Wednesday, November 25, 2020

What is data science? Transforming data into value

What is data science? A method for turning data into value

Data science definition

Data science is a method for gleaning insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning. For most organizations, data science is employed to transform data into value in the form improved revenue, reduced costs, business agility, improved customer experience, the development of new products, and the like. Data science gives the data collected by an organization a purpose.

Data science vs. analytics

While closely related, data analytics is a component of data science, used to understand what an organization’s data looks like. Data science takes the output of analytics to solve problems.

“Data science is coming to conclusions that drive your data forward,” says Adam Hunt, CTO at RiskIQ. “If you’re not solving a problem with data, if you’re just doing an investigation, that’s just analysis. If you’re actually going to use the outcome to explain something, you’re going from analysis to science. Data science has more to do with the actual problem-solving than looking at, examining, and plotting [data].”

The difference between data analytics and data science is also one of timescale. Data analytics describes the current state of reality, whereas data science uses that data to predict and/or understand the future.

Data science vs. big data

Data science and big data are often viewed in concert, but data science can be used to extract value from data of all sizes, whether structured, unstructured, or semi-structured. Of course, big data is useful to data scientists in many cases, because the more data you have, the more parameters you can include in a given model.

That said, more isn’t always better. As Hunt says, “If you take the stock market and try to fit it to a line, it’s not going to work. But maybe, if you only look at it for a day or two, you can set it to a line.”

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