NASA is one of the largest
They have four main mission areas: Human Exploration and Operations, Science, Aeronautics Research, and Space Technology. Considering the size of the
Stephen Chesley, NASA's Senior Workforce Planning Specialist who spoke at the HR & Analytics Summit in Chicago in 2015, says that similar to other large
Firstly, he believes that in order to understand good data, you need to have context:
- Does this data make sense?
- Does it agree with other data?
- What is the source of it?
- Can all types of data be good data?
- Is it important for data to be replicable?
- Do you have a large sample size?
Answers to those questions can lead
During Stephen's crowdsourcing experiment, where people were asked 'why do we need good data?', the answers were mostly given in contrast to 'bad data': Bad data can lead to inconsistent reporting or analysis, unlike good data which is reliable. Bad data can take more time to make a use of and understand, whereas, with good data, the analysis is organic. And finally, bad data can cause mistrust. However, there was never a universal definition of what good or bad data is.
People may have different interpretations of data, this is because it can be of different types and use points, depending on each case. The critical factor is,
More isn't necessarily better and it can be useful to define the scope and only use data that is needed. Additionally, not all data is equally available, but that's not the reason to start the analysis based on data which may not be enough to achieve the required level of accuracy. Stephen recommends waiting until requisite data emerges (as it always does with time), so the analysis can be completed. And also, he says, not every insight adds enough value to justify the resources needed to extract data, so think and plan thoroughly your data sources.
Companies can and should innovate with data. From NASA's experience, there is no such thing as one single method or approach to