"Not everything that can be counted counts, and not everything that counts can be counted." - Albert Einstein
Curiously I Googled ‘big data’ vs ‘web analytics’ to understand a coherent relationship between the two.
The interest for ‘big data’ search over a time has taken a steep rise that may be attributed to unpresented hype. However, what is more surprising is that “the search interest” for ‘web analytics’, appears to be flat. Notwithstanding the fact that big data originated from the internet, Web Analytics, which is essentially a study of tracking visitors’ behavior, does not scale up as much as big data. Ideally, there should have been progression in web analytics since both complement each other, but I don’t see this happen if we look at the trend.
With artificial intelligence, cognitive computing, and deep learning, big data is advancing with lots of promises. Web analytics too is getting smart as analytics has gone through a significant improvement in data collection process over a period of time. Both have the same objective – to help workforce take more informed business decision. Nonetheless, web analytics is crawling while big data is taking leapfrog.
What really warrants the growth of web analytics? To my understanding, one of the important reasons among others is – lack of technical understanding in using analytics, and our inability to interpret the findings aligned with a business goal. In other words, there is an obvious gap between privilege of recommendations/suggestions and power of decision, possibly due to “complex hierarchy” of an organization. If I were granted one wish, it would be a “holacracy” model that I want for a data-driven company to adopt like Zappos has embraced despite all criticisms. This may bring in a fair understanding and acceptance of analytics, which I believe, an integral part of progression to the web analytics to the next level.
I’ve worked with both good and bad web analytics team all through my experience. To my utter surprise, I could not see the best ones were ever empowered enough to provide business insights at tactical level. They were asked to “support” a business. They get locked in their cubical circle to perform KRAs. Sorry, but it’s an astute reality that these poor guys, like express mails, are only used to deliver the data and messages!
Inevitably, exasperation prevails on them for their “under-ability” tasks juxtaposed to those awful analysts whose “extra ability” looks exaggeratedly ‘extra’ than necessary. In our industry, its unfortunately true that many professionals in digital marketing don’t understand the data collection process, real time analytics, cohort analytics, attribution model, interest level, heat map, tagging, market segmentation, and so many other real metrics which are of paramount importance today.
All they fathom is traditional metrics such as sessions, page views, bounce rate, time spent, etc., which I believe, are losing their grounds, as browser is becoming smarter and so is analytics tool. Add to this apathy, they feel comfortable by looking at numbers, graphs and share insights based on their prediction of the future impacts? The basics descriptive metrics can no way lead to predictive and prescriptive analytics, and an organization should never pin its hope on such analysis.
In gist, web analysts can never become the trailblazers of big data, nor do big data complement the growth of web analytics unless these bottlenecks are eliminated, or at least minimized.
Web 2.0, SMO, SEO, Google, yahoo, semantic search