Prescriptive Analytics, the most advanced stage in the data world, is a powerful data analysis approach that not only tells organizations “what might happen” but also suggests “what should be done.” Positioned at the peak of the analytics spectrum, this approach helps decision-makers in the business world determine the most optimal solutions to complex problems. Simply put, Prescriptive Analytics answers the question “which path should we follow to achieve the best result?”
Core Components of Prescriptive Analytics
Prescriptive analytics works through the integrated use of various mathematical and computational techniques. The core components behind this sophisticated approach are:
Data and Model Integration
Prescriptive analytics integrates information from various data sources and combines them with meaningful models. In this integration process:
- Structured and unstructured data are collected
- Internal and external data sources are combined
- Real-time and historical data are blended
- Business rules and constraints are incorporated into the model
According to IDC research, 83% of companies using prescriptive analytics consider data and model integration as one of the most critical success factors.
Optimization Techniques
Optimization lies at the heart of prescriptive analytics. These techniques help determine decisions that will yield the best results under specific constraints:
- Linear and non-linear programming
- Integer and mixed-integer programming
- Multi-objective optimization
- Constraint-based optimization
- Meta-heuristic algorithms (genetic algorithms, particle swarm optimization, etc.)
Simulation and Scenario Analysis
Simulation creates a virtual environment to test the potential outcomes of different scenarios. This component includes:
- Monte Carlo simulations
- Discrete event simulations
- System dynamics models
- What-if analyses
- Stress tests
Decision Modeling
Decision modeling enables evaluating the outcomes of different alternatives and determining the best action:
- Decision trees and influence diagrams
- Markov decision processes
- Game theory models
- Bayesian networks
- Multi-criteria decision analysis
Prescriptive Analytics vs Other Analytics Types
There are four fundamental approaches on the data analytics spectrum. Understanding the difference between prescriptive analytics and other approaches is important to grasp its value.
Comparison with Descriptive Analytics
Descriptive analytics answers the question “what happened?” and summarizes past data. Whereas prescriptive analytics tells companies what they should do. While descriptive analytics looks backward, prescriptive analytics provides forward-looking action plans.
Comparison with Diagnostic Analytics
Diagnostic analytics focuses on the question “why did it happen?” and investigates the causes of specific outcomes. Prescriptive analytics is not content with just knowing the causes but uses this knowledge to determine the best action plan.
Comparison with Predictive Analytics
Predictive analytics answers the question “what might happen?” by predicting future scenarios. Prescriptive analytics takes these predictions a step further by determining which actions should be taken to achieve desired outcomes.
According to research conducted by TDWI (The Data Warehousing Institute), while only 10% of organizations use prescriptive analytics, 30% use predictive, 40% use diagnostic, and 90% use descriptive analytics. This situation demonstrates the yet undiscovered potential of prescriptive analytics.
Advantages of Prescriptive Analytics
Prescriptive analytics provides businesses with various strategic advantages:
Proactive Decision Making
Prescriptive analytics enables organizations to be proactive rather than reactive. Instead of seeking solutions after problems occur, it identifies potential problems in advance and provides the best solution path. For example, an airline can automatically determine flight rescheduling strategies that maximize passenger satisfaction while minimizing costs when adverse weather conditions are predicted.
Resource Optimization
It increases operational efficiency by determining how resources can be used most efficiently. It provides valuable insights in areas such as production planning, inventory management, staff scheduling, and supply chain optimization.
Risk Management
It helps manage uncertainty by identifying potential risks in advance and suggesting the most appropriate strategies to mitigate these risks. It is particularly valuable for financial institutions, insurance companies, and investment firms.
Competitive Advantage
Prescriptive analytics helps identify opportunities that competitors have not yet acted upon and develop the best strategy to take advantage of these opportunities. In rapidly changing market conditions, it enables companies to remain agile and gain competitive advantage.
Prescriptive Analytics Solutions with Qlik
Qlik offers advanced solutions in the field of prescriptive analytics that enable organizations to make data-driven decisions. Qlik’s prescriptive analytics capabilities include a comprehensive approach ranging from data discovery to automated recommendations.
