Credit: CCO 1.0 BY JEFERRB
The expanded growth of Internet of Things (IoT) devices
In recent years, Internet of Things (IoT) devices have become commonplace in our daily lives. We wear fitness trackers, have smart home thermostats, and even use medical devices that remotely transmit health information to our care providers. In 2015, there were 15.4 billion IoT devices in use, but analysts predict that number will double to 30.7 billion by 2020—and grow to 75.4 billion by 2025. For more than a decade, there have already been more internet-connected devices in the world than people.
IoT technology is valued on the user side for the conveniences it affords. It is valued by companies for the potential to learn more about their customers. Seems like a win-win situation for both parties. But on the backend, there are serious matters to consider—namely, what to do with all of the information that these devices abstract and how one can keep it secure. This presents a very real question: how do companies manage customer data for IoT devices?
The high priority of keeping customer data secure
Compliance is key. It is not enough to simply abide by Federal Trade Commission (FTC) standards and regulations. The stakes are higher now for companies because customer data is even more sensitive. Since IoT users are deliberately or inadvertently sharing real-time and sentiment information about themselves, there is a higher bar of security needed. Not only should measures be put in place and revisited regularly, but protocols should be updated as needed. Even just one data breach can erode customer confidence overnight.
The “Secure by Design” policy report from the UK’s Department for Digital, Culture Media and Sport, advocates for global industry best practices that would be transparent about how customer data is being used and collected. Its top recommendation is that devices should not allow for passwords that can be reset to default factory values.
A strategic data governance plan, with clear infrastructure and processes to utilize, protect, and maintain consumer data is recommended by developers at IBM. They are reminding companies of the importance of ensuring that data is “effectively used by intended stakeholders and not misused by others.” To this end, many in the industry are advocating for an IoT Bill of Rights that underscores transparency and two-way communication between company and customer.
Managing data with purpose
Not all data is useful—having a clear organizational strategy in place will help you identify the “good data.” In the course of regular device operation, companies are collecting a large volume of information about their users. Much of that is what is termed “dark data,” or information assets that are not being utilized. To avoid a dark data buildup, experts recommend querying your database for insights you need, so that you can surface information that’s useful to your business..
IoT data can help you correlate customer behavioral patterns—such as how often users engage with a brand’s app before making a purchase. Other useful details such as geographic location of customers can provide value to an organization when thinking about how to create targeted marketing collateral.
While targeted data management can help companies identify customer usage patterns, it can also help identify company weaknesses in a more authentic way. Using analytics tools will enable you to identify where, when, and why customers are compelled to engage with your brand. Further, linking IoT data to your existing tech infrastructure will allow you to build a complete user profile—all in one location.
Managing data with machine learning techniques
The benefits of IoT data are significant, but the tradeoff is how your organization will select, sort, and prioritize data. Smart algorithms can enable machine learning techniques to easily classify and tag data, which can be useful for predictive maintenance scenarios, or even for compliance in heavily regulated sectors like health care or finance. According to Dave Schubmehl, research director for cognitive and AI systems at IDC, machine learning technology “can provide predictions, recommendations or potentially prescriptive actions” for companies. After data has been processed, using customer relationship management (CRM) tools can support long range data management in alignment with your corporate goals.
As IoT devices continue to grow and become even more complex, organizations must be well-poised to adapt and think outside the box. Data security strategies and measures must be established and enforced. Data abstraction must have purpose and utility—for both the company and customer. Data analysis techniques must be agile, allowing for real-time analysis of information assets. Finally, modes of data collection and sharing must be transparent—even for metadata. IoT has simplified or enhanced many aspects of daily life for the end user; it is important for companies to manage its complexities on the backend.