Imagine you’re the principal of a bustling secondary school. Lunchtime rolls around, and you notice more and more hungry students slipping out the gates to buy snacks and food elsewhere. Your first instinct tells you that the food must be terrible. You start thinking about replacing the cook, rewriting the menu, and maybe even raising the budget.

But then you pause. Instead of relying on your hunch, you decide to investigate the situation more thoroughly and gather some data. You run a quick survey with students, check purchase records, and even time how long students spend in the lunch line. What you discover changes your initial interpretation of the situation: the students actually rate the food pretty highly—it’s the long wait times that push them to leave.

With that insight, you add another serving line and rearrange staff schedules. Within weeks, the cafeteria is full again. By using data to drive your decision-making, you avoid making costly mistakes and address the real problem at hand. 

This scenario is an example of data-driven decision-making, the practice of using evidence and information to guide our choices. At its core, data-driven decision-making means asking, “What does the data tell us?” before acting. Data-driven decision-making can be found in practically every aspect of modern life. If you own a smart watch or sports wearable, you likely check your step count to decide whether or not to walk home from work or have that extra cookie. In public places, data is used to inform planning and strategy: schools use attendance records to support struggling students, hospitals analyze patient data to improve care, and businesses adjust strategy based on customer behavior. 

So why is data-driven decision-making so important? And why can’t we just go with our gut instinct? According to a study conducted in 2016, just over half of Americans relied on their gut to decide what was right and what wasn’t.7 If that many people rely on instinct, why do we need data?

Data-driven decision-making helps us see reality more clearly. And while there are some situations where going with our gut might be best—for example, when time is short and we have to act quickly, or when we’re drawing on deep personal experience—most of the time, our instincts are shaped by biases, assumptions, or incomplete information. Data gives us a way to check those instincts, challenge our blind spots, and make choices that are grounded in evidence rather than guesswork.

Decision Support Systems (DSS): Computer-based tools that help people make informed decisions by collecting, organizing, and analyzing data. DSS often combine models, data, and user-friendly interfaces to support complex problem-solving—for example, helping managers compare scenarios or forecast outcomes.

Business Intelligence (BI): A set of technologies, processes, and practices used to transform raw data into meaningful insights for decision-making. BI tools typically include dashboards, reporting systems, and data visualizations that allow organizations to monitor performance, identify trends, and support strategic planning.

Big Data: Extremely large and complex datasets that cannot be managed or analyzed with traditional methods. Big data is often described using the “3 Vs”: volume (large amounts of data), velocity (data generated quickly and in real time), and variety (different formats such as text, images, video, or sensor data). Techniques such as machine learning and cloud computing are commonly used to extract insights from big data.

Internet of Things (IoT) Network: A system of physical devices—such as sensors, wearables, vehicles, and appliances—connected to the internet. These devices collect and exchange data, often in real time, enabling smarter decision-making in areas like healthcare, transportation, and urban planning—for example, smart cities using traffic sensors to reduce congestion.

ArcGIS Online: A cloud-based mapping and analysis platform developed by the technology company Esri. It allows users to create, share, and analyze interactive maps and spatial data. ArcGIS Online is widely used by governments, businesses, and researchers to visualize geographic patterns, track changes over time, and make location-based decisions (for example, planning city infrastructure or mapping environmental risks).

Long before big data arrived, humans were using numbers to make decisions. Ancient civilizations like Egypt and China relied on meticulous record-keeping—censuses, crop yields, and tax registers—to guide choices about taxation, military service, and food distribution. The Romans, for example, institutionalized the census, conducting it every five years to manage property rights, taxation, and conscription. By the 17th and 18th centuries, statistics had become a formal discipline in Europe and were used to monitor populations, trade, and health. English Haberdasher turned demographer John Graunt, for example, started to study death records that had been kept by London parishes for over a century. He noticed that certain phenomena of death statistics appeared at regular intervals, prompting him to write in 1662 Natural and Political Observations… Made upon the Bills of Mortality. His tables gave the city’s authorities new insights into public health trends, such as plague deaths and seasonal mortality. In turn, they could make better decisions about the risks to the population. 

