What applications does data science have in business?

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Data science is defined as the process of using scientific methods and complex algorithms to gain insights from ‘big data’. Businesses attempt to structure datasets for analysis in order to solve a problem or take advantage of an opportunity. Extracting valuable information in this way helps them to make better decisions and improve the quality of core processes.

While data science is heavily reliant on advanced applications and cutting-edge tech, scientists still stress the importance of human input. “With this new world of possibility, there also comes a greater need for critical thinking,” Harvard Professor Dustin Tingley says. “Without human thought and guidance throughout the entire process, none of these seemingly fantastical machine-learning applications would be possible.”

That’s why there is always a huge demand for talented scientists capable of leveraging this tech to analyze and interpret data to help businesses in a range of industries and sectors. Completing a Masters in Applied Statistics will give you the skills to analyze datasets and extract insights and potentially secure a high-paying position afterwards.

The US Bureau of Labor Statistics (BLS) expects statistics-related job roles to grow by around 35% by 2030. Joining a top-ranked online university will enable you to get the qualifications to apply for these roles while offering you the flexibility to complete a course without having to physically attend a campus.

In today’s tech-driven business landscape, data science is critical as it can be applied in a wide variety of use cases, from conducting risk management and improving cyber security, to delivering targeted advertising campaigns and personalized customer service. Data scientists employ a range of different applications to support these use cases.

Anomaly detection – detecting fraud, cyber security

Data science can be used to analyze statistics at a large scale and detect anomalies that can then be acted on. This is a process that is very useful for spotting fraudulent behavior during the thousands of online transactions that companies have to handle each day. American Express was struggling with fraud detection due to the volume and variety of transactions, so it started applying data science techniques to flag fraudulent spending behavior.

Anomaly detection is also useful for monitoring an IT infrastructure to identify outliers that can help to prevent hacks and attacks from cyber criminals. Data shows that preventing a cyber security attack can save an average of $682,650. Seven in 10 security professionals believe that prevention is better than remediation in these instances and anomaly detection can play a crucial role.

Pattern recognition – inventory management, risk management

Pattern recognition uses machine learning algorithms to discover patterns and trends from data. This process has numerous business applications, but among the most popular is customer analytics, which helps a company to manage its inventory by gleaning insights from purchasing behavior and habits. Ecommerce companies use pattern recognition to identify products that customers regularly buy during a certain period of the year or during certain events.

For example, after using data science, Walmart found that strawberry Pop-Tarts were a popular purchase for customers in preparation for natural disasters such as storms and hurricanes. This insight would enable it to stock more of a particular product when a storm has been forecast. Pattern recognition can also be used for identifying and managing risks, diagnosing medical conditions, and investment and stock trading decisions.

Predictive modeling – infrastructure maintenance

While pattern recognition looks to act on existing behaviors, predictive modeling looks to the future and attempts to model upcoming events. However, it still utilises a similar method of spotting patterns and outliers to achieve this aim. Businesses use machine learning for predictive modeling to better prepare themselves for future evolutions in business, markets and customer behaviors.

For example, a company might be able to identify an upcoming financial risk that they can then act on. Manufacturers also deploy predictive modeling to monitor and gauge the lifespan of physical equipment. This can be very useful for equipment that is critical for production as repairs can be made to increase uptime. Both Airbus and Boeing have used this method to maximize the performance of their respective fleets.

Recommendation engines – marketing offers, hyper-personalization

A recent study by IBM found that “consumers want it all” and are now demanding hybrid shopping and high-quality user experiences. With expectations higher than ever, businesses are turning to recommendation engines built using data science to deliver the personalized experiences that customers now expect, both online and in physical stores. These engines can create a complete, detailed profile of each customer and their preferences so that brands can serve them with the right messages at the right time and keep them engaged through the sales cycle.

Streaming platforms are also getting in on the act. Netflix has leveraged hyper-personalization to tailor TV and movie recommendations to users in their feeds, which can increase the time they spend on the platform. Financial services firms and healthcare organizations use the same methods to provide better service and for the latter, more targeted treatments.

Sentiment analysis – marketing and customer service

Deep learning systems are digging deeper into data to find the reasoning behind why customers act the way that they do. This sentiment analysis is particularly useful for companies that want to know whether their products and services are actually satisfying customers. This data can then be compared and contrasted over time to see how sentiment has changed, which can again be acted on to serve a range of business use cases.

Certain industries prefer to act on sentiment analysis immediately to improve customer service rather than using it behind the scenes to inform marketing. This method is popular in travel and hospitality where responding to poor experiences is important. These companies identify disgruntled customers or happy ones so that they can follow up and provide the right support afterwards. Law enforcement firms also use behavioral analysis via social media posts to spot potential incidents and events.

The applications and use cases listed here are just the tip of the iceberg in terms of how data science can be used in business to transform a company’s prospects. Conversational and autonomous systems driven by artificial intelligence (AI) promise yet more advancements and point to an exciting future where data science can potentially improve every aspect of business.

Please see the information below to learn more on how data can help you better understand and meet the demands of your customers.

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Infographic provided by Riveron – expert accounting and finance functions

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