Statistics and data analysis have leveraged the power of data to explain the current situation in any business set-up and predict certain outcomes. With data science, this gets enhanced further. Data science solves real business problems by utilising data to construct algorithms and create programs that help prove optimal solutions to individual problems.
Data science solves real business problems using hybrid math and computer science models to get actionable insights. It takes the risk of going into the territory of uncharted ‘unstructured’ data and getting meaningful insights that help businesses make better decisions.
Let’s talk about how data science solves real business problems. We will take examples of a few companies and some concepts that are used in data science to solve real business problems.
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Automating the placement of a digital advertisement
Let’s start with Svorn brokers. This is a company whose vision speaks for itself. It works for both publishers and advertisers. If you are an advertiser, it will connect you to a passionate audience through their clean, certified exchange.
But what does it do?
Well, it deals between advertisers and channels like ESPN, Encyclopedia, Bustle, and StarTribune. As these deals happen numerous times in a day, Sovrn has access to a lot of data for insights. It uses this data to automate digital ad placement. Its interface is compatible with Google as well as Amazon’s server-to-server bidding platforms and can monetize by sending target campaigns to a particular set of customers.
Use of data science and advanced analytics to revamp the search function
AirBnB is the prime example of a technology company that has leveraged the power of data science to solve real business problems. It gets a million users each day who search for top-rated vacation rentals. Not just that, it has data from hosts, demand for rentals, hosts and what not! Airbnb realised the importance of this data and created a dynamic pricing system called Aerosolve.
Being an open source resource, Aerosolve’s predictive model considers a variety of attributes like an optimal price for a rental based on its location, the time of the year it is mostly booked, etc. It then uses the insights to help AirBnB hosts set their prices and gain maximum returns.
Using data science for generating data-driven crime predictions
Data science solves real business problems not just at corporates or tech companies, but there are multiple ways in which the government agencies of the US. For instance, there is a software suite widely used by the American judicial system and law enforcement called the Northpointe software suite. Designed by an Ohio-based company, Equivant, it tries to simulate whether an offender’s risk of trespassing, based on its data-driven algorithms. The algorithms assess the risk on the basis of a questionnaire that asks questions on the offender’s employment status, education, etc.
Using data science for evading tax evasion
The Internal Revenue Service in the US government has used data science to create evolved fraud-detection protocols in the digital times. Tax evasion costs the US government billions of US dollars a year, which has been one of the main reasons the IRS has stepped up its game. It has improved the efficiency by creating multidimensional taxpayers profiles by digging deep into the data the citizens provide at multiple avenues. For example the social media data, analysis of emails, recognizing the electronic payments, etc. Based on such profiles, the agency predicts individual tax returns, and those whose predicted and real returns don’t match, they get picked out for auditing.
Conclusion
In this article, we aimed to cover some of the ways in which data science solves business problems. There are a lot more areas in which this can be applied. So by no means is this list exhaustive.
But I am sure after reading this article, you would have definitely realized the massive growth of data science in US and around the world. You can read more about data science in the US in this ebook.
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