The 3 R's of Data Conflation

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Data is not only the language of computing and of business, increasingly it’s the language of life, every day. It’s the fuel enabling connections for the engines that allow us to speak with friends online, order food or stream TV or films. It’s also what powers relevant and timely suggestions such as an advert for luggage just in time for an upcoming trip and the fuel that keeps services and websites that provide us with so much valuable and entertaining content, available and free for all.  

But there is an issue. Shocking headlines around data breaches coupled with “all data is bad data” fear-based, click-bait campaigns have led to confusion, fear, and mistrust among people. For the fearful, the only answer is a data lockdown, to narrower use cases and limited players. What this could mean is the thriving data environment that has been the foundation for new and exciting innovations online could become a relic of the past while another issue is data becomes more centralized to a few big players, limiting competition and actually working against those hoping to limit the amount of data organizations hold.   

The reality of how data serves people is lost to conflation. There is a general misconception that data is dangerous, so therefore all companies that use it are dangerous. The result is, the good actors have become conflated with the bad, and good data use with misuse – it’s all lumped together.

So, what’s next? 

Firstly, it is important to clarify the wide spectrum of data that exists today. Data can range from non-sensitive data, like the kind of car you drive, to more sensitive data – termed special category data such as ethnicity, political leanings and religious data. When the distinction isn’t clear between the two, they become conflated, increasing the risk people think all companies have access to all data which is nonsense. The vast majority of times when advertisers and marketers talk about data, it’s non-sensitive data. 

Crucially, it must be acknowledged that there are bad actors whose goal is to ignore or break the law, steal data, commit identity theft and misuse data for their gain. Of course this activity should be stopped, prevented, and punished. The actions of these criminals cause disproportionate worry and harm to people, but most other organizations use data not to harm people, but to serve them, this gets lost in conflation.  

To reduce conflation its clear some of the misinterpretation and fear needs to be explained away to help people understand the basics of data and data use. A logical start is to help people understand not all companies are in the same position. These three simple classifications, should help people better understand how companies are working with data, reducing conflation and fear, while increasing choice and trust: 

The Responsible: These brands understand the rules of good data use and are committed to applying them. They use data to understand both current and prospective customers, make better connections across insights and provide better experiences. This includes making loyalty programs work, offering special deals based on previous purchases, making marketing and user experiences more meaningful to make people’s everyday lives better. Most of the brands the public is familiar with fall into this category. 

Data is not inherently bad, nor equal

The Revolutionary: These are the technology innovators. They’re typically looking to solve customer problems, some previously unknown – and when they get it right, they grow fast. They don’t have bad intentions, but they represent the cutting edge. Many of the benefits people rush to enjoy are actually enabled by data. The public needs to be reminded that the new services – such as digital maps – in the palms of their hands are free because data is used to show us more relevant ads. And here we must take care, because rapid innovation runs the risk of outpacing regulation, potentially creating an imbalance between customer and commercial benefit.  

The Repentant: These are brands that have encountered a problem. They may be major brands, but they have made a mistake that left their proverbial data door open to an incident such as a breach. Brands who end up here have a choice. Own up to the mistake, be accountable and take urgent remedial action or fail, by burning customer trust. Respected brands who have slipped surely do not mean to and can recover, but only through swift and transparent action.  

Bottom line: data is not inherently bad, nor is it all equal.  If the public misunderstands that, a great deal is at risk. Brands serve people, and people reward them with their attention and spend. This is a fair exchange, and data use by responsible organizations is part of what makes the modern world turn. 

Conflation by default, demonizes data and masks the many benefits that people get in return for it, such as the largely free internet. Despite sensationalized claims that every person is being watched, manipulated and monetized, data fuels our economy and powers meaningful connections between people and brands. On its own, data is neutral. But when put in the hands of trusted brands and smart innovators, it has the potential to make life better for all of us.  

It’s time to start simple and de-conflate, to encourage people to understand the basics to focus attention not on data as one, but on data that matters most.

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Jed Mole serves as Chief Marketing Officer at Acxiom. He is responsible for Acxiom’s communications with its various audiences, building the brand and supporting business growth.

Acxiom enables people-based marketing everywhere through a simple, open approach to connecting systems and data that drives seamless customer experiences and higher ROI.