A business use case is generally defined as a sequence of business actions that lead to a predetermined, value-added outcome. Data Flair shares the Top 6 Data Science Use Cases slated to bring in another industrial revolution. From banking to transportation in the physical business world, and from social media to e-commerce on the digital business space — data-driven decisions are reigning supreme across business functions.
A business use case can often take multiple roles. A machine-learning model or smart algorithm may be considered a core “use case” in an application development company, but this same machine-learning model will become a supporting use case, when used in a fintech or trading business.
According to What Is Data Science? 5 Applications in Business:
“Data science can be used to gain knowledge about behaviors and processes … [to] process large amounts of information quickly and efficiently … [and to] guide data-driven decision-making.”
Determining an Appropriate Use Case
Most data science use cases have two significant components: Data pile and actionable business decisions. For a specific use case to be successful, these requirements have to be fulfilled:
- The data that is used for a use case must be of superior quality coming from clean sources.
- The tools used for data analysis must be right for the given type, size, and nature of data.
Industry-Specific Data Science Use Cases
According to a recent study, the data science market will reach $115 billion in 2023 with a CAGR of 29%. Here are some important data science use cases within business sectors:
In sales and marketing sector, data science is mostly used for forecasting markets, identifying new customer base, optimizing pricing structures, and analyzing customer portfolios.
Data science use cases for manufacturing: The use cases used in this critical business function are prototyping, demos, virtual product analysis, and market trend analysis.
Data science use cases for finance and banking: The common use cases used in banks or finance companies include product and services design, customer portfolio management, risk analysis, and custom financial solutions based on past investment trends. The banks are intensely using data science platforms to enhance security.
Data science use cases for film-making: Some of the ways that data technologies have helped the movie-making industry are failure prevention, revenue generation, and real-time streaming in websites such as HBO or Netflix.
Data science use cases for e-commerce: The most popular use case found in e-commerce is audience targeting — locating target markets in huge piles of customer data.
AI and Data Science Together: Combined Use Cases
AI and data science are currently being used in conjunction with each other in the following sectors:
- In Trading: AI-powered data analytics is heavily used for automated trading decisions at high speeds, transactional data analysis, predictive analytics, and financial audits.
- In Spam Mail Filtering: The growing need for anti-spam filters in email has propelled the development of advanced ML models to filter out spam mails.
- In Autonomous Vehicles: In this sector, data science and AI are used to provide cyber-security systems for safety and data-privacy, integrated vision-sensing systems, and driving assistance for improved driving experience.
- In Talent Management: Data processing of HR data helps identify better candidates for specific job roles. Data science is also used for conducting interviews through bots to evaluate best matches for a position. Moreover, employee performance data (KPIs) can provide accurate insights for gauging employee satisfaction and attrition rates. Advanced Data Management systems are used for productivity monitoring and performance metrics analysis.
- In Healthcare: In this sector, the critical use cases are patient data analysis for enhanced healthcare decisions, assisted diagnostics with high accuracy, and symptom detection. In medical insurance, data science solutions are used to design suitable treatment plans with custom plans for the best treatment outcomes. The most valuable contribution the data science use cases have made in the world of medicine is in drug discovery — combining medical intelligence, historic patient data, and FDA data for faster drug development and increased approval rates. Prescriptive analytics use cases have helped reduce missed urgent care visits and facilitate early diagnosis. In healthcare marketing, data-enriched market intelligence has improved market research, brand management, target market mapping, and segmentation.
- In Customer Service: Natural language processing (NLP) with ML models track, target, and approach customers through agents or automated responses. Social data is increasingly empowering the customer-service staff to offer personalized (recommendations, offers, freebies) service experience to happy customers. Sophisticated call routing systems enable matching of right agents with customers for the most optimized services. Chatbots and biometric voice authentication available 24/7 are just some of the unique benefits of such hi-tech customer call-management platforms. ING Bank gained a 15% increase in sales performance and a 3% reduction in silence rates after implementing AI into their call systems.
- In Sales and Marketing: AI is used for making data-driven forecasts and predictions in sales. Advanced scoring techniques help prioritize sales activities. Customer transactional data is heavily used in AI-powered data analytics systems to increase sales and provide superior marketing services. Chatbot analytics and sales rep response data are collectively used to enhance sales performance. Sales data analytics is used to match personalized sales content with high-priority leads. Finally, AI-powered marketing analytics systems track and analyze media events to trace customer behavioral patterns and the key drivers of traffic, customer engagement, and revenue.
The Twitter Case Study: Most Talked about Data Science Use Case
The hottest AI-driven use case that everyone is talking about now is the “smart algorithm” that can identify those Twitter users who are likely to spread disinformation before they actually do it. A team of researchers from the University of Sheffield’s Computer Science Department “have developed the method for predicting whether a social media user is likely to share content from unreliable news sources.”
These researchers reviewed and “analyzed more than 1 million tweets from approximately 6,200 Twitter users” with the help of NLP technology. These massive amounts of data trained an ML algorithm to predict with almost 80% accuracy the spread of disinformation on social media.
A Ph.D. candidate at the University of Sheffield commented:
“Studying and analyzing the behavior of users sharing content from unreliable news sources can help social media platforms to prevent the spread of fake news at the user level, complementing existing fact-checking methods that work on the post or the news source level.”
From Data Science to Data Analytics
With many ambitious data science projects failing during execution, data analytics has shifted to the vertical markets over the years. The “specialized” needs of data analysis and analytics platforms have pushed vendors to design analytics solution platforms with the “domain expert” in mind. Here are some major transitions sweeping the current data analytics market worldwide:
Trend 1: “Verticalization” is the fail-safe approach that the data analytics market has chosen to reduce data-analytics project failures.
Trend 2: For cloud-based data analytics, the lakehouse data-storage solution now can house both structured and semi-structured data with access to all data, and enable advanced analytics with sophisticated models and BI capabilities.
Trend 3: Gartner has predicted that by 2023, graph technologies will dominate the decision-making process in 30% of global companies.
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