Customer Data Collaboration Can Be Both Effective and Safe

Kaz Ohta, Co-Founder and Chief Executive Officer, Treasure Data  

In today’s data-driven world, businesses are exploring innovative techniques to acquire customer insights. One powerful trend to emerge from this is data collaboration, where businesses combine diverse data sources to generate intelligence that informs decision-making. By sharing with internal and external partners, businesses can create even more value with their data.

Nevertheless, this strategy does come with potential risks to the security of the data. Because sensitive information is being exposed to more parties, the likelihood of data leaks is increased. These risks are compounded by the rise of generative AI as well, as businesses are beginning to share their data with AI vendors to create personalized content. Consequently, it is imperative for brands to find a balance between collaboration and security to maximize the benefits of data while minimizing potential harm. Here’s how brands should think about data collaboration strategies going forward.

Get to Know Your Data Collaboration Tools

Data collaboration has become a more widespread practice in recent years due to emergent data enablement technologies. One such solution is the data clean room, which offers a secure and controlled environment enabling businesses to share sensitive data with external parties. Data clean rooms and similar solutions are on the rise because they power retail media networks that have become increasingly popular in recent years.

If data clean rooms and retail media networks have transformed the marketing landscape, then generative AI technology has the potential to truly revolutionize it. With the ability to create highly personalized content at scale, generative AI empowers marketers to deliver relevant experiences that not only satisfy but truly delight target audiences. It goes without saying that these marketing use cases rely on customer data, which intrinsically means that brands are sharing their data with third parties in order to leverage its capabilities to the fullest.

Understand the Potential Downside of Data Collaboration

While these technologies and platforms are designed to ensure data privacy and confidentiality, they are not entirely immune to potential threats. One of the biggest risks with data collaboration solutions like clean rooms is the possibility of data misuse or unintentional leakages. Retail media networks and generative AI platforms have protocols in place to protect against these types of breaches, but they do not necessarily guarantee data privacy and security. Furthermore, there is the possibility of a security lapse occurring in a brand’s underlying data infrastructure, such as cloud-based data platforms that connect to clean rooms and generative AI platforms. This can result in the accidental exposure of sensitive data to unauthorized parties. 

Imagine an alcoholic beverage brand using a travel media network to determine which market or demographic to target for its new seltzer water. If the alcohol brand were to leverage underlying data that contained personally identifiable information, then the travel brand that powers the media network would be collecting private information in the commingled data. Pharmaceutical brands, meanwhile, have a heightened duty to protect customer data, as they often work with patient health information to help ascertain which healthcare organizations would likely prescribe a certain drug. Data leakage involving sensitive patient data could create a serious legal quagmire that must be avoided at all costs. These are the types of unintentional data breaches that can taint data collaboration efforts. 

Strengthen Your Data Collaboration Security and Privacy

When companies conduct security assessments of their data collaboration tools and practices to identify and address potential vulnerabilities, they must look at their overall marketing tech stack to ensure a secure environment. At the heart of a company’s marketing tech stack lies a customer data platform (CDP) or data management platform (DMP). CDPs and DMPs have become the nexus of a brand’s data infrastructure because they can consolidate first, second, and third-party data to create a “golden” profile of the customer. Through APIs, CDPs and DMPs are also the go-to technologies for brands to connect their data within clean rooms, generative AI platforms, and other data-sharing solutions. For example, marketers can connect their CDP to a data clean room to anonymize first-party data, receive third-party audience data, and analyze it alongside other data sources.

Because brands use these technologies to collaborate with outside parties, it is essential to establish robust access controls and monitoring systems to prevent unauthorized data access or data leakage. This becomes even more complicated with privacy rules that require companies to obtain consent from customers to use their data for marketing-related purposes. Businesses that are considering data collaboration strategies should prioritize CDPs that boast features like customizable workflows, access permissions, consent preferences, and personally identifiable information hiding to ensure confidentiality and integrity. For pharmaceutical and other healthcare-related brands, data security tools that are HIPAA compliant should be a focus. These data governance tools will give brands holistic data governance enforcement necessary to secure data across all sources and channels, including data clean rooms and other data collaboration platforms.

Data sharing and collaboration strategies are viewed by many as the future of marketing due to the deprecation of third-party cookies that continues to limit targeted advertising. With the loss of this valuable data source, brands are turning to alternative means, such as data collaboration with partners and vendors, to gain deeper insights into their customers. While data collaboration can be a potent tool for marketers, it also introduces potential security threats that cannot be overlooked. Brands must ensure that they have the appropriate security measures in place to protect their customer data when leveraging these solutions.

Kaz Ohta is the Co-Founder and Chief Executive Officer at Treasure Data. Kaz is a graduate of The University of Tokyo and is a customer-centric entrepreneur with the heart of computer science. Kaz can be reached at his LinkedIn or Treasure Data’s website.


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