Time and time again, experts have pointed out that companies willing to innovate have to rely on data to drive that innovation forward. We even did so in this blog. That’s because following data-driven strategies can bring multiple benefits for any business. Properly executed data science plans can uncover new market opportunities, help better satisfy customer demands, improve overall customer experience, and boost productivity.
Yet, when going over data science initiatives, a lot of experts forget about another essential aspect—data privacy and security. Given that most of those data-driven strategies are based on customer data, protecting said data has become a business imperative. Security and privacy are crucial, on the one hand, because of widespread breaches and cyberattacks, and on the other hand, because people expect businesses to earn their trust through robust privacy-centered data strategies.
Both reasons are deeply intertwined and should be enough reason for companies to embrace a privacy-first approach to data science. But let’s dig past those reasons to understand a somewhat hidden motivation to fully adopt privacy-centered measures in data science: the boost to innovation itself.
Growing Privacy Concerns
If you’ve been following the news over the last few years, you’ve surely come across stories about digital breaches that violated the privacy of millions of users’ data. You’ve also likely read about companies being caught red-handed gathering customer data and selling it without consent. In a certain way, it feels like this kind of scandal happens way too frequently.
Thus, it’s not a surprise to learn that a survey conducted by Pew Research has found that 79% of people are very or somewhat concerned about how companies are using the data they collect about them. And while certain cynics might point out that the same people who are worried are also willingly sharing information over social media, the fact that people are concerned should be the only factor here.
That’s especially true if you consider that data gathered by companies and organizations goes well beyond the photos of a weekend barbecue people share over Instagram. Some of the sensitive information that’s being stored on the cloud includes financial information, medical records, personally identifiable information, intellectual property, inventory information, and many, many more types of information concerning private citizens, businesses, and even governments.
If you account for all those types of information, it’s only natural that people are worried about what companies are doing with that data, both in terms of protection against breaches and cyberattacks and in terms of responsible management. In other words, there’s a growing preoccupation with what data is collected, how it’s stored, and how it’s managed.
I’ll put a pin in this issue for a second to discuss the relationship between innovation and data. I’ll circle back to this at the end.
Innovation Is Inherently Data-Based
In a great read from a couple of years ago, BairesDev’s own CEO, Nacho De Marco, took a deep look into what innovation means today and what businesses can do to achieve it. The entire article has many interesting takes, but I want to highlight one paragraph in particular:
“Things like artificial intelligence, data science, the Internet of Things, and blockchain are changing everything around us, so the process of using them to rise up to the challenge and adapt to the new context is essential. You can call that innovation, lateral thinking, revolution, or whatever name you like. The important thing is to embrace the evolutionary process.”
Why am I citing that paragraph? Because it has what I believe to be the core of what innovation looks like today: disruptive technologies that serve an adaptive purpose. AI, blockchain, the IoT, 5G—all of these are technologies that are rapidly changing the way in which we do everyday things, including business. And the thing that ties them all together? Data.
Think about it. Data is used to train and adjust AI models while, in turn, AI helps analyze big datasets. Data flows in blockchain networks, and it has a symbiotic relationship with the IoT and, consequently, with 5G. Collected data can come from all these technologies while also pushing these developments even further. That mutually beneficial relationship is essential to understanding why innovation is inherently data-based.
Available data leads to curated sets that can provide great insights that, in turn, uncover new opportunities for improvement and innovation. Healthcare providers are using data from multiple touchpoints to improve their patients’ care. Energy organizations leverage data to develop more efficient systems. Data can even redefine the entire financial chain by helping expedite transactions, identify frauds, and make underlying processes more efficient.
These are but just a few of the examples of data-driven innovation. In fact, I’d argue that most innovations today are data-driven because companies are now relying more than ever on information they gather. That way, it’s pretty clear that data is crucial for new developments.
Privacy First Is Essential for Innovation
So, on one hand, we have virtually all innovations relying on readily available data to take place. And on the other hand, there’s a growing reluctance from people to give away their information because of security and privacy concerns. What’s the only way forward, then? For organizations to recapture people’s trust in their ability to keep data secure from attacks and private from prying eyes. In other words, to embrace a privacy-first approach to data.
That would imply providing more control to users, who should own and control their data. It means that businesses should aim to institute more robust security measures and adopt more transparent data management practices. A privacy-first approach also requires new regulations and standards to govern how to collect, store, and share any kind of data. It also needs cloud providers themselves to step up their game and develop systems that check for regulatory compliance of all data while also analyzing the data flow to identify vulnerabilities.
Some startups are already paving the way for some of those privacy-first initiatives to materialize. But we need to make a bigger effort. The entire business spectrum, along with other organizations and governments themselves, should aim to make a more universal approach to data management that guarantees privacy, security, and ownership to users of any kind of service.
While there are certainly commendable projects underway, innovation in a broader sense needs a much bigger effort. Technology might have some of the keys to unlock this privacy-first approach (think of blockchain, cloud computing, and AI all working in unison to grant privacy and security for all data). However, there’s a human component that’s critical to the mix. That is until we all embrace this mindset, people’s distrust will remain a big obstacle for data-driven innovation.