Adoption of Predictive and Prescriptive Analytics: Challenges and Opportunities

Today, leveraging data analytics is no longer an option, but a