Industry-Specific Applications of Data Science

In modern industries, data has become the lifeblood that fuels progress and innovation.¹ By analyzing the past and the present, various data science methods can help us predict and optimize for the future.

Although it might seem very tech-focused, data science applications aren't just beneficial in technology sectors. You can use data to your advantage in virtually every industry, including finance, marketing, healthcare, agriculture, retail, and real estate. Successful data analysis can help unearth critical insights about your business, such as how to streamline business operations, predict customer behavior, and reduce waste. This puts data scientists, and others who can effectively wield data, in high demand.

In marketing, for example, data’s significance is illustrated by success stories of popular brands like Coca-Cola. The company spends $4 billion annually on advertising and uses data science and AI to ensure the money is well spent.² To achieve this goal, the soft drink giant analyzes market trends and customer behavior across the more than 200 countries it serves. Next, it tracks each mention and image posted of one of its products on social channels and then targets ads to specific consumers based on the content they post.²

This is just one example of data science's immense impact on how organizations structure and run their business. For more information, this blog post explores the diverse applications of data science across various sectors, from healthcare and marketing to manufacturing and agriculture.

Data Science in Healthcare

Data science, along with AI tools, plays a crucial role in healthcare by using pattern recognition, predictive modeling, and large volumes of patient data to improve detection, diagnoses, and treatment.³ Data science algorithms can track common health characteristics in patients with breast cancer or Zika to determine what might be a signal of something worse. On the other hand, an estimated 12 million Americans are currently misdiagnosed, which can annually result in up to 80,000 deaths a year.³ Here, data science approaches can draw from health-related research to confirm or refute diagnoses, as well as help develop treatment recommendations.

Predictive Diagnostics

Predictive diagnostics not only supports the efficient allocation of resources, but it also saves lives. An example of this is Google’s LYNA, an artificial intelligence tool, has shown promise in helping to reduce cancer mortality rates. In trials, LYNA’s algorithm was able to distinguish a slide with advanced breast cancer from one without cancer 99% of the time.⁴ Plus, it could pinpoint the location of the tumor within the slide better than pathologists.⁴ However, before doctors can use this technology in hospitals, more clinical trials and testing are needed.

Personalized Medicine and Treatment Optimization

The personalized medicine approach marks a paradigm shift in the healthcare industry. While some providers may make prescriptions more generally (e.g., pain medications), data-driven approaches enable healthcare providers to understand individual variations, optimizing drug dosages and treatment strategies. Treatment plans can be based on patient data, rather than doctors' notes and the information the patient shares, which results in more effective care and improved outcomes for individuals.

When treatments for illnesses prove to be more elusive, researchers leverage big data, with support from new AI platforms. One of these tools, Virtualitics, transforms data relationships into 3D visuals.⁵ Such tools have helped 150 researchers extract insights from health data, which can inform the development of better treatments for long COVID.⁵

Drug Discovery and Development

Bringing a new drug to market carries a price tag of $2.6 billion and can take up to 12 years.³ In the past, drug discovery involved identifying active ingredients or happened by luck. But, thanks to a new simulation method using data science, organizations could expedite the drug development process and drastically shorten the release to two years.³ Using a molecular dynamics simulation method, scientists can replicate how a drug will react with proteins and different cell types in the human body.³ Data science also aids in identifying potential drug candidates, streamlining the path to novel treatments.

Data Science in Finance

The financial industry is especially primed for data science applications because of the vast amounts of data points and financial information that need to be collected, analyzed, stored, and protected. In this sector, data science can help with anything from financial market analysis to suggesting ways that customers can improve their budget.

Fraud Detection and Prevention

Because of its ability to constantly track activities to determine what is "typical," a data science model can serve as a guardian against fraudulent activities, such as unauthorized push payments. This type of fraud is projected to cost $4.6 billion in the U.S. and U.K. combined by 2026.^6,7 By identifying anomalies in financial transactions, fraud detection systems can mitigate risks and safeguard customers’ finances.

Ian Selley, the manager of fraud analytics and data science at U.K. bank,TSB says scammers now impersonate family members, debt collectors, and even romantic partners to get victims to send them money.^6,7 Fraud detection systems of the past might flag the payee if, for example, an elderly customer suddenly wiring a large sum of money overseas. With the help of an AI-powered tool, Mastercard says financial institutions can focus on a new and critical data point—the recipient’s account activity—to identify scammers in real time and stop funds from leaving the victims’ bank accounts in the first place.^6,7

Credit Scoring and Risk Assessment

Additionally, data has become a valuable tool for evaluating borrower creditworthiness. If a customer is seeking a home or car loan, financial institutions can create more accurate and fairer credit scoring and risk assessment. That enables them to optimize loan approvals and enhance risk management, ensuring a balance between serving clients and maintaining a healthy financial ecosystem.