In the early 20th century, American engineer Frederick Winslow Taylor developed the idea of scientific management, which laid the foundations for modern data-driven approaches to work.15 Through his pioneering time-and-motion studies, Taylor systematically measured how tasks were performed in factories, breaking them down into discrete steps and recording how long each took. By analyzing this data, he sought to identify the most efficient methods of completing work, redesigning tasks to maximize productivity and minimize waste. Although controversial for its mechanistic view of labor, Taylorism marked one of the first major attempts to apply quantitative analysis to decision-making in the workplace. 

By the 1970s, corporate data had begun to grow, and the concept of decision support systems (DSS) emerged. The term was originally coined by a British academic called P. G. W. Keen in a book he wrote with Scott Morton titled Decision Support Systems: An Organizational Perspective.16 These early computer systems helped managers make decisions based on structured data, but Keen and Morton emphasized that human judgment was still an essential part of the process. And as computer use grew, particularly in the corporate sector, the term business intelligence gained traction. This concept refers to the use of databases, dashboards, and reporting tools to inform corporate strategy, and today can be seen in tools such as Power BI, Oracle, and Tableau. However, it’s not a new term and was first used by Richard Millar Devens to explain how a banker called Sir Henry Furnese was profiting by gathering and acting on information before his competitors.17 Business intelligence resurfaced again in 1958 when IBM computer scientist Hans Peter Luhn spoke about the potential of gathering business intelligence through emerging technologies.18

In 2024, an S&P Global Market Intelligence Study found that 96% of businesses believed that using data in decision-making processes was important.12 The exponential growth in the popularity and use of data-driven decision-making in recent years is primarily due to the greater availability of data. A few decades ago, large amounts of data were only available to a select few—researchers, policy makers, and CEOs. Now, data is instantly available to everyone with access to the internet. In fact, humanity now generates more than 2.5 quintillion bytes of data per day.9 That equates to a lot of decisions that can be driven. 

This explosion of information is what gave rise to the era of big data, a term that describes not only the sheer volume of data available but also its speed and variety.19 Big data technologies allow organizations to capture, store, and analyze massive, complex datasets that were once unmanageable. For decision-making, this means moving beyond static reports toward real-time insights that can inform everything from predicting customer behavior to detecting fraud or anticipating supply chain disruptions.

John Graunt:

An English haberdasher from London, Graunt played an important role in the development of demography and modern statistics. In his 1662 work, Natural and Political Observations… upon the Bills of Mortality, he analyzed London’s weekly death records to estimate population size, birth and death rates, and patterns of illness—introducing the first known life tables and laying the groundwork for epidemiology and social statistics

Frederick Winslow Taylor: American mechanical engineer known as the father of scientific management. Taylor developed time-and-motion studies to improve industrial efficiency and advocated for a systematic, data-driven approach to work processes. His 1911 book, The Principles of Scientific Management, shaped modern management and industrial engineering, though critics argued his methods treated workers too mechanically.

P. G. W. Keen: A British-American scholar instrumental in the development of DSS. Keen co-authored the foundational text Decision Support Systems: An Organizational Perspective and was one of the early thinkers to explore how computer systems can aid managerial decision-making.

Richard Millar Devens: An American writer best known for introducing the term “business intelligence” in his 1865 work, Cyclopaedia of Commercial and Business Anecdotes. In it, he recounts how Sir Henry Furnese’s success was built on gathering and interpreting information, foreshadowing modern concepts of strategic, data-informed business practices.

Data-driven decision-making shapes how we act as individuals, how organizations compete, and how governments serve their citizens. Its impacts are visible at every level, from personal health choices to global strategies for urban sustainability.

We make better decisions

Intuition, or gut instinct, has been idealized in modern culture largely because so many revered figures have celebrated it. Albert Einstein, for example, is often quoted as saying, “The intuitive mind is a sacred gift,” while in business, Apple’s former CEO Steve Jobs urged people to “Have the courage to follow your heart and intuition; they somehow already know what you want to become.”8 According to these revered experts, we don’t need anything but ourselves to make the best decisions. 

But actually, we do. According to research, using data significantly improves our decision-making. Global consultancy firm PwC surveyed more than 1,000 senior executives in large businesses and found that highly data-driven organizations were three times more likely to report significant improvements in the decision-making processes, compared to those that used less data.13  Another study by another consultancy firm, McKinsey, in 2014 found that being data-driven makes businesses 23 times more likely to acquire customers, six times more likely to retain those customers, and 19 times more likely to be profitable.14 Yet the PwC report also emphasizes that seasoned, successful business leaders are also guided by experience and intuition—sometimes the data only takes you so far, and it requires a human to make the final choice. 

