Data science techniques, applications, and tools allow organizations to extract valuable insights from data.
The evolution of data science and advanced forms of analytics has created significant change for companies. The conditions were created for the emergence of various applications that provide deeper insights and business value.
While data science was once considered the risky and even more nerdy side of IT, it has now become the cornerstone of the working principles of any organization.
How are data science techniques used today?
Modern data science techniques offer the capabilities needed to crunch and analyze large data pools for a wide variety of applications, including predictive modeling, pattern recognition, anomaly detection, personalization, speech-based AI, and autonomous systems.
Many organizations today rely on data science-based analytics applications, mostly focused on areas that have proven their worth over the past decade. By using the power of data, organizations can gain competitive advantages against their competitors, serve their customers better, and gain the ability to react more effectively to rapidly changing business environments that require constant adaptation.
Let’s take a closer look at the most popular data science techniques that have already become the cornerstone of the business world:
Anomaly detection, one of the most popular data science techniques, uses statistical analysis to detect anomalies in large data sets. While it is a simple practice to fit data into clusters or groups, then spot outliers when dealing with small amounts of data, this task becomes a real challenge when it is petabytes or exabytes.
Financial services providers, for example, are finding it increasingly difficult to detect fraudulent spending behavior in transaction data, which continues to grow tremendously in volume and diversity. Anomaly detection applications are also used to eliminate outliers in datasets to increase analytical accuracy in tasks such as preventing cyber-attacks and monitoring the performance of IT systems.
Recognizing repetitive patterns in datasets is a fundamental data science task. For example, pattern recognition helps retailers and e-commerce companies detect trends in customer purchasing behaviors. Organizations need to make their offerings more relevant and ensure the credibility of their supply chain to keep their customers happy and prevent customer churn.
Giant retailers serving tens of millions of customers today have long used data science techniques to discover purchasing patterns. In one of these studies, a retailer noticed that many customers shopping in anticipation of a storm or tropical storm bought a particular brand of strawberry biscuits and took advantage of this invaluable information to change its sales strategy. This resulted in increased sales. Such unexpected correlations are made possible by recognizing data patterns. The insights created from data help create more effective sales, inventory management, and marketing strategies.
Pattern recognition also helps improve technologies such as stock trading, risk management, medical diagnosis, seismic analysis, natural language processing (NLP), speech recognition, and computer vision.
Data science makes predictive modeling more accurate by detecting patterns and outliers. While predictive analytics has been around for decades, data science techniques today create models that better predict customer behavior, financial risks, and market trends. It also applies machine learning and other algorithms to large datasets to improve decision-making capabilities.
Predictive analytics applications are used in various industries, including financial services, retail, manufacturing, healthcare, travel, utilities, and many others. For example, manufacturers use predictive maintenance systems to help reduce equipment failures and improve production uptime.
Aircraft manufacturers rely on predictive maintenance to improve their fleet availability. Similarly, the energy industry is using predictive modeling to improve equipment reliability in environments where maintenance is costly and difficult.
Organizations are also leveraging the predictive ability of data science to improve business forecasting. For example, formulaic approaches to purchasing by manufacturers and retailers have failed in the face of the sudden shifts in consumer and business spending caused by the COVID-19 pandemic. Innovative companies have overhauled these fragile systems with data-driven forecasting applications that can better respond to dynamic customer behaviors.
Recommendation engines and personalization systems
Customers are very satisfied when products and services are tailored to their needs or interests and when they can get the right product at the right time, through the right channel, with the right offer. Keeping customers happy and loyal gives them enough reasons to choose you again. However, tailoring products and services to the specific needs of individuals has traditionally been very difficult. It used to be a very time-consuming and costly task. This is why most systems that customize offers or recommend products need to group customers into clusters that generalize their features. While this approach is better than no customization, it is still far from optimal.
Fortunately, combining data science, machine learning, and big data allows organizations to build a detailed profile of individual customers and users. Systems can learn people’s preferences and match them with others with similar preferences. This is the working principle of the hyper-personalization approach.
Popular streaming services, as well as the largest retailers today, are using data science-driven hyper-personalization techniques to better focus their offerings on customers through recommendation engines and personalized marketing. Financial services companies also offer hyper-personalized offers to clients, while healthcare organizations use this approach to provide treatment and care to patients.
Investing heavily in its recommendation engine and personalization systems, Netflix uses machine learning algorithms to predict viewer preferences and deliver a better experience. The streaming service uses a recommendation engine that influences critical data touchpoints such as browsing data, search history, user ratings on content, and device information to provide customers with relevant recommendations through a hyper-personalized homepage that differs for each user.
Emotion, sentiment, and behavior analysis
Data scientists probe data stacks to understand the emotions and behaviors of customers or users using the data analysis capabilities of machine learning and deep learning systems.
Sentiment analysis and behavioral analysis applications allow organizations to more effectively identify customers’ buying and usage patterns, understand what people think about products and services, and how satisfied they are with their experience. These approaches can also categorize customer sentiment and behavior and reveal how they change over time.
Travel and hospitality organizations are developing strategies for sentiment analysis to identify customers with very positive or negative experiences so they can respond quickly. Law enforcement also uses emotion and behavior analysis to detect events, situations, and trends as they arise and evolve.
Classification and categorization
Data science techniques effectively sort large volumes of data and classify them according to learned features. These capabilities are especially useful for unstructured data. While structured data can be easily searched and queried through a schema, unstructured data is very difficult to process and analyze. Emails, documents, images, videos, audio files, texts, and binary data are unstructured data formats. Until recently, searching this data for valuable insights was a huge challenge.
The advent of deep learning, which uses neural networks to analyze large data sets, has made it easier for organizations to perform unstructured data analysis, from image, object, and voice recognition tasks to classifying data by document type. For example, data science teams can train deep learning systems to recognize contracts and invoices among document stacks and identify various types of information.
Government agencies are also interested in classification and categorization practices powered by data science. A good example is NASA, which uses image recognition to reveal deeper insights into objects in space.
Chatbots and voice assistants
One of the earliest applications of machine learning was the development of chatbots that could communicate like real humans without any intervention. Designed by Alan Turing in 1950, the Turing Test used the speech format to determine whether a system could mimic human intelligence. So it’s hardly surprising that modern organizations are looking to improve their existing workflows by using chatbots and other conversational systems to delegate some tasks previously handled by humans.
Data science techniques have been extremely useful in making speech systems useful for businesses. These systems use machine learning algorithms to learn and extract speech patterns from data. Powered by advanced natural language processing technology, chatbots, smart agents, and voice assistants now serve people everywhere, from phones to websites and even cars. For example, it provides customer service and support to find information, assist with transactions, and engage in both text-based and voice-based interactions with people.
Speaking of cars, one of the dreams that the artificial intelligence field has been trying to achieve for a long time is driverless vehicles. Data science plays a huge role in the ongoing development of autonomous vehicles, as well as AI-powered robots and other intelligent machines.
There are numerous challenges in making autonomous systems a reality. In a car, for example, image recognition tools must be trained to identify all elements. The list goes on and on, in the form of roads, other cars, traffic control devices, pedestrians, and anything else that can affect a safe driving experience. Moreover, driverless systems must know how to make snap decisions and accurately predict what will happen based on real-time data analysis. Data scientists are developing supporting machine learning models to help make fully autonomous vehicles more viable.
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