n this episode of The Data Standard, Benjamin Rogojan talks about the importance of Consultant data engineering and the steps engineers have to take to cope with huge amounts of data. He talks about the problems engineers encounter when trying to analyze different types of data. His passion for finding solutions led him to become a consultant and teach other experts on how to approach specific data analytics jobs. The process usually requires extracting data from multiple applications and pulling them into a single excel file. Since the data is gathered from multiple sources, there is a lot of room for error. Then he goes on to talk about the need to centralize data inquiries. The idea is to mix all the data into one platform and work with it from there. The use of data warehouses that’s so popular among companies these days is inefficient as analytics takes too long. Many businesses make new changes before they get results from previous implementations. Benjamin explains how speeding up data analytics through a central pile provides much more accurate results much faster. Many companies use AI to extract data, but Benjamin explains that they are doing it wrong. They use expensive features before they set clear goals, effectively losing money in the process.