Individual decision-making

In 2025, it’s predicted that over 454 million people will use smartwatches or fitness devices, making wearables one of the most widely adopted digital health tools worldwide.6 By offering real-time feedback on steps taken, heart rate, sleep quality, glucose levels, or blood pressure, these devices put health data directly into the hands of individuals. This has shifted decision-making power away from occasional doctor visits and into people’s daily routines. Data from their devices can help them decide when to prioritize sleep, adjust their diet, or seek medical care. Research conducted in the United States, for example, shows that wearables have already improved physical activity levels, reduced cardiovascular hospitalizations, and supported better diabetes and hypertension management.5

The significance of this shift lies in how wearables make individual decision-making data-driven. Instead of guessing whether they are getting enough exercise or managing their medical condition well, people can rely on concrete metrics and alerts. However, there are still challenges—many users abandon their devices within a year, and concerns over data privacy and ownership can affect how confidently people act on the information their wearables provide.5 Unfortunately, these devices don’t come with data-driven motivation to help people stick to their fitness and lifestyle goals. 

Public decision-making

Beyond the individual and organizational level, data-driven decision-making has had a profound impact on how governments and public institutions design and deliver services. During the COVID-19 pandemic, for example, governments used real-time mobility and infection data to shape lockdown measures, allocate resources, and plan vaccination campaigns.3 Similarly, cities increasingly rely on “smart” data to make urban planning decisions that improve sustainability and quality of life. These initiatives use real-time information from sensors, IoT networks, and citizen interactions to optimize traffic flow, manage energy usage, and improve urban services.

Barcelona has emerged as a leading example of how urban areas can leverage IoT and data to improve governance and quality of life. Through its CityOS initiative and open-source Sentilo platform, the city has implemented a network of sensors that monitor everything from air quality and noise levels to traffic patterns, energy usage, waste management, and irrigation systems. This real-time data enables city officials to optimize public services, including synchronizing smart traffic lights to ease congestion and adaptively managing water and energy resources for sustainability and efficiency.23

Although data-driven decision-making has been widely celebrated for improving efficiency and outcomes, it has also sparked a number of important controversies. Questions about privacy, inequality, and bias reveal that relying on data is not always neutral and can produce risks and unintended consequences alongside its benefits.

Data privacy and security 

One of the most pressing concerns surrounding data-driven decision-making is data privacy and security. Organizations now collect vast amounts of sensitive personal information, from consumer habits to health records, raising the risk of misuse, breaches, or surveillance.20

This creates a tension between the benefits of personalization and the rights of individuals to control their own data. While some organizations comply with regulations such as the General Data Protection Regulation (GDPR) and Health Insurance Portability and Accountability Act (HIPAA), others are less transparent about how they handle, store, and share individuals’ data.21 When large data breaches occur—such as the 2017 Equifax breach, which exposed the personal information of around 147 million people—trust between organizations and their clients and users can be seriously eroded.21

To fully realize the benefits of data-driven decision-making, organizations must balance innovation with responsibility, embedding transparency, security, and ethical safeguards into every stage of the process.

Data-driven decision-making inequality

The spread of data-driven decision-making has been far from uniform. Small and medium-sized enterprises (SMEs) in particular face significant hurdles. In 2012, only 0.2% of SMEs had adopted business and big data analytics compared to 25% of firms with over 1000 employees.1 

While digitalization has opened new opportunities for SMEs to reach wider markets, many have struggled to capture the benefits of this technological shift.2 Research consistently shows that SMEs lag behind larger firms in adopting digital tools and analytical applications that could help them harness data, strengthen competitiveness, and move toward becoming information-driven decision-makers.1 For many smaller companies, the resources needed to invest in advanced business analytics simply remain out of reach. 

These disparities also appear between countries. Advanced economies, particularly in North America and Western Europe, have been much quicker to adopt data analytics compared to many developing and emerging economies. This is because access to infrastructure, skilled labor, and affordable digital tools is more limited.3 For example, Eurostat data shows that in 2020, over 19% of EU enterprises in the Netherlands and Ireland were using big data analytics compared to less than 5% in countries such as Romania and Cyprus.4

The uneven growth of data-driven decision-making—across company size and national borders—reinforces broader social and economic inequalities, and underscores the urgent need to address who has access to data and who does not.

Bias in decision-making 

Artificial intelligence (AI) has become a powerful driver of data-driven decision-making. Algorithms are used to process vast amounts of information, detect patterns, and generate predictions or recommendations that can guide choices across diverse sectors. 

However, these algorithms, which are designed, trained, and refined by humans, are not immune to bias. Algorithms work by identifying patterns in the data they are given, which means any existing distortions or imbalances in that data can be learned and reproduced as if they were objective truths. The choice of what data to collect, how to label it, and which outcomes to optimize already introduces human judgment into systems that appear objective. When these systems are scaled up across organizations, they can amplify individual biases into widespread patterns of unfairness. 

In healthcare, for example, predictive algorithms are increasingly used to flag patients at risk of complications. However, studies have shown that these tools can reflect and reinforce racial bias when trained on unequal datasets.22 A well-known study by Dr. Ziad Obermeyer at the University of California, Berkeley, revealed that a healthcare algorithm used in U.S. hospitals systematically underestimated the health needs of Black patients. Because the algorithm used past healthcare spending as a proxy for illness, it reflected existing racial disparities in access to care—meaning Black patients had to be sicker than White patients to receive the same risk score. Correcting this bias would have nearly tripled the number of Black patients identified for extra care programs.

Google’s Project Oxygen

Google is often portrayed as the ultimate workplace. In films and TV shows, it’s the dream office, complete with pool tables, nap pods, and free food on tap. But behind this playful image lies something far more strategic. Google’s reputation as one of the best places to work is built not just on perks, but on a deep commitment to data-driven decision-making about its people.

Project Oxygen is part of Google’s broader “people analytics” strategy.10 In this initiative, the company mined data from more than 10,000 performance reviews and matched it against employee retention statistics. The analysis revealed a set of behaviors that consistently defined high-performing managers—such as effective communication, supporting career development, and empowering teams rather than micromanaging them. Instead of leaving leadership to instinct or personality, Google designed training programs to deliberately strengthen these competencies across the company. As a result, the median favorability scores for managers rose from 83 percent to 88 percent, reflecting tangible improvements in how employees experienced their leaders.10

The data used in Project Oxygen enabled Google to break down what makes a “good boss” into concrete, learnable skills. This not only improved retention and performance, but also created a more supportive workplace culture. More broadly, Google’s approach illustrates how data-driven decision-making provides a competitive edge on multiple fronts. Just as user search data powers innovations in algorithms, AI, and product design, internal data fuels decisions about hiring, training, and leadership. 

Location, Location, Location 

In 2008, amid the global financial crisis, American coffee chain Starbucks was forced to close hundreds of its storefronts. In response, the company’s returning CEO, Howard Schultz, decided that Starbucks needed to take a much more analytical approach to deciding where to place their stores. Schultz established a long-term contract with a location-analytics firm called Esri, which uses technology to analyze maps and retail locations. Rather than just picking out spots that looked nice or were situated in a desirable neighborhood, Starbucks and Esri analyzed data such as population density, average incomes, and traffic patterns to identify target areas for new stores. The software also took into account where to place new stores without impacting the sales of existing Starbucks cafés.11 

It’s not just Starbucks that is using Esri’s technology (called ArcGIS Online). Petco, the one-stop shop for animal lovers, uses the mapping software to mitigate potential risks of opening new stores in unsuitable locations. For example, launching a new store in an area with a low concentration of pet owners would be a disaster for the company, as would opening near a competitor. Similarly, fast-food chain Wendy’s used location analytics to determine how far their customers in Land O’Lakes, Florida, were willing to travel for their burgers. They surveyed existing customers to understand their travel time between home, work, and Wendy’s, and used the data to make informed decisions about where to invest in new restaurants.11

Personalization 

Data plays an important role in the process of personalization, where experiences, products, or interactions are tailored to individual preferences, behaviors, and needs. In this article, Annika Steele looks at how this process is leveraged by companies to enhance user engagement, improve customer satisfaction, and drive better outcomes. But there are also ethical and security challenges related to the use of personal data.  

Decision-Making 

We make thousands of decisions every day, many of them with help from data and technology. Lauren Braithwaite explores the process of decision-making, something we do all the time but perhaps don’t think about very much. In this article, we look at the theories behind decision-making and why, after making decisions all day long, we end up with “decision fatigue.”

1. E-skills UK. (2013). Big data analytics: adoption and employment trends, 2012-2017. E-skills UK. https://www.techskills.org/globalassets/pdfs/research-2013/bigdataanalytics_report_nov2013.pdf 
2. Gehrmann, L. C. (2020). Data-driven decision-making in the innovation process of SMEs.BMS. [Master’s thesis, University of Twente]. University of Twente Student Theses.. http://purl.utwente.nl/essays/82879
3. World Bank. (2021). World development report 2021: Data for better lives. World Bank. https://doi.org/10.1596/978-1-4648-1600-0 
4. Eurostat. (2021). Enterprises using big data analysis by type of analysis and size class [isoc_bde15b]. Eurostat. https://ec.europa.eu/eurostat/databrowser/view/isoc_bde15b/default/table 
5. Mahabub, S., Jahan, I., Islam, M. N., & Das, B. C. (2024). The impact of wearable technology on health monitoring: A data-driven analysis with real-world case studies and innovations. Journal of Electrical Systems, 20(11s), 172–183.
6. Kumar, N. (2025, July 8). Smartwatch statistics 2025: Market shares & sales data. DemandSage. https://www.demandsage.com/smartwatch-statistics/
7. Garrett, R. K., & Weeks, B. E. (2017). Epistemic beliefs’ role in promoting misperceptions and conspiracist ideation. PLOS ONE, 12(9), e0184733. https://doi.org/10.1371/journal.pone.0184733
8. Stobierski, T. (2019, August 26). The advantages of data-driven decision-making. Harvard Business School Online. https://online.hbs.edu/blog/post/data-driven-decision-making
9. Domo. (2024, December 18). Data Never Sleeps 12.0 [Infographic]. Domo. https://www.domo.com/learn/infographic/data-never-sleeps-12
10. Garvin, D. A. (2013, December). How Google Sold Its Engineers on Management. Harvard Business Review, 91(12), 74–82.
11. Thau, B. (2014, April 24). How big data helps retailers like Starbucks pick store locations—An (unsung) key to retail success. Forbes. https://www.forbes.com/sites/barbarathau/2014/04/24/how-big-data-helps-retailers-like-starbucks-pick-store-locations-an-unsung-key-to-retail-success/
12. S&P Global Market Intelligence. (n.d.). About Market Intelligence. S&P Global. 
13. https://www.spglobal.com/market-intelligence/en
14. PricewaterhouseCoopers. (2016). Data-driven: Big decisions in the intelligence age [Report]. PwC. https://www.pwc.com/jp/ja/knowledge/thoughtleadership/big-decision-survey/assets/pdf/view-of-decisions.pdf
15. Bokman, A., Fiedler, L., Perrey, J., & Pickersgill, A. (2014, July 1). Five facts: How customer analytics boosts corporate performance. McKinsey & Company. https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance
16. Kanigel, R. (1997). The one best way: Frederick Winslow Taylor and the enigma of efficiency. MIT Press.
17. Keen, P. G. W., & Scott Morton, M. S. (1978). Decision support systems: An organizational perspective. Addison-Wesley.
18. IBM. (2021, August 9). What is business intelligence (BI)? IBM Think. https://www.ibm.com/think/topics/business-intelligence
19. Foote, K. D. (2023, April 6). A brief history of business intelligence. DATAVERSITY. https://www.dataversity.net/brief-history-business-intelligence/
20. Gartner. (2018). Big data. Gartner IT Glossary. https://www.gartner.com/en/information-technology/glossary/big-data
21. IEEE Transmitter. (2022, June 8). Wearables and privacy: What you need to know. IEEE Transmitter. https://transmitter.ieee.org/wearables-and-privacy-what-you-need-to-know/
22. Sahota, N. (2023, December 6). Leveraging AI for data-driven decision-making while safeguarding privacy and security. Forbes. https://www.forbes.com/sites/neilsahota/2023/12/06/leveraging-ai-for-data-driven-decision-making-while-safeguarding-privacy-and-security/
23. Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453. https://doi.org/10.1126/science.aax2342 
24. Barcelona City Council. (2025). Smart city. Info Barcelona. https://www.barcelona.cat/infobarcelona/en/tema/smart-city