If you’re not experimenting with big data analytics, you could be putting your business at a competitive disadvantage. Analytics projects have the potential to unearth incredible insights related to customers, competitors, markets and internal efficiencies.
However, by gathering more data and opening up more endpoints, organizations create potential vulnerabilities for cybercriminals to exploit. Running headlong into a new data analytics strategy without considering the inherent security challenges at play is a recipe for disaster. But there is a way to balance the security risks with the incredible opportunities that big data analytics present.
Interest in data analytics has reached a tipping point, pushing it well beyond the “disruptive technology” phase squarely into a business imperative. In fact, many organizations have moved on to more sophisticated analytical initiatives, including the use of artificial intelligence. According to New Vantage Partners’ “Annual Big Data Executive Survey 2018,” 97 percent of executives were either launching, investing in or building an AI-driven analytics project.
Meanwhile, the pressure’s on for slow adopters to pick up speed. The longer companies wait to deploy big data initiatives of their own, the more they’ll fall behind their forward-thinking competitors. Indeed, businesses that continue to hold off on data analytics risk losing out on a key competitive advantage at this important stage.
Esteemed technology research firm Gartner urged companies to embrace big data as soon as possible.
“As data and analytics become more widely adopted than ever before, the potential for business growth is truly exponential rather than just cumulative,” Gartner’s Rob van der Meulen wrote. “Those who fail to act today will suffer not just in 2017, but also hugely limit their potential for growth in 2018 and beyond, as the returns from increased insight, responsiveness and efficiency snowball.”
The possibilities presented by big data analytics are incredible, but enthusiasm for this technology should be tempered by the privacy and security concerns at play. Take, for instance, customer engagement projects that involve gathering large quantities of consumer personal information to make brand outreach more targeted. Without high-quality security measures in place, that sensitive information could be at risk for a data breach. Such incidents are typically expensive for organizations once the cumulative costs of remediation, security updates, legal fees and brand reputation loss have all been tallied.
Data security and privacy regulations present additional concerns as organizations must abide by extensive guidelines dictating how information is gathered, handled and stored. The European Union’s forthcoming General Data Protection Regulation, for example, will place strict limitations on what customer data can be collected as well as penalize companies that fail to respond to a breach in a timely manner.
There’s a lot of ground to cover, which is why security conversations should occur at every stage of the big data journey. With the stakes so high, you want to be sure that everything is accounted for.
Big data’s greatest asset is also its most worrisome security vulnerability: There’s so much data to safeguard that legacy cybersecurity solutions may not be all to cover it all. As projects scale and data repositories expand, cybersecurity applications struggle to keep up.
Furthermore, data lakes are at risk for tampering and breach since it’s so easy to miss malicious activity hidden within these vast repositories. Any viable big data security solution will need to account for these issues and prevent unauthorized access.
One of the most important features companies should look for in a big data security tool is insider threat detection. These capabilities empower businesses to sniff out suspicious behavior that may indicate the presence of a data breach. Cybercriminals continue to use more sophisticated strategies to avoid detection for as long as possible, so companies should seek out security solutions that are able to identify malicious activity without flagging a bunch of false positives in the process.
In addition, big data repositories should be read-only, meaning only authorized users have the ability to alter stored files and information. Tamper-resistant features further prevent external forces from accessing sensitive data and compromising their integrity.
Successful big data programs depend on a delicate balance of analytics excellence and diligent cybersecurity protocols. High-quality solutions like SenSage AP help lay a sound foundation where security best practices are adhered to and advanced analytics can flourish.