Many times when people ask me what I do, and I tell them and they say “Oh, your in IT”. I then give a contemplative pause and reply ‘Not really’. To say an analyst or statistician is an IT employee is like saying a HR rep is in IT because they use a computer for the forms, or a communications expert since they put speeches on everyone’s computer.
IT is Information Technology. Often times we think of computers as being IT (which they are), but so are cell phones, faxes, and printers. Again tools everyone uses and IT professionals specialize in maintaining. IT professionals are usually the ones setting up those tools, maintaining them or even tweaking them for new purposes.
Analytics (and thus the statistical and mathematical side of data science) is focused around discovering information by way of applied mathematical statistics. Thus the analyst isn’t by default concerned with setting up and maintaining tech to transmit information, like phones, faxes, and computers. They do use any technology for data collection and computer in particular for data processing. But this does not even mean that an analyst MUST use computers for their analysis. There are some situations where if the data is small enough or the analysis is simple, the processing can be done by pencil and paper. One example is determining how much a business should re-order materials given inventory costs and historical demand (note: the end result of this analysis is a decision and not just information display).
Although generally the techniques and data used by analysts require computers for speedy processing, this does not count as doing IT work. Like many domains, analytics and applied statistics can reveal useful information and relationships between factors. IT can also be used to store data across many domains as well. So both can impact one another, so let’s discuss how they DO intersect.
Statistics can be applied to computer processes, such as determining what programs are likely to run at a certain time of the day or what memory sections tend to be accessed together. And statistics can be apart of IT such as auto-sorting emails based on expected importance value, or suggesting new programs based off of your computer usage. IT can impact statistical analysis by providing faster processors, more memory, or even access to data.
All in all confusing a new profession with some of the tools used in that profess ion is understandable. This compounded with the fact that more and more data scientist have an IT background rather than a statistics background as they use to, is contributing to this confusion. But ultimately just remember this analytics and science are based on analyzing, and analysis can be done by hand (ie without technology, like when you did your math homework) or with a computer. IT is based on technology, and most of us don’t use tech to conduct analysis when we are on Netflix or surfing the web.