Advancements in technology helped data science evolve from cleaning data sets and applying statistical methods to a field that encompasses data analysis, predictive analytics, data mining, business intelligence, machine learning, deep learning and much more.
It was previously thought that data science was just a trend and that the hype would eventually go away, but time has proven that nothing could be further from the truth.
Why is Data Science Important?
Data science is gaining speed as all businesses (and government organizations) use enormous volumes of data to improve their operations. There is virtually no industry that can’t benefit from it.
Retail and e-commerce, logistics and transportation, healthcare, finance, insurance, and real estate—all these sectors need a strong data science team to leverage their organization’s data to gain a competitive advantage.
But what makes data science the magic ingredient for success?
We answer this question with three tangible examples.
Formula 1 – The Brazilian Grand Prix 2012
Red Bull’s Sebastian Vettel suffers the worst possible start—his car spins out of control and loses its front wing on his opening lap. Vettel suddenly drops down to last place. All seems doomed for the champion. But the German somehow manages to recover and finishes sixth, which grants him just enough points to snatch the world title for the third year in a row in the most dramatic fashion.
Fierce driving or luck?
Nope. That’s data science.
By the time Vettel made it to his 10th lap pit stop, a team of brilliant data engineers had already modeled data to run simulations. This allowed them to analyze what adjustments were necessary to keep Sebastian’s Renault going for the remaining 70 laps.
What a huge victory fueled by data
Amazon
Amazon has thrived by adopting an everything-under-one-roof model. But when faced with such a broad range of products, customers can often feel overwhelmed. To help customers find the right product, Amazon uses data gathered from customers to build andfine-tune its recommendation engines.
The more Amazon knows about you, the more it can predict what you want to buy.
And once the retailer knows what you might want, it can streamline the process of persuading you to buy it—e.g., by recommending specific products so that you don’t have to search through the whole catalog.
Netflix
At the end of 2019, Disney launched their streaming service Disney+, which was built to compete with Netflix. The most significant competitive advantages Netflix had in the early days were their data and the fact that they were able to analyze more than 30 million “plays” per day and four million subscriber ratings.
This enabled them to predict with accuracy what customers wanted to see next. Moreover, Netflix famously uses data to make winning bets on TV series,such as House of Cards, The Crown, and Stranger Things.
The powerful recommender engines and data analysis allowed the company to keep its market leadership position in the years to come.
These are three notable examples of the clever usage of data science in business. But its application isn’t restricted to the F1 racetrack, e-commerce, or the production of hit TV series.
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