Yes, you can build better trust in your data —but only if the organization can collect, structure, analyze and protect data in the right ways with the right tools. Modern supply chains integrate a host of related parties (and human error) into knowledge systems, which include business metrics, supplier sustainability performance data, and even intellectual property (IP). This is why systems and data must be built around secure access and verifiable trust.
Who wouldn’t want to eliminate time-consuming admin tasks in favor of strategic talent management activity? It’s no surprise HR executives are so excited about the promises of AI technology for automation efficiency. Along with the anticipation, though, keep this caveat in mind: AI and automation technologies can deliver if, and only if, they run on a foundation of clean, secure data.
Everyone agrees GDPR has changed the digital marketing paradigm. In the process, it’s also changed the marketing game—providing smart marketers an unprecedented opportunity to shine. While seeking unambiguous and continuous opt-in from customers, marketers have the obligation (and privilege) to take scrupulous care of customer communication. Those that do this well will reap big rewards.
And in HR, the variety, scope, and volume of data collected and stored makes this permission even more important. From personal details to payroll and career information, HR is a hub of data, and therefore a security vulnerability that needs to be tightly controlled.
It's a win-win situation in its best sense: Migrating to the cloud, while doing good for society – a German food charity is doing just that. While this may set a precedent for other NGOs, each organisation – whether big or small – must find its own data model and ensure that the asset is always available, trusted, and secure.
Why do you work in HR? Most probably because you are interested in people and their welfare. You’re a people person. AI (Artificial intelligence) will probably become your new best friend in the next couple of years, but AI will never be human. Its limitations need to be understood and taken into account.
Different people need and work with different data. Having an understanding of the different datasets that exist across an organisation and the characteristics and quality of the data that could be used to train and run AI systems is key, not just to unlocking the insights and power from data that exists, but also to realising the full potential of the latest data-driven technologies.