Researchers discovered a way to extract principal features from big data sets, reports Texas A & M Today.

Whenever a scientific experiment is conducted, the results are turned into numbers, often producing huge datasets. In order to reduce the size of the data, computer programmers use algorithms that can find and extract the principal features that represent the most salient statistical properties.

According to Venture Beat, Noogata, a big data analytics firm has raised $12 million in funding. 

Noogata, a startup developing products for designing, implementing, and deploying big data analytics models at scale, today announced that it raised $12 million. 

Experts are warning data scientists not to confuse quantity with quality, reports Bloomberg. 

It seems obvious that the opinions of 2.3 million people would be more representative than the opinions of a randomly selected 400. In reality, it depends entirely on how the bigger data set was put together.