data privacy

For years, top executives and thought leaders spent valuable time and money pondering the possibilities and implications of Big Data. Yet, some failed to take their own advice and implement their own recommendations. Now, however, as artificial intelligence and machine learning gain momentum, it almost feels like history’s repeating itself—except this time, inaction could truly mean the difference between sinking or swimming amidst today’s flood of information.

As Nathan Sinnott writes for Entrepreneur, “Machine learning is one element (perhaps the driving force) of AI, whereby a computer is programmed with the ability to self-teach and improve its performance of a specific task. In essence, machine learning is all about analyzing big data – the automatic extraction of information and using it to make predictions, decipher whether the prediction was correct, and if incorrect, learning from that to make a more correct prediction in the future.”

Sinnott adds that, because of the staggering amount of data gathered each day, it would be impossible to analyze without the help of machine learning. Thus, it would seem that AI and ML technologies are the answer to every executive’s Big Data prayers. Instead of allowing the endless accumulation of structured and unstructured consumer data to mount, these tools present the opportunity to bring insight to action. By adopting AI and ML, companies can automate analysis, thereby facilitating a smoother, more precise process for formulating predictions and detecting complications.

Read: From Hype to Happening: Exploring the Realities of Machine Learning & AI”

But, as with any new technology, organizations must also lay the groundwork for consumer trust, as any situation in which personal data’s collected could cause customers to waver. Whether you’re manning the B2B or B2C front, security remains paramount for successful long-term partnerships.

Mark MacCarthy, a faculty member at Georgetown University, an affiliate of the Georgetown Center for Business and Public Policy, and the senior vice president of public policy at the Software & Information Industry Association (SIIA), explains that more data improves machine-learning algorithms’ performance. However, as he writes for Project Syndicate, because “the use of these algorithms creates a technological momentum to treat information about people as recordable, accessible data,” said algorithms run directly counter to society’s innate desire for personal privacy.

No matter the industry, customers must trust that their technology partners will safeguard private data. MacCarthy adds, “Business leaders and policymakers can develop and deploy the technologies they want, according to their institutional needs. It is within our power to cast privacy nets around sensitive areas of human life, to protect people from the harmful uses of data, and to require that algorithms balance predictive accuracy against other values such as fairness, accountability, and transparency.”

Both personal and professional relationships cannot thrive without trust. It’s the single-most important factor for lasting success, especially where sensitive data’s concerned. After all, you wouldn’t share your deepest, darkest secrets with your significant other if they threaten to blast your personal confessions in public. Why would consumers afford you access to private data if they believe you might misuse or abuse their information? Companies might have the solution to their Big Data problems now within grasp, but how they implement said technologies could make or break existing and future partnerships. Establish clear guidelines and concrete parameters from the beginning to ensure miscommunications don’t lead to excommunication down the line.