Working Together in a Data-Driven Environment

  |  February 14, 2011   |  Comments

Three defining characteristics will help platforms work together and grow the adoption of data-driven advertising in the coming year.

Last year was the year of platforms for the online advertising industry. Demand-side platforms, supply-side platforms, data platforms, etc. are all gaining adoption at an accelerated pace. In addition, new technologies and advertising solutions have been introduced for just about every facet of a marketers' needs. As the inventory available across ad exchanges increases and publishers look to adopt audience-based targeting and buying models, the data-driven ad market is ready for a big leap. However, just like any technology enabler, significant barriers to growth exist in making all of this complex technology work together.

For example, I see two challenges facing us head-on in 2011: the lack of standards and lack of openness. In order to work together, we need standards that ensure the flow between all data-driven advertising players from DSPs, ad exchanges, data providers, yield optimizers, analytics to ad servers, and so forth. Plus, in order for standards to be useful, we need to see broader adoption. The short history of high-tech is littered with abandoned standards such as Betamax and Firewire.

In December 2010, the industry took a large step forward with OpenRTB, a project designed to provide open industry standards for communication between the buyers and inventory providers. While this is a great example of companies working together to remove barriers to growth, this is not enough to drive industry-wide adoption. Bottom line: we need to do a lot more work to remove technical roadblocks that impact marketers' ability to achieve the best solution for their needs.

Here's what I see as the three defining characteristics that will help various platforms to work together and grow the adoption of data-driven advertising in the coming year:

Transparency: Platforms can't be black boxes. For digital advertising platforms, everything has to be a lot more transparent: how much you charge for each bit of data; on what inventory and what sites are the ads shown; what the performance is and how it is achieved. Better yet, just bring all the data to the table so that advertisers and media buyers can easily dig into the data and make more informed campaign decisions.

Inter-operability: Platforms need to be compatible, meaning that the different systems and sub-systems can work together like the plumbing in Bill Gate's house. These systems can be internal or external systems. And when we say work together, we really mean working together with the full potential of each end. This means the data and events from one system flow to another platform with no loss, no delay, and no dropped bits. Transactions and data bits always add up, show up in reporting, and are factored in analytics and optimization at all times. The optimization done on one side can be propagated over to the other side in a compatible, unhindered fashion. This might be a lofty goal for this year, but until we achieve this level of integration, we'll be stuck in the pre-Web 3.0 world for a while longer.

Best of breed components: This is probably the most controversial one. I'm in the camp that argues a sheer number of choices shouldn't define what technology platform is the best. It's the number of best of breed components that matters. Clients prefer systems that enable them to choose the highest quality components and have the flexibility to fit all the right pieces together. One gold star for each best component: best UI, best data warehouse, best throughput and reach, best security and privacy, best optimization, best number crunching, best visualization and reporting, and so on.

The ability to work together and drive standards is critical for all of us – advertisers and media buyers, publishers, data companies, and other value-add solution providers. Marketers should use these three characteristics as criteria when evaluating the platforms they plan to work with. After all, if they want to differentiate themselves from others, it's important to be a sophisticated consumer that demands these factors in order to create a highly-customized solution built specifically to their vision and company needs.

For publishers, it's time to drive home the value of transparency. The transparency of inventory should be rewarded like an organic apple. It may not be the shiniest in the basket, but it's safe to pick and consume. Transparency can also mean giving off additional insights, value-add data, or visibility into inventory structure. Everyone has to adopt the same set of standards to allow these data bits to flow freely through the plumbing system.

Data providers and other solution providers also benefit from transparency as it means more opportunity and visibility in creating the value-add service in the first place. Inter-operability means the largest amount of your services can be preserved and piped through unthrottled. The best component principle also means more healthy competition on technology and quality, which ultimately rewards innovation and hard work.

This is a crowded ecosystem with many great innovators, and the faster we can work together, the better the opportunity there is to grow data-driven advertising and achieve more market share. In the end, everyone should be a winner as we speed up adoption and drive up the efficiency of digital advertising with yet another wave of innovation and value creation.



Xuhui Shao

As Chief Technology Officer at Turn, Xuhui Shao focuses on the power of optimization, machine learning, and advanced analytics solutions in driving new business models, products, and services across all industries. Xuhui is responsible for architecting the machine learning and optimization technology to deliver the most effective data-driven digital advertising in the world. He is passionate about the dynamic online advertising community and works closely with industry leaders developing data transparency and consumer privacy protection.

For the last 12 years, Xuhui has practiced research and development in machine learning, statistical theory, and computational intelligence for Fortune 100 companies in various industries from banking, finance, online retailing, healthcare, insurance, marketing, and online advertising. As the lead inventor and co-inventor of three awarded patents in the areas of advanced analytics and optimization, Xuhui is a recognized expert in harnessing data and transforming analytics into actionable insights and optimization strategies.

He earned his bachelor's and master's of science degrees from Tsinghua University, Beijing, and his Ph.D. in electrical engineering from the University of Minnesota.

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