As businesses and organizations across sectors continue adopting data solutions to improve operations, the use of data analytics and prescriptive analytics will become increasingly important to business success while the big data industry continues to grow. Experts valued the global big data market size at $199.63 billion in 2024, and it’s projected to reach $573.47 billion by 2033. With three primary types of business analytics (prescriptive, predictive, and descriptive), understanding the subtle differences can help business leaders and data analysts determine which models will best inform their business decisions and support their goals.
Below, we outline the basics of prescriptive analytics, how it works, its benefits, and real-world applications — plus how an advanced education in data analytics could help you achieve your professional goals.
What Is Prescriptive Analytics?
Analytics is among the most essential business management tools available. Prescriptive analytics uses data analysis to achieve objectives that enhance business operations, performance, and outcomes. It leverages historical data to create business performance models that can help a business owner predict what will happen in their business while also revealing which variables they could manipulate to improve those anticipated outcomes.
How Prescriptive Analytics Differs From Other Analytics
Prescriptive analytics aims to provide business leaders with actionable insights that allow them to make data-driven decisions to increase efficiency, maximize productivity, enhance customer experiences, cut costs, and boost the bottom line. With its value and success relying heavily on the accuracy of the data, prescriptive analytics works alongside descriptive analytics and predictive analytics to meet these goals.
Prescriptive Analytics vs. Descriptive Analytics
Descriptive analytics consists of historical data that reveals information about what has happened in a business, so businesses can understand and evaluate past performance.
Prescriptive Analytics vs. Predictive Analytics
Predictive analytics builds complex models using historical data, market information, economics, and other variables to provide information about what will likely happen in a business’s future (given the accuracy of the model’s assumptions). Leaders use predictive analytics forecasts to identify trends and better understand the path a business is on.
How Prescriptive Analytics Works
Prescriptive analytics works in a similar way to predictive analytics — data collection, modeling, reporting, and analysis. With prescriptive analytics, however, the models are taken a step further, allowing individual variables to be manipulated. As a result, prescriptive analytics can be used to make recommendations regarding business decisions and changes. Showing what could happen if variable x, y, or z were to be changed in a particular way can project a range of potential outcomes (rather than just trends).
Benefits of Prescriptive Analytics
Transforming data insights into actionable strategies, prescriptive analytics offers several benefits and advantages.
Optimizes Decision-Making
With predictive analytics, business leaders can be proactive instead of reactive. They use data-driven decisions to leverage opportunities and avoid challenges, instead of responding to them after they have occurred.
Improves Risk Management
Leaders turn to prescriptive analytics forecasts to identify potential challenges and pitfalls before they occur — ensuring they are able to take action to prepare for or prevent problems.
Enhances Operational Efficiency
Prescriptive analytics can help reveal workflow bottlenecks, waste, and inefficiencies in operations in order to elevate efficiency, productivity, and profitability.
Drives Revenue Growth
Businesses use prescriptive analytics to identify the variables that represent their profit and growth drivers. This informs business strategy with data-driven decision-making to cultivate more successful outcomes.
Personalizes Customer Experiences
Paired with customer data, prescriptive analytics can help create customer personas, better understand the customer journey, and personalize a consumer’s experiences with the business, brand, and marketing materials. This could, in turn, boost leads, conversions, and customer satisfaction.
Real-World Applications of Prescriptive Analytics
Prescriptive analytics offers powerful, real-world applications across industries.
Healthcare
In healthcare, prescriptive analytics may enhance both patient outcomes and business models, helping to:
- Optimize treatment plans and resource allocations.
- Improve patient risk predictions.
- Reduce hospital readmissions.
Supply Chain and Logistics
Prescriptive analytics in the supply chain uses machine learning (ML) and data to optimize inventory levels and delivery routes as well as suggest strategies for avoiding future problems and reducing costs.
Finance and Risk Management
In the finance realm, prescriptive analytics has several applications for optimizing financial health and mitigating/managing risk, such as:
- Fraud detection and prevention
- Investment optimization
- Credit risk management
- Risk modeling
- Increased efficiency
Marketing and Customer Experience
By gathering customer data at every level of the marketing funnel and during every interaction with a business, businesses may optimize their marketing messaging for maximizing conversions and marketing return on investment (ROI) — while also using customer data to improve the customer experience and customer retention.
Challenges in Implementing Prescriptive Analytics
Both business management and data analytics are complex, so integrating the two isn’t always simple. Data analysts and business leaders face several challenges in implementing prescriptive analytics for better-informed decisions. A few primary concerns include:
- Data quality issues – Data quality issues (such as incompleteness, inconsistencies, inaccuracies, irrelevance, or outdated numbers) can impact the validity, accuracy, and usefulness of data — thus rendering data-based recommendations less effective (in the best-case scenarios) or harmful (in worst-case scenarios).
- Model complexity – Businesses, economies, markets, consumer behaviors, and all the other factors impacting business performance are complex systems on their own. Altogether, they can become mystifying, which can result in creating models that are difficult to read, interpret, and apply to real-world scenarios.
- Privacy and ethics – Leveraging big data carries the risk of stumbling upon privacy and ethics violations. These issues can stem from the collection of large amounts of personal data that could violate rules and regulations if an individual’s personal information is inferred without consent or used to profile them. For instance, in 2023, the U.S. Equal Employment Opportunity Commission (EEOC) settled a case against iTutorGroup after its recruiting software was programmed to automatically reject female applicants ages 55+ and male applicants 60+ — illustrating how prescriptive screening tools and algorithms can encode discriminatory rules and quietly exclude protected groups.
Being aware of and prepared to overcome common prescriptive analytics pitfalls can help data analysts and business leaders work together more seamlessly.
Overcoming Implementation Challenges
With clear business objectives and questions to answer, professionals who understand what they want to learn from their data can help narrow down the list of what information to collect, along with which metrics should be measured and reported on. Knowing which key performance indicators (KPIs) are most valuable to a business can save time and energy by focusing on only the data that is vital to decision-making processes.
Overcoming implementation challenges and producing reliable business intelligence and actionable insights calls for sound data collection, organization, and reporting systems and tools that will be ready to scale with a business. Additionally, leveraging fully integrated enterprise resource management tools can help prevent data siloing and inaccurate, redundant, or lost information. Plus, now more than ever, cutting-edge tools like machine learning and artificial intelligence (AI)-powered modeling can help data analysts parse meaning from increasingly complex systems and models while incorporating data variability and uncertainty quantification.
The Future of Prescriptive Analytics
As technology advances, businesses are called to adapt to and adopt innovation. This means that more are poised to rely on prescriptive business analytics to make informed decisions and remain competitive in their respective industries or markets.
As is the case with many tech-based industries, the future trends of prescriptive analytics will likely reveal a reliance on the integration of:
- Artificial intelligence
- Machine learning
- Cloud computing
- Real-time data
- Increased data availability through the growing grid of the Internet of Things (IoT)
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Data has become an invaluable business asset, and business leaders rely on data scientists and analysts to uncover insights that drive smarter decision-making. If you’re interested in pursuing or advancing a career in data and business analytics, a master’s-level program can help you strengthen your skills while gaining technical knowledge and experience in data systems, collection, reporting, modeling, forecasting, and application.
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