Hadoop

data

10 actual uses of big data

First off, what is it?

Well don’t let anyone tell you it’s down to sample size, or about measuring everything. It’s about combining datasets (sometimes ‘dirty’ ones), contrasting them in different ways, and doing it as quick as possible.

Sometimes this necessitates great computing power, but not always. You can read more about such technology as Hadoop and GreenPlum in this nice little article).

Datasets are multiplying as we measure lots more than we used to. This means our thinking has to broaden – no longer is ‘what can we do with our database of email addresses?’ the question, rather ‘what data can we look at to give us the best idea possible of a customer’s stage in the buying cycle and what they’ll be receptive to next?’

The definition of big data isn’t really important and one can get hung up on it. Much better to look at ‘new’ uses of data.

So, here’s some examples of new and possibly ‘big’ data use both online and off.

You (probably) don’t need a rockstar engineer

If you work in the tech industry, you’ve probably heard somebody lament just how difficult it is to find “good” engineers these days.

Thanks to the booming internet economy and the fat wallets of companies like Google and Facebook, it’s a good time to be a software engineer. There are more jobs than viable candidates, salaries and benefits are high as a result and the best engineers have no shortage of opportunities to work on interesting things.

Econsultancy Google hangout: big data and best practices in data management – Thursday, December 13

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.

What is a data scientist and do you need one?

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.

Best practices in data management: report

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.

Are relational databases a thing of the past? Not quite

The web is literally built on databases. The majority of your favorite websites are probably driven by one or more relational databases.

But there’s a “movement” afoot. Its goal: provide a superior alternative to popular RDBMSs like MySQL and Microsoft SQL Server.