As we put 2020 in our rearview mirror, we will see significant tailwinds in a world where digital transformation is accelerating. It has never been more clear that organizations will need to invest in this transformation with the intention of using data to make decisions. Whether they’re using that data to improve customer trust, to leverage human resources more effectively, to model claims propensity for insurance companies, or to cure rare diseases, companies that safely and ethically use data to make their decisions will gain a competitive advantage. Those that don’t, and those that are slow to adapt, will lose market share and may even risk complete irrelevance.
In a post-COVID era, where our digital dependence has become a lifeline, companies now more than ever need to drive their innovation strategies to incorporate a tsunami of sensitive information, not only within their data privacy operations, but across marketplaces and beyond to gain unique insights. Similar to how guardrails protect you from running off the road, collision avoidance systems avoid a crash, and in the worst case, air bags prevent harm in an accident, privacy technologies must provide the same multi-level controls to ensure data is protected, usability is preserved, and in the event of a breach, remediation is a given. As a result, organizations need to become data guardians and provide guardrails that ensure the responsible and ethical use of data privacy ops within, across, and outside their organization.
Before COVID-19, organizations were already aware of the tremendous value gained by using, analyzing, and exchanging data for organizations worldwide. The pandemic has only accelerated existing trends towards cloud transformation while making consumers aware of both how much of their information they must share and the regulations protecting them.
The cloud democratizes access to the raw computing power necessary to perform advanced analytics as well as deploy machine learning and artificial intelligence (AI) projects, which was simply not available to many organizations before. This allows tiny companies to compete with megavendors. Migrating to the cloud provides considerable advantages for organizations seeking to innovate quickly using AI to process and learn from an ever-growing cache of data. That innovation does not, unfortunately, come with a conscience out of the box.
AI alone cannot ensure that this data is used safely, legally, and ethically. In an era of new and evolving privacy regulations, data-driven misinformation campaigns, and increased risks for data breaches, organizations must think through their entire data protection strategies, including elements of both privacy and security.
As software continues to eat the world, data privacy and ethics are the sauce making it palatable to consumers and regulators alike. In 2021, AI and ML practices will evolve to make this a reality.
The adoption of cloud computing for big data analytics and machine learning will only accelerate in 2021. This will drive advances in data privacy, and also raises some new challenges.
Organisations are rightly nervous of data breaches, and are thinking carefully about what data should move to the cloud, for what purpose, and how its security and privacy can be ensured. Re-platforming to use cloud-native services and applications will lead to much simpler architectures, enabling data leaders to shed legacy complexity and adopt privacy by design, building privacy protection into their new data collection and consumption architectures from the outset.
Recent advances in machine learning have been driven by two factors — the availability of large compute power and the collection of large datasets. Cloud makes huge compute power accessible to all. There will therefore be increasing pressure to put data to work and to share it within and between organisations, and the flexibility of cloud architectures will make this provisioning easier. It will be critical to ensure that this opening up of access to data is done safely and in compliance with the growing body of privacy regulation.
The cloud abstraction allows one to forget that data still resides on physical servers somewhere. Nevertheless, managing data residency will continue to increase in importance, with the breakdown of the EU/US Privacy Shield, Britain leaving the EU, and many nations placing tight control on data transfers. Cross-border collaboration on data will therefore require new approaches, such as federated analytics, moving the computation rather than data.