We live in an age of Big Data and more and more companies in a wide range of industries are making it a point to collect as much data as they can about markets, transactions, their website's users and customers.
When it comes to customer data, retailers are a blessed bunch because they have greater opportunities than many to collect this type of data.
Data has become such a popular topic in digital marketing circles that we're running out of metaphors. But whether you think of data as the oil of marketing, a firehose of numbers or the next gold rush, chances are that your organization is still coming to grips with the possibilities and realities of "Big Data."
As a great end of the year Google Hangout, join us December 13, 2012 at 12:30 EST as we discuss cutting-edge data techniques for supercharging advertising used by global corporations for marketing as released in the latest Econsultancy report "Best Practices in Data Management." Data Management Platforms (DMPs) and Audience Management Platforms (AMPs) are all the rage, but getting the most out of data for audience segmentation, insights, and targeting takes more than just a relationship with a vendor.
Data is everywhere. As the cost of storing and collecting data decreases, more of it becomes available to marketers looking to optimize the way they acquire new customers and activate existing ones.
In the right hands, data can be the key to understanding audiences, developing the right marketing messages, optimizing campaigns, and creating long-term customers. In the wrong hands, data can contribute to distraction, poor decision-making, and customer alienation.
Over the past several weeks, I asked over thirty of the world’s leading digital data practitioners what marketers should be thinking about when it comes to developing a data management strategy.
The result is the newly available Best Practices in Data Management report. A few big themes emerged from my research, which I thought I would share.
Although it's fast becoming a hot position, ask different people what a "data scientist" is and you'll get different responses. Invariably, you'll hear buzzwords like Big Data, Hadoop and Cassandra, as well as technical terms like predictive modeling and regression analysis.
If you're not familiar with these, the role may be something of a mystery, but it is an important and lucrative one at many tech companies.