Insight vs. Information vs. Data
I came across an interesting article written by Michael Wu over at Lithium that goes into detail (in a not overly geeky way) on why there is a significant need for an even greater amount of what is commonly known as “Big Data”. The basic argument is that companies are looking for Insight, which is a subset of Information, which is a subset of Big Data.
Big Data is the noise that now envelopes us; Tweets, Facebook posts, comments on User Forums, any activity on the myriad of “Social Networks”, which some vendors who track this space now claim has over 200 million separate sites (really?). This actually tracks fairly well to my last post (see below), where I blogged about the need for relevancy based on setting realistic expectations in terms of what Big Data can actually deliver. There are now over 1 billion users on Facebook generating a staggering amount of content on a non-stop basis, the amount of data (Tweets) generated by Twitter is absolutely vast (low signal to noise ratio), on a different but still large scale, you have mobile developer user groups with millions of members posting constantly (smaller volume, but very high signal to noise ratio). The list of sites generating “social data” is extensive, and growing at exponential rates.
What is particularly interesting (and Wu’s article doesn’t specifically call this out) is the shift of analytic focus from B2C to B2B. This was actually called out in a blog post by Twitter but seemed to fly under the radar at the time (except for those interested in specifics of Twitter’s API changes driven by the need for authentication). It feels like the enterprise side of this ecosystem is finally starting to sort itself out in terms of coming to grips with how Big Data can be used to more effectively engage with their customers. I’ve been in the technology domain my entire career, and have seen the enterprise space get caught off guard multiple times (the unexpected rise of the World Wide Web, then a few years later the sudden ascendance of mobility, now its social media and the analysis thereof). The enterprise market does eventually engage with the technology as it matures, and then it becomes embedded and institutionalized.
The step that is still missing is the logical application of social data to operationalization of how an enterprise interacts with their customers. Having loads of data is great, the more data presumably the more information, the more information presumably the more insight, but that is still one step short of the final goal. I’ve seen lots of enterprises try to claim the moral high ground on social media (“look how much we post on Facebook!”), but all they do is post. The whole point of social media is that it’s Social. You have to engage in a meaningful dialogue (not a monologue, which seems to be the standard at the moment). It’s easy to run detailed analysis on Big Data since most of it is algorithm driven, the challenge is how to create a value-add response to a consumer in the context of social media, which is something an algorithm is not necessarily going to deliver. That requires a CSR (customer service rep) that has access to timely social data that has been thoroughly scrubbed, contextualized, and operationalized. Once this process is defined and institutionalized, then you will start to see Big Data reach its full potential.