Making big data smart

Challenging, isn’t it?

For about 10 hours of effort, you should be analyzing valuable data you previously didn’t have, and be poised to make serious optimizations to your campaigns.

It should come as no surprise that the digital analytics and marketing industries are talking about big data. After all, big data is a very exciting and popular topic, with many industry experts writing and speaking about big data, and why it’s important. All of this content and popularity is good for the big data movement; however, the conversation could still be improved.

By its definition, the collection and analysis of big data is supposed to be large, complex, and difficult to work with. From the outset, this already seems like a conversation about to get extremely technical, costly, and time consuming (I can see the « non-data geek » executives in the room checking out right about now). In my experience, none of these attributes correlate well with successful projects. This is why we should shift the conversation away from big data, and talk more about smart data.

So What Is Smart Data?

Smart data is a subset of big data and should be a part of every big data project. The goal of smart data is to quickly show a return on investment and lay the foundation for ongoing big data projects. More specifically, smart data has three attributes:

  1. Smart data requires at least two data sources. Just like big data, smart data will combine at least two previously disconnected data sets.
  2. Smart data is efficient. It looks for the largest potential impact, with the least amount of resources.
  3. Smart data is actionable. The analysis and insights that come out of your smart data project need to have a clear objective, and one that can be acted upon in a fast, efficient manner.

How About an Example?


Orginal on Clickz

Making big data smart

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