The Overlap of Data Science and AI: Unlocking the Future of Business Intelligence

January 2, 2024

In its report, “The data-driven enterprise of 2025,” international consulting firm McKinsey & Company predicts that by 2025, nearly all workers will “naturally and regularly leverage data to support their work.” It also projects that organizations will automate “basic day-to-day activities and regularly occurring decisions” to take advantage of data-driven insights and processes.

In adapting to these process changes, businesses must collect, store, process, and interpret vast amounts of data generated daily, which requires specialized professionals to do the work: data scientists. These experts in mathematics, statistics, computer science, and software engineering use advanced tools, such as algorithms and predictive analytics, to organize data and develop methods to discern patterns and extract information from enormous data sets. 

Data scientists have also started using artificial intelligence (AI) and machine learning alongside their subject matter expertise to reveal additional insights. AI simulates human intelligence processes using machines, especially computer systems. Machine learning, a subfield of AI, enables software applications to become more accurate at predicting outcomes without additional programming. AI and machine learning can process large, complex data sets much faster than humans, making them valuable data science tools. 

AI and machine learning have extraordinary potential in many fields, including business intelligence (BI). BI is a subfield of data science that focuses on analyzing past data to understand business trends and derive insights that can drive predictive modeling. The online Master of Science in Data Science (MSDS) offered by Tufts University prepares students for careers in business intelligence, where data science and artificial intelligence overlap. 

The Purpose of Business Intelligence 

Business intelligence provides organizational leaders with the information they need to make sound decisions. It doesn’t offer definitive answers or guaranteed outcomes around business options. However, through data collection and descriptive analytics, BI professionals enable decision-makers to examine all available data, understand the complete picture, and arrive at an informed choice. 

Many leading companies utilize BI to improve their decision-making processes: 

  • Starbucks uses data collected through its loyalty card and mobile application to drive BI, analyzing customers’ purchase history to personalize promotional offers. 
  • American Express utilizes BI technology to inform new payment service products and marketing campaigns. For example, through BI, American Express has gained the ability to identify up to 24% of all Australian users who will close their accounts within four months. The company can then take steps to work to retain those customers. 
  • Chipotle Mexican Grill implemented BI to track operational effectiveness across its more than 2,400 restaurants nationwide. The company standardized reporting across the entire organization to ensure all branches used the same data ecosystem, enabling the company to set uniform KPIs across the business, benchmark the performance of individual restaurants, and share improvement tips and success stories across the organization. The result is overall increased efficiency. 

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Bridging the Gap: Business Intelligence, AI, and Machine Learning

Companies increasingly use AI and machine learning to augment their data analytics and BI efforts. Modern BI helps organizations make data and insights accessible to all employees, resulting in a better-informed workforce capable of improved data-driven decision-making. 

A Salesforce survey found that 80% of senior IT leaders think generative AI will help their organization use data better. And they’re acting on that belief: according to McKinsey, 33% of organizations responding to their survey hired AI data scientists in 2022.

AI and machine learning applications can enhance BI results across industries. Examples include:

  • Increasing supply chain visibility: AI, coupled with Internet-of-Things (IoT) sensor inputs, can provide real-time information to BI applications, affording better visibility into the entire supply chain and enabling companies to make more informed decisions. 
  • Improving maintenance recommendations: AI can assist BI in a manufacturing or transportation environment by making maintenance recommendations and failure predictions based on past and real-time data, resulting in better preventative maintenance and less downtime. 
  • Helping companies better understand their customers: AI and machine learning can help businesses collect and segment customer data more effectively. For example, Coca-Cola uses AI-powered image-recognition technology to find photos of its drinks on social media. This data, paired with BI, gives the company insights into its customers and allows Coca-Cola to develop more targeted ads. 
  • Detecting problems faster: Many software systems still rely on humans to detect issues and create alerts. However, machine learning technology can continuously analyze numerous telemetry streams, enabling companies to flag problems faster to maximize business intelligence and act when needed. 

Despite the myriad benefits, AI and machine learning in BI pose significant risks. Data security and privacy top the list of business-related challenges. Organizations must operate in line with regulations like the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Many companies also must migrate and integrate data from legacy sources, which can be a daunting challenge.

Data Science and AI at Tufts

If you’re interested in pursuing a career in AI data science or business intelligence, the Tufts online Master of Science in Data Science program can help you achieve your goals. It’s designed to cultivate your data collection and interpretation skills so you’ll be prepared to deliver actionable insights to your organization’s leadership. 

The curriculum, taught by faculty with extensive data science and AI expertise, covers crucial data science skills, including database systems management, probabilistic systems analysis, and statistics. Future-focused courses covering AI and machine learning prepare students for successful careers in modern data science. In addition to hard skills, the curriculum also teaches soft skills like problem-solving and communication to prepare students to lead at the intersection of data and decision-making. 

Tufts online MSDS students may also complete a substantial data science capstone project in which they solve data science and business intelligence problems using AI in the real world. During the capstone project, students will propose a substantial data science project, complete it, and demonstrate their conclusions in a professional presentation to faculty and peers.

Stay Ahead of the Curve with the Tufts Online MSDS Degree

Businesses are increasingly focused on data-driven decisions. According to a survey by EY, 53% of senior executives see data and analytics as a top investment priority in the next two years; that’s a 50% increase since 2020. Following this trend, the demand for data science professionals is soaring. The U.S. Bureau of Labor Statistics (BLS) expects employment of data scientists to grow 35% over the next decade, with nearly 18,000 new openings in the field each year. Possible jobs for Tufts MSDS graduates include data science manager, database architect, machine learning engineer, and analytics manager.

The Tufts online Master of Science in Data Science can put you at the forefront of this expanding field. Whether you want to pursue a research career, switch jobs, advance within your current organization, or increase your earning potential, this program can prepare you for your future career. 

If you want to learn more, set up a call with an enrollment advisor or start the online application process today.

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