Big data is transformative because it provides entrepreneurs with valuable insights into their businesses. Coupled with artificial intelligence and machine learning, big data has the potential to deliver better marketing returns. It can also inform product development, increase efficiency, reduce excess inventory, and prevent fraud. However, big data comes with big risks, including exposure to hackers and other cyber threats. For this reason, many businesses are incorporating data minimization into their cybersecurity and risk management policies. This article will explain data minimization, its benefits, and how to implement it effectively.
1. Limit Data Collection
Focus your data-gathering methods to ensure you only gather essential information. Once data is deemed necessary to collect and keep, controlling access to it is critical. For example, an admin professional may not need access to specific customer data, while a sales rep or customer service agent might. Clearly defined, strict access controls are necessary to protect confidential information. “Access rights should be limited to those who need to know,” said Harvey Jang, Cisco vice president and chief privacy officer. However, assigning access rights often requires collecting additional personal and confidential employee data for authentication. “Businesses should minimize the data they use for authentication, collecting only what is necessary and proportionate,” Jang advised.
Multiple tools and services can help businesses define and control access to their data sets. For example, the best call center software allows businesses to restrict customer data by user type. Platforms like GoTo Contact Center allow companies to customize the data system administrators, supervisors, and agents can see by role. This targeted approach ensures that only the necessary personnel have access to sensitive information, reducing the risk of data breaches and misuse. By implementing stringent data access controls, businesses can safeguard their most critical data and significantly enhance their cybersecurity measures.
2. Verify and Screen Users
Many bulk data collection workflows operate under the assumption that the vast majority of collected data is usable and relevant. In reality, businesses often collect significant amounts of risky data. Data stored on company servers could be inaccurate or outdated, exposing the business to unnecessary risks. Strong data minimization plans incorporate user verification and screening processes to weed out problematic data. For instance, a rideshare company with such tools in place could catch a criminal attempting to submit someone else’s personal details.
Data minimization plans also ensure that only verified, real users can access a platform. This screening, through tools like CAPTCHA checkboxes and puzzles, helps screen out bots. “By verifying your user is human, and directly has interest in your organization, you can better tailor their experience with your products and services,” pointed out Cody Hall, product manager at Synology’s U.S. headquarters. “This reduces the bad data and fake user info that would be collected otherwise, so you can now focus your resources on servicing your true customers.” With these initial assessment procedures in place, organizations can gather only usable information from verified sources, thereby enhancing data quality and reliability while minimizing risks.
3. Manage Data Progressively
User data eventually becomes outdated, but many organizations don’t account for this. This results in databases full of unusable or incorrect information. This outdated data places a burden on IT infrastructure; plus, if it’s used for business data analytics, it will skew results. “When data is no longer needed, it shifts from being an asset to a liability,” Jang cautioned. “There is very limited business benefit to over-retention, just increased cost and risk. Businesses should always consider the cost, benefit and ROI of their data retention practices.”
Data minimization plans that include progressive evaluation protocols can avoid this issue, ensuring users keep data accurate and updated. This approach also simplifies database management for actionable reporting. In the long run, the business saves time and money; it also better mitigates the risk that comes with the growth of user data. Regular audits and evaluations of stored data ensure that irrelevant or outdated information is identified and purged. Implementing this progressive management approach helps maintain the integrity and accuracy of data, enabling better decision-making and operational efficiency.
4. Delete Strategically
All user information has a lifespan, especially in today’s fast-moving digital marketplace. Businesses must consistently purge stale data from their servers to maintain data value and reduce security risks. As a result, all data minimization plans should include strategic deletion protocols. “The best way to deal with future stale data is to manage your file structure with your full data lifecycle in mind from day one,” Hall advised. “For a business, this would mean intentionally structuring your file system to reflect the ebbs and flows of your business’s accounts, opportunities, or projects.”
Decisions on a business’s future direction should always include two things: identifying the new types of data a company needs and deleting outdated information that no longer serves the organization. “Records management and information governance models (e.g., IGRM v.4) are useful in framing how to manage the data lifecycle from creation and collection to use, processing, transfers, sharing, retention, and disposition,” Jang explained. Security and data protection must be maintained throughout the lifecycle. Any time you store data, you are vulnerable to breaches, unverified data, and more. There’s no way to eliminate those risks. However, companies that adopt sound data minimization strategies streamline data collection, gather more valuable information, and reduce risk.
Conclusion
Big data is revolutionary because it offers entrepreneurs invaluable insights into their businesses. When combined with artificial intelligence and machine learning, big data has the potential to significantly enhance marketing returns. These technologies can inform product development, boost efficiency, minimize excess inventory, and help prevent fraud. However, the advantages of big data are accompanied by substantial risks, such as vulnerability to hackers and other cyber threats. Consequently, many businesses are now incorporating data minimization strategies into their cybersecurity and risk management policies to mitigate these risks.
Data minimization involves the reduction of collected and stored data to only what is strictly necessary for business operations. This approach not only helps in minimizing the data exposed to potential breaches but also makes it easier to manage and analyze the data that is retained. The benefits of data minimization include lower storage costs, reduced risk of data breaches, and simplified compliance with privacy laws. Implementing data minimization effectively involves regularly auditing the data held, identifying what is essential, and securely disposing of unnecessary information. This article will delve into what data minimization entails, its key benefits, and practical steps for its successful implementation.
