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 in proving optimal solutions to individual problems.

Data science solves real business problems by using hybrid models of math and computer science to get actionable insights. It takes the risk of going into the territory of the uncharted ‘unstructured’ data and getting meaningful insights that help businesses make better decisions.

Let’s talk about how problems are solved by data science. We will take examples of a few companies and some concepts that are used in data science in solving real business problems.

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Problems solved by data science

1. 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, 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 the 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.

2. 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.

3. 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.

4. 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 and this has been one of the main reasons why 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 problems are solved by data science. 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.

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