Whether you’re talking about big data, data science or analytics, one thing is clear: there is a lot of buzz and hype when it comes to gaining a competitive advantage from data. In this blog series, I want to focus on the potential of data – both big and small – and applications and approaches to quantitative analysis in the area of communications.
It’s not hard to see why there’s so much confusion around the utility of data. A quick review of the Harvard Business Review’s data analytics section, for example, reveals a volume of articles and perspectives that can leave even data scientists, let alone managers, not tuned in to the industry, confused.
To clear things up, let me first answer this: What makes big data distinct today?
There is some consensus – depending on who you ask – on what makes big data unique, and it comes down to the “Four V’s”: volume, the scale of the data; velocity, the rate at which data is generated and captured; variety, the different forms of data; and veracity, the uncertainty of the data available. Put simply, big data is distinct today because we are generating, analyzing and applying more data more quickly than ever before. To put this into perspective, we now create 2.5 quintillion bytes of data every day. That is so much data so fast that it has been estimated that 90 percent of all the data in the history of the world has been created in the last two years alone.
Another important aspect is the affordability of capturing, storing and analyzing large data sets today. What was once only financially feasible to a handful of organizations is now a viable method of analysis to anyone who wants to make data-driven decisions.
So does a data set need to be big to derive any value? Of course not, and for the most part, the approaches to analyzing big and small data sets are similar, minus some technical considerations that are specific to working with big data.
So where does analytics fit into a communications campaign? Analytics can be broken down into three categories: descriptive, predictive and prescriptive analysis. These map to various communication activities, including – but not limited to – audience research, advanced segmentation, optimization and evaluation. In this blog series, I will discuss some of these areas in more detail and outline relevant approaches, whether it’s applying a data mining algorithm for advanced audience segmentation or using basic statistical analysis to measure content performance on social channels.
The veil of big data and data science is thick. Hopefully, this series will cut through some of the noise so PR pros can approach data systematically and use it as a strategic asset.