The CEO of Affinio, Tim Burke, talks to TechFunnel.com about the new development of a recently released containerization model for Affinio’s marketing strategy platform. The new model integrates Microsoft Azure Managed Applications into the enterprise marketing stack, allowing global enterprises to use the company’s AI-based technology in their own private cloud. Tim tells us how the new solution works, why marketers should consider adopting a containerization model, and how this model could possibly overtake the SaaS-based model soon.
Tim, along with co-founder Stephen Hankinson, previously developed and successfully commercialized Tether.com, the most commercially successful application of its kind, with over 300,000 paid users worldwide. From 2007-2013, Tim founded and led 26ones Inc. (formerly Quark Engineering and Development Inc.), an IP and product development engine. From 2000-2007, Tim held engineering R&D/design positions with Skillz Systems Inc., Innovation in Design Lab (Dalhousie University), Heidelberg, and Creare. He is based in Halifax.
Danni White: You recently announced the creation of a containerization model for Affinio’s marketing strategy platform. What is a containerization model and how does it work?
Tim Burke: Containerization is a technology deployment model that has been around for quite some time, mostly used by IT departments and software teams. Rather than uploading enterprise-owned data to a third-party vendor’s tech for processing, the vendor sends its tech to the enterprise to run within their firewalls. So you can think of Affinio’s container model as the cloud version of on-premise software.
Before cloud infrastructure existed, technology companies deployed their tech on hardware (servers) that resided on the customer’s premise. This meant that the enterprise had control over it, and critically, could minimize security risks. But today, cloud-based solutions are the preferred deployment efforts and that has given rise to new challenges. Among them, are privacy issues. Keeping marketing data within the enterprise’s walls is now a top priority for enterprises and regulators.
For this reason, Affinio has opted to containerize all of its intellectual property (securely so it can’t be reversed). This allows enterprises to deploy our marketing platform on their own private cloud instances, meaning they can reap the benefits of our AI-segmentation and visualization technology on any and all of their data, without it ever leaving the security of their private data lake. So like the on-premise models of old, the enterprise enjoys complete control while minimizing their infosec risks.
DW: Is containerization new in the marketing world or has it been used before just under another name?
TB: The idea of containerizing marketing applications is relatively nascent, but it is a rapidly emerging technology. For the most part, it is applied to the kinds of marketing solutions that are traditionally available via the on-premise model. But again, the nature of software deployment is changing. Enterprises have been moving to the cloud and to data lakes, both for scale and security reasons. Our solution offers the convenience of the cloud along with the security of on-premise.
No doubt Affinio is ahead of the market in terms of leveraging this deployment strategy, but I don’t think we’ll be the only one for long. We absolutely believe that other martech and AI companies will follow suit because the market will demand it.
DW: The mar-tech industry is in the spotlight now more than ever when it comes to first-party data. Do you think marketers should adopt a containerization model?
TB: Absolutely, I think they should adopt a containerization model for many reasons. To begin, now more than ever, marketers need to leverage first-party data. More often than not what stops them isn’t a lack of desire, but a lack of access. Infosec restrictions prohibit many marketers from ever accessing raw, first-party data, which is why we were motivated to create Affinio Container, to begin with.
When our technology is deployed in the enterprise’s private cloud, it can be hooked into any first-party data set, and marketers can run analytics against this data set almost immediately.
Containerization answers the number one need for marketers seeking to leverage their first-party data. In our model, all PII has stripped automatically, so when end users analyze their data and access the visualization outputs, they see only aggregate traits, attributes, and behaviors of the users. In other words, they can see all of the nuances and stories that are hidden in the first-party data, but absolutely no PII. As such, we envision containerization as a secure “bridge” or “middleware” between the marketers and their raw data lakes, which reduces the infosec and privacy risks.
There are other reasons why marketers should adopt containerization. For instance, when our technology is deployed in the enterprise’s private cloud, it can be hooked into any first-party data set and marketers can run analytics against this data set almost immediately. And, because it works within the marketer’s infrastructure, they can act on the insights immediately, by say, pushing segments through our interface back into their data lake or marketing clouds. So for marketers seeking to move faster in the market, containerization lets them leverage their first-party data to gain insights quicker and take action on those insights — all while reducing privacy and infosec risks.
DW: With your platform, marketers can benefit from segmentation and visualization without sharing first-party data directly. Do you think this model will overtake the SaaS model soon?
TB: Yes, I believe this model is the future. The SaaS model in marketing has many advantages, such as the speed of deployment, simplicity, and scalability. Many marketing SaaS platforms, like DMPs, also benefit from the “network effects” of co-mingling data across multiple customers and suppliers, making it easy for marketers to buy and activate data. However, with GDPR, the California Consumer Privacy Act and increasing data privacy concerns, many global enterprises have changed the ways they use and share their data.
For instance, they are increasingly reluctant to share first-party data in “co-op” models. And they’re shying away from using external vendors to manage and analyze their data. Although the container deployment eliminates some of the benefits of the traditional network effect, the security and privacy benefits are very compelling and will be the driver of adoption by other tech vendors.
DW: Where do you see your platform leveraging this and other types of new AI-related technologies in the future?
TB: Our container solution allows us to apply our AI technology to massive, global first-party data assets, which just isn’t possible in a traditional hosted/SaaS model. This allows us to work with the global Fortune 500 companies and enable them to own their insights and strategy through our tech, which is why we developed this solution in the first place.