While working with #data you will notice something that happens a lot. You get thru a huge presentation then at the end someone asks for the data so they can make their own #analysis. This is good and bad.
Good because if your analysis wasn't insightful to begin with, no one would want your data. Good because your #presentation was compelling enough to inspire someone to go further. Good because you will most likely be asked additional questions and be sought out for additional #insight in the future (or one would hope).
Bad because when they do their own analysis, data may be misinterpreted and the misinterpretation may be attributed back to you. Bad because whoever sees the new analysis will most likely not know the effort you put in to refine that data or perhaps not even hear your name (or one would hope).
Whatever the situation, the good outweighs the bad. Everyone being or thinking like a data person is beneficial to the #organization (Data Culture!). Having discussions based on real data as evidence brings conversations into actual real context that are more applicable to the market. There will always be the gal/guy who can take raw data, import it, analyze it then #visualize it but he/she is not diminished by the person who takes that refined data from the first person and creates something new. It should be known where the data has come from and who did the analysis so that the reader can differentiate and weigh their decisions on an insight such as Jon Snow #visualization versus something else.
Lery works with technology, finance, marketing, analytics, business intelligence & data science teams to further their efforts in data driven innovation; DataESV (engineering, science & visualization).