When data teams are well-organized and structured to succeed, the insights they can bring to companies are far reaching and significant. The challenges that these teams often face in getting to that point, however, can often be significant. Predicting when these problems might arise and investing energy in doing things right from the start has the capacity to greatly improve the efficiency of any company. The objective world of data can sometimes be fraught with highly subjective challenges, and companies that create teams to work on data projects face a number of obstacles as they ramp up their operations.
Many of these challenges are centered on this need for collaboration between IT and business profiles. Common mistakes, such as using static data or not thoroughly planning a solution’s implementation, can trip up a young data team before they complete their first proof-of-concept. As data teams mature, the challenges do not go away but instead take different forms, like deciding whether to stick with older technologies (SAS, SPSS) or opt for newer approaches (R, Python, Spark). This whitepaper aims to address these challenges and offer solutions that are applicable to all teams working on data projects whether they are just starting out or are already established.