As analytics becomes more and more integrated and data continues to emerge as businesses' key consideration in decision making, it's likely that many more jobs will require some amount of data literacy. From the ability to understand simple performance graphs to entry-level data manipulation, non-data professionals will have to continue to adapt and pick up new skills to harness the power of data.
Nowhere is this truer than in research functions where Market Researchers are already being encouraged to try their hand at new technologies. In many businesses, research and analytics teams work closely. Both functions communicate fluidly to produce the best work and eliminate any data/information silos. Analytics embrace new technologies while Researchers tend to opt for me traditional, established tools. However, this can lead to Analysts or Data Scientists being asked to complete the task which Researchers may want to complete themselves. One way to overcome this is by learning a versatile tool in which Researchers and explore data for themselves.
R is quickly becoming the Market Researcher’s ‘go to’ programming language. The language was created in 1995 with the premise of user-friendly, flexible deployment central to its design. It is an open sourced language so provides freedom to users and comes at no cost. It is regarded as one of the most powerful programming languages and used, to some extent, by big boys such as Facebook, Google and Microsoft for data manipulation, visualisation and statistical analysis. In R you are free to do what you’d like with your data. As such, Researcher’s could save themselves a lot of time being able to run valuable tasks typically left to data professional who might take more time than desired to respond to requests.
R can be used to complete processes much faster in both quant and qual research. Techniques such as ‘text mining’ can help qual Researchers analyse large amounts of text quickly and quant researchers can take advantage of R’s powerful statistical environment which allows much greater functionality than SPSS.
It is clear from the candidates that we speak with, the possibilities in R are almost endless. However, having said that, it is also clear that learning R to help you perform more effectively in your role can happen in days. How stuck in you get with it is down to you!
In many businesses ‘research’ and ‘analytics’ teams sit well alongside each other and cross-fertilise skills – the former adopting new technologies to enhance traditional techniques, the latter filtering torrents of data with a critical researcher’s mind.