The Data Standard
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.
Meet The Host
Data scientist at The Data Standard
Catherine Tao is a tech enthusiast looking for new methods for building connections with businesses around the world. Her extensive knowledge of data science allowed her to develop new solutions and implement them into existing ecosystems. She is currently working as a Data scientist and Exclusive Podcast Producer at The Data Standard.
Meet The Guest
Data engineer and data scientist at The Seattle Data Guy
Benjamin Rogojan is a data scientist and engineer with extensive experience designing ETL pipelines, databases, and websites. He worked for multiple startups as well as solutions for existing corporations.