same is true for well-established products. New techniques may have been developed that could result in harm in unforeseen ways. In short, it’s about taking responsibility for the products that are built. The five C’s are a mechanism to foster dialogue to ensure the products “do no harm.”

Post topics: AI & ML, Data
Post tags: Data Ethics, Deep Dive

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