Maintaining Maximum Relevancy for Buyers and Sellers
Executing at Scale
In short, Technology = executing at scale. For example, machine-learning platforms for targeting marketing messages across multiple channels based on individual user behaviors; or natural language processing to speed interactions with systems; or fully dynamic modular web pages to optimize user engagement and sales; or even open-source machine learning platform development.
Integrating Real-time Closed Loop Data
Fully integrated real-time closed-loop data is a constant challenge. The speed to insight and action continues to shorten plus the variety of data sources and the number of interaction points continue to explode (e.g. wearables, connected cars, etc). Multiple data sources need to be linked and all customer touch points need to be aware of all bi-directional customer interactions that occur, in real-time. For example, when you open the App, is the App aware that you have not read the most recent email sent to you?
Adopting Artificial Intelligence
Real-time and machine learning/AI are two big industry trends that eBay is taking advantage of to drive growth. The continued explosion of the variety and velocity of data demands real-time analysis in order to maintain maximum relevancy for our customers, both buyers and sellers. And of course, the number of data sources coupled with continued advances in computing power are enabling rich applications in the world of machine-assisted or machine-learned applications, e.g. suggested pricing on products, most relevant personalized deals, or identifying and merchandising inspirational products that engage users at different points in their shopping journey.
Always put the business problems first and work backwards from there. And not just today’s problems, but the real leaders in Data will anticipate tomorrow’s business problems.
“The Curse of Big Data”
I call the unmet needs as “The Curse of Big Data.” Data for data’s sake is no longer the need. In the first phase, just simply managing the volume and velocity of data was its own challenge. How will I store this data? How will I access this data? However, acting on this data will drive the next wave of growth. Rapid data exploration/mining, scenario planning, feature extraction, and machine learning are some of the aspects of the next wave of growth for data.
The Primary Focus
It’s an age-old problem, but to reiterate, it’s never about technology for technology’s sake. Always put the business problems first and work backwards from there. And not just today’s problems, but the real leaders in Data will anticipate tomorrow’s business problems. It is also important to think across silos and carefully plan for and distinguish between “what is horizontal” and “what is vertical”. Organizations often find themselves finding a point solution for a specific problem but then realize later the same problem exists somewhere else that wasn’t considered. Those who take a step back and leverage data as offense (operational actions) rather than pure defense (historical reporting/root-cause) will come out on top.
Getting the Most out of Big Data
Big Data: Separating the Hype from Reality in Corporate Culture
Building Levies to Manage Data Flood
Resolving Disassociated Processing of Real-Time and Historical Data in IoT
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