Data Science in Marketing

Personalization is the cornerstone of modern marketing, and data science is the driving force behind delivering tailor-made content to consumers. Although tech giants like Amazon, Spotify, and YouTube use tools to deliver targeted recommendations to their users, some rank Netflix as the king of personalization.⁸

As of 2016, Netflix has saved over $1 billion annually in customer acquisition thanks to its recommendation engine.⁸ This engine filters a vast library of over 3,000 titles through 1,300 recommendation clusters that are based on user preferences.⁸ Remarkably accurate, the NRE influences a staggering 80% of viewer activity.⁸

Regardless of size, organizations are turning to data-based marketing methods that deliver results, such as:

• Customer Segmentation: Analyzing consumer behavior and understanding the preferences and habits of different customer groups to deliver more relevant, targeted messaging.
• Social Media Analytics: Extracting insights from social media data helps marketers learn about brand perception and consumer sentiments and adapt audience engagement strategies in real time based on these insights

Data Science in Manufacturing

At the intersection of data science and manufacturing, advancements are reshaping the industry, from lowering maintenance costs to ensuring the highest quality products reach the market. Data science is having a measurable and positive impact in these key areas:⁹

• Predictive Maintenance: By monitoring equipment performance, companies can reduce downtime and machine maintenance costs, ensuring optimal efficiency and productivity
• Supply Chain Optimization: Data-driven insights enable manufacturers to streamline operations and respond swiftly to changes in demand, creating a more agile and responsive supply chain
• Quality Control: Data science applications in quality control enhance manufacturing processes, reducing waste and ensuring that products meet the highest standards

Emerging Trends and Cross-Industry Applications

As we learn more about the vast amounts of ways in which data science is used, data scientists are finding applications in previously untapped markets. In agriculture and the automotive industry, for example, data science applications contribute to increased efficiency, resource optimization, and enhanced decision-making, fueling innovation and sustainability.

Data-Driven Insights in Agriculture

From production to distribution, data insights aid in optimizing the agricultural supply chain—including efficient inventory management and route optimization for transportation. For farmers, predictive analytics can help them prepare for next season's crop by knowing when the soil will be the most fertile.

Vineyard owners like Emma Thompson are using sensors on their crops, maximizing profits and minimizing environmental damage.¹⁰ First, the sensors gather real-time data on factors like humidity and pest activity.¹⁰ Then, predictive models help identify the best times for targeted pest control. She credits the data-driven approach with reducing pesticide use by 30% and lowering production costs.¹⁰

Artificial Intelligence in Autonomous Vehicles

Another area that depends heavily on data science is integrating artificial intelligence (AI) in autonomous vehicles. In self-driving cars like those from Tesla, machine learning algorithms analyze vast datasets to improve maintenance without any human intervention.¹¹ Another development is the installation of in-cabin cameras linked to AI emotion detectors in newer vehicles, which can record a driver’s facial expressions and extract insights from the data or even sell it for profit.¹²

While such systems often collect sensitive information from automobile owners, innovators say it’s up to car manufacturers to inform and gain consent from their customers.¹² Although AI has resulted in improvements like safer road travel and easier navigation, cases like these reveal the darker side of innovation and the ethical dilemmas that go along with it.

Challenges and Considerations

In this era where personal information remains a valuable commodity, striking the right balance between innovation, profits, and individual privacy protections is a major challenge. Another challenge is ensuring the quality and accuracy of data. Both present hurdles as the amount of data that companies and individuals produce globally is expected to reach an estimated 463 exabytes per day by 2025.¹³ The more data produced, the greater the need is to responsibly collect, analyze, and use this data.

Data scientists have a pivotal role to play in shaping data ethics policies because they have expertise in the most prominent data disciplines: artificial intelligence, machine learning, probability, and statistics. However, experts say they should not be the only gatekeepers of data integrity within an organization. Instead, leaders should build diverse data teams to raise and contribute solutions to various ethical issues beyond privacy and security.

Prepare to Lead in the World of Data Science

More and more businesses recognize the value of strong data science skills and knowledge to their bottom line. Whether you’re interested in becoming a data science professional or have your sights set on a management role, New York Institute of Technology’s Online Data Science, M.S. allows you to learn the latest strategies for effectively analyzing data, communicating with stakeholders, and positively impacting business decisions.

Gain market-ready skills to prepare you for diverse roles as you take challenging courses. You’ll also tap into an expansive network in New York and receive mentorship from renowned professors. Don’t wait to advance your data science career—schedule a call with an admissions outreach advisor today.