Big data is very valuable, but it can' t do everything. The numbers can only take you so far. Even as big data gets even bigger, don’t forget the value of big ideas based on true human insight and how they can be what really drives social media content and engagement.
Marketers and advertisers might have something to learn from McCandless because of how his work communicates complex ideas with very little effort from his viewers. “People across a range of industries, not just science, are struggling with their communication because their output doesn’t compete with what people see on a day-to-day basis,” McCandless told Guernica . “Some of the commercial work I do is helping people to improve their presentations and add some design thinking.”
There’s no doubt that the Internet of Things has arrived. From Amazon’s Dash button to wearables to smart parking meters, examples of IoT device innovation are sprouting all around us. But we're still, essentially, in the dark ages with this technology. We’re just beginning to see that the real value in IoT isn't hardware-centric; it's the data that comes with it that's of most interest.
Many of us have been learning more about the data lake, especially in the last 6 months. Some suggest that the data lake is just a reincarnation of the data warehouse—in the spirit of “been there, done that.” Others focus on how much better this “shiny, new” data lake is, while others are standing on the shoreline screaming, “Don’t go in! It’s not a lake—it’s a swamp!” All kidding aside, the commonality I see is that they are both data storage repositories. Beyond that, the table below highlights some key differences. This is, by no means, an exhaustive list, but it does get us past this “been there, done that” mentality. A data lake is not a data warehouse.
While there’s no question that the buzz of “big data” is still going strong, how well is big data actually catching on? To answer this question, we’ll review some of the recent research to see what’s trending in the world of big data.
Big Data has become one of the key buzzwords for businesses everywhere over the last few years. With data of all kinds being produced in record amounts every year, collating and analyzing this information will give businesses more insights than ever before into their customers, their industries as a whole and perhaps even let them predict what might happen in the future.
This week I moderated another Social Media Today webinar as part of their Best Thinker webinar series, this time on the topic of: Stop Drowning in Digital Data: Social Listening for Campaign Measurement. This webinar was sponsored by Brandwatch and featured panelists from Brandwatch, Sony Electronics, and Visa. We discussed why, and most importantly what, you should measure for your campaigns.
The quickest way to make a CMO roll their eyes is to tell them they should be making more data-driven decisions. Of course we should, everyone knows that. Unfortunately there are a lot of “buts” attached to making this a reality.
Big Data as a concept is characterized by 3Vs: Volume, Velocity, and Variety. Big Data implies a huge amount of data. Due to the sheer size, Big Data tends to be clumsy. The dominating implementation solution is Hadoop, which is batch based. Not just a handful of companies in the market merely collect lots of data with noise blindly, but they don't know how to cleanse it, let alone how to transform, store and consume it effectively.