Top Data Science Applications And Business Use Cases



The advancement of data science and advanced forms of analytics has resulted in many applications that provide improved insights and business value in the enterprise. Data science practices, methodologies, tools, and technologies, in particular, provide organizations with the capabilities they require to extract valuable information from ever-increasing amounts of highly variable data.

Big data tooling and artificial intelligence provide the power required to manage and analyze large amounts of data for applications ranging from predictive modeling to pattern recognition, anomaly detection, personalization, conversational AI, and autonomous systems. Indeed, data science and the data scientists who primarily perform it have been elevated from a wonky, academic side of IT to a core part of business operations.

While many different types of organizations are implementing data science-driven analytics applications, those applications focus on areas that have proven their worth over the last decade. Businesses can gain competitive advantages over competitors, better service to customers, citizens, users, and patients, and the ability to respond more effectively to a rapidly changing business environment that requires continuous adaptation by delving deeper into them.

Let us take a closer look at 8 common data science applications.

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1. Anomaly detection

Using statistical analysis to detect anomalies in large data sets is a powerful application of data science. When dealing with small amounts of data, fitting data into clusters or groups and then identifying outliers may be a relatively simple exercise; however, dealing with petabytes or exabytes of data becomes significantly more difficult.

Financial services firms, for example, are increasingly challenged to detect fraudulent spending behavior in transaction data that is growing in volume and variety. American Express was a forerunner in applying data science techniques and methods to big data in real-time for fraud detection and other purposes, allowing the company to respond quickly to events and changes. Anomaly detection can also be used to prevent cyber attacks, monitor IT systems’ performance, and eliminate outlier values in data sets to improve analytics accuracy.

2. Pattern recognition

Identifying patterns in data sets is a fundamental data science project as well. Pattern recognition, for example, assists retailers and e-commerce companies identify trends in customer purchasing behavior. Making product offerings relevant and ensuring supply chain reliability is critical for organizations that want to keep their customers happy – and keep them from purchasing from competitors.

Data science approaches have long been used by companies such as Amazon and Walmart to discover purchasing patterns. In one early example, Walmart noticed that many customers purchasing items anticipating a hurricane or tropical storm also purchased strawberry Pop-Tarts. Such unexpected correlations can help drive more effective purchasing, inventory management, and marketing strategies.

Pattern recognition has a wide range of other data science applications. For example, it can help with stock trading, risk management, medical diagnosis, seismic analysis, natural language processing (NLP), speech recognition, and computer vision.

Read more: What is Advanced Analytics?

3. Predictive modeling

Data science aims to improve predictive modeling accuracy and detect patterns and outliers. While predictive analytics has been around for decades, data science is the application of machine learning and other algorithmic approaches to large data sets to improve decision-making capabilities by developing models that better predict customer behavior, financial risks, market trends, and other factors.

Financial services, retail, manufacturing, healthcare, travel, and government are among the industries that use predictive analytics applications. Manufacturers, for example, use predictive maintenance systems to help reduce equipment breakdowns and increase production uptime. Predictive maintenance is also used by aircraft manufacturers Boeing and Airbus to improve fleet availability. Similarly, Chevron, BP, and other energy companies use predictive modeling to enhance equipment reliability when maintenance is difficult, time-consuming, and expensive.

Furthermore, organizations leverage data science’s predictive power to improve business forecasting. For example, in the face of the COVID-19 pandemic’s sudden changes in consumer and business spending, manufacturers’ and retailers’ formulaic approaches to purchasing failed. However, these brittle systems in forward-thinking businesses have been replaced with data-driven forecasting applications that can better respond to changing customer behavior.

4. Recommendation engines and personalization systems

User and customer satisfaction are typically highest when products and services are tailored to people’s needs or interests – especially if they can get the right product at the right time, through the right channel, with the right offer communicated using the right message, and with the right level of service and attention. And keeping customers happy and engaged means they are more likely to return.

However, tailoring products and services to the specific needs of individuals has traditionally been extremely difficult; doing so was both time-consuming and costly. As a result, most systems that personalize offerings or recommend items must classify people into buckets based on their characteristics. While this approach is preferable to no customization, it is far from ideal.

Fortunately, organizations can now create detailed profiles of individual customers using data science, machine learning, and big data. Their systems can learn people’s preferences over time and match them with others with similar preferences – a technique known as hyper-personalization.

Companies like Home Depot, Lowe’s, and Netflix use data-driven hyper-personalization techniques to better focus their offerings to customers via recommendation engines and personalized marketing. Financial services companies are also making hyper-personalized offers to customers, healthcare organizations are using the approach to provide treatments and care to patients, and educational institutions are providing students with highly tailored, adaptive learning.

5. Categorization and Classification

Data science tools have demonstrated real-world capabilities for sorting through large amounts of data and categorizing or classifying it based on learned characteristics. This is especially useful when dealing with unstructured data. Unstructured data is much more challenging to process and analyze than structured data, which can be easily searched and queried using a schema. Unstructured data includes emails, documents, images, videos, audio files, and various types of text and binary information. Mining that data for valuable insights was difficult until recently.

Deep learning, which uses artificial neural networks to analyze large data sets, has improved organizations’ ability to perform unstructured data analysis, from image, object, and audio recognition tasks to data classification based on the document type. Data science teams, for example, can train deep learning systems to recognize contracts and invoices among stacks of documents and perform various kinds of information identification.

Government agencies are also experimenting with data-driven classification and categorization applications. For example, NASA uses image recognition to help uncover deeper insights about objects in space. The US Bureau of Labor Statistics is automating workplace injury classification based on the analysis of incident reports.

6. Sentiment and behavioral analysis

Data scientists are digging through reams of data to understand the sentiments of customers or users and their behavior, leveraging the data analysis capabilities of machine learning and deep learning systems.

Through sentiment analysis and behavioral analysis applications, data science enables organizations to identify buying and usage patterns, what people think about products and services, and how satisfied they are with their experience. These applications can also categorize and track customer sentiment and behavior over time.

Travel and hospitality companies have adopted this high-powered sentiment analysis approach to quickly identify customers with high positive or negative experiences. Law enforcement also uses sentiment and behavior analysis to detect incidents, situations, and trends as they emerge and evolve, such as by analyzing social media posts.

7. Communication systems

One of the first applications of machine learning was the creation of a chatbot capable of having somewhat lifelike conversations without the need for human intervention. Alan Turing’s Turing Test, developed in 1950, employs a conversational format to determine whether a system can mimic human intelligence. As a result, it’s no surprise that businesses are turning to chatbots and other conversational systems to supplement existing workflows and take over some tasks previously handled by humans.

Data science has been highly beneficial in transforming conversational systems into helpful business tools. Data scientists use machine learning algorithms to train these systems on large amounts of text to derive everyday patterns from the data. Chatbots, intelligent agents, and voice assistants are now appearing everywhere, from phones and websites to cars, to engage in both text- and voice-based interactions with people – for example, to find information, assist with transaction processing, and provide customer service and support.

8. Autonomous systems

Speaking of cars, the self-driving car has long been a dream of AI enthusiasts. Wouldn’t it be nice to get in a car or truck and let it drive while you do other things without worrying about what’s going on on the road? Data science plays a vital role in the ongoing development of self-driving cars, AI-powered robots, and other intelligent machines.

Making autonomous systems a reality is fraught with difficulties. For example, image recognition tools in a car must be trained to recognize all relevant elements: roads, other vehicles, traffic control devices, pedestrians, and anything else that can affect a successful driving experience. Furthermore, self-driving systems must be capable of making split-second decisions and accurately forecasting what will occur based on real-time data analysis. Data scientists are developing machine learning models that can work together to make fully autonomous vehicles a reality.

The future of data science applications

Data science’s power is already being applied to many areas where combining big data management, data wrangling, statistics, machine learning, and other disciplines can be very effective. As data science tools and techniques in the enterprise grow, so will the types of applications they enable.

Indeed, while the CIO and CTO are two essential roles in organizations today, the emergence and growing prominence of the chief data officer – often in charge of data science initiatives and other responsibilities – demonstrates how much business value having a firm grasp on data provides. In many ways, the successful application of data science to discover critical business insights and knowledge may be more important than the operational systems that generate the data. After all, data is what truly powers the modern enterprise.

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