Big data can be thought of as the “crime scene investigator” of the data science world. If an organization is hacked and sensitive data is compromised, big data analytics can be used to collect huge amounts of information about systems, users, and customers to provide insight into what went wrong and how to prevent it in the future. However, while big data can help solve an issue after it has occurred, it won’t prevent it. Organizations are left to clean up the aftermath of the breach, including financial loss and a tarnished reputation. This is where big data fails to live up to its expectations. It can provide hindsight, but it won’t alert managers to a breach as it’s happening. Big data will not secure anything on its own. One way to ensure systems are truly secure is through training. Employees should learn best practices, how to spot a phishing email, and the dangers of clicking on unproven links. However, cybersecurity training at the human level is essential, not a cure-all. Humans make mistakes. Employees are tired, distracted, or in a rush. However, machine learning can fill in the gaps traditional training leaves. This technology relies on algorithms that learn and update in real time, which allows computers to adapt to situations without being explicitly programmed. Because of these abilities, machine learning can identify a data breach as it’s happening.