Is data analysis same as data analytics?

I have been hearing the use of term “data” in a rather vague form in MIS (management information system), ERP (enterprise resource planning) and so on. But, during the last four years it has gathered increase attention by the industries across geographies. Its advent is in sync with growth of data users across the globe, primarily from emerging economies. Common public has grown attached with internet and Smartphone to such an extent that makes it worthy to note exponential rise in the user base. While, at first this looked like a trend that was forth coming, but lately it has transcended to a different domain of its own which is IoT (internet-of things). Vast amount of digital footprints left by individuals operating on the internet with use of even remote devices are up for grabs to analyse. An article dating back to 2014 showed that IDCs digital universe predicted growth in world’s data to 44 zettabytes by 2020 and IoT would contribute 10% of it. That is a huge number in absolute sense. Data storage and securities are the issues that come along with it and require analysing them on the fly.

So there is huge data, but how does one make sense out of it. There is lot of noise in the data, which it makes it a gruesome task to explore and visualise the findings. Statistics is the basic technique that has been used to analyse the data, but data science has added new dimension to the existing knowledge framework by proposing a scientific means to explore the data and present its findings. It is confusing to define boundaries associated with buzzwords such as data science, machine learning or artificial intelligence. At, first it occurred to me that they are almost the same, but there is a fine line that defines them into unique specialisations. Same is the case with data analysis and analytics. Once, we are given data there are two ways of basic analysis i.e. descriptive and prescriptive. The descriptive analysis reports the data in its crude form and provides information evident at the first instance. But, there is nothing great with it; we want to know something that is not evident from first look. Prescriptive analysis is a diagnostics mechanism that provides recommendations and inferences by delving much deeper into data. While, these two mechanisms have their proven worth at different phases of decision making, the important thing is they both are data analysis. Usual MIS reporting to upper management is a process and more or less doesn’t change with time; it is a form of data analysis. Visualisations have become so important for managers that, sometimes an inferential chart is best way to send the message.

Performing data analysis on humongous data is not a feasible alternative, rather one must move towards a more detailed manner for evidence based analysis. Data analysis is a subset of data analytics that initiates with basic data cleaning, analysis and further moves towards applying specific statistical tools and machine learning for providing better insights. It is becoming an integral part of industry decision making to use data analytics for both quantitative and qualitative analysis. It would be right to say that data analysis has moved ahead and become part of a larger data analytics domain, that is suitable answer to future data explosion.