 John Kerry Rue's book, Bad Blood, describes the rise and spectacular fall of once-unicorn startup Theranos. Theranos' board of directors included many tried-and-true leaders. So, how did this happen? Theranos said they were making a new way of doing blood tests that used a fraction of the blood normally used. So, they were building a healthcare innovation in doing science. But even though they had a board of superstars, these people were not scientists and none had specific expertise in lab testing. Even so, the board was responsible for oversight of the company and preventing fraud. Yet, at one point, Theranos submitted data to regulators that came out of commercial machines they purchased, and they lied and said it was from their new testing equipment. You might wonder how a non-scientific board would have prevented this. Well, lab data come out of machines in a certain format, usually a format that data scientists like me do not like. So, they need to be reformatted before analysis. But if I'm the person managing the data project, not analyzing the data, a simple solution to improve communication and understanding is to make a data dictionary. Data dictionaries can be made in Excel. They include data descriptions and use plain language that even dumb managers like me can understand. I'm Monica Wahee and I'm an author on LinkedIn Learning. Want to learn how to make a data dictionary and prevent misconduct on your team? Take my course in data curation foundations. The link is in the description.