Qlik’s Prescriptive Analytics Capabilities
The Qlik platform offers various capabilities for prescriptive analytics:
- Qlik Insight Advisor: Provides AI-powered recommendations, guiding users on “what should be done”
- Data Discovery and Visualization: Thanks to the associative data model, users can make more informed decisions by exploring data and understanding relationships
- Augmented Intelligence: Offers cognitive capabilities that enhance people’s insight potential and suggest the best actions
Data Integration and Modeling
Qlik offers powerful solutions for data integration from various sources:
- Qlik Data Integration: Combines different data sources and prepares them for prescriptive analytics
- Qlik Catalog: Manages data assets and ensures users access the right data
- Data Modeling Automation: Automatically models complex data relationships, enabling rapid creation of analytical models
Scenario Analysis and Simulation
Qlik solutions offer powerful capabilities for evaluating the effects of different scenarios:
- Dynamic Calculations: Ability to instantly see scenarios by changing different variables
- What-If Analysis: Visualizing how changing different parameters will affect results
- Sensitivity Analysis: Determining which factors most affect the results
Decision Support Systems
Qlik’s decision support capabilities are designed to recommend the best actions to users:
- Smart Alerts: Automatically provides best action recommendations when business conditions exceed certain thresholds
- Optimal Path Recommendations: Suggests the best strategy to reach a specific goal
- Decision Automation: Enables automation of routine decisions, allowing users to focus on more strategic issues
Challenges in Prescriptive Analytics Applications and Solution Proposals
Despite all its benefits, prescriptive analytics may encounter various challenges. Understanding and addressing these challenges is critical for a successful implementation.
Complexity Management
Prescriptive analytics models may contain numerous variables, constraints, and complex business rules. This complexity can lead to challenges in model creation, maintenance, and comprehensibility.
Solution Proposal: Qlik’s intuitive interface and automatic modeling features make complexity manageable. Developing models gradually and starting with focused use cases is important for successful implementation.
Model Reliability
Since prescriptive analytics relies on predictive models, prediction accuracy is critically important. Incorrect predictions can lead to wrong recommendations and consequently bad decisions.
Solution Proposal: Qlik’s model monitoring and evaluation features enable continuous monitoring of the accuracy of prediction models. Additionally, multiple modeling approaches and ensemble learning techniques can increase model reliability.
Implementation and Adoption
The adoption of prescriptive analytics solutions across the organization may involve technical and cultural challenges. Users may struggle to trust “black box” recommendations or resist change.
Solution Proposal: Qlik’s transparent analytics features clearly show the logic and data behind recommendations. User training, sharing success stories, and gradual implementation can increase adoption rates. Additionally, it is important to emphasize that prescriptive analytics is designed to support business users’ decisions rather than replace them.
The Future of Prescriptive Analytics
As technology evolves, prescriptive analytics is expected to rapidly evolve as well. Here are some important trends that will shape this field in the coming years:
Artificial Intelligence and Automation Integration
Artificial intelligence and machine learning will make prescriptive analytics more powerful and accessible. In the future:
- Thanks to natural language processing, users will be able to ask questions in natural language and receive action recommendations
- Deep learning algorithms will be able to provide more accurate recommendations by detecting previously unnoticed patterns
- Automatic model selection and optimization will allow even users without technical knowledge to create complex prescriptive models
Real-Time Prescriptive Analytics
As the business world becomes increasingly faster, the ability to make real-time decisions will be critically important:
- Edge computing technologies will enable data to be analyzed directly where it is produced
- Stream processing platforms will provide instant data analysis and action recommendations
- Data from IoT (Internet of Things) devices will be fed to prescriptive systems in real-time
Business Process Automation
Prescriptive analytics will play an increasingly central role in business process automation:
- Integration with RPA (Robotic Process Automation) will enable automatic implementation of recommended actions
- Closed-loop systems will collect feedback for continuous learning and optimization
- Business process management systems will be able to dynamically reconfigure processes according to recommendations from prescriptive models
Conclusion
Prescriptive Analytics, as the most advanced form of the data analysis spectrum, enables organizations not only to predict events but also to determine the best action plans. Using powerful tools such as optimization techniques, simulation, and decision modeling, this approach, which offers optimal solutions to complex business problems, has now become a necessity, not a luxury, for businesses seeking competitive advantage.
Explore Qlik’s prescriptive analytics solutions to take your business’s data strategy to the next level and optimize your decision-making processes. Take action today to improve your business results with data-driven decisions and gain competitive advantage.
References:
- IDC, “Prescriptive Analytics Market Trends”, 2023
- TDWI, “Advanced Analytics: Beyond Business Intelligence”, 2023
- Forrester Research, “The Forrester Wave: Prescriptive Analytics, Q2 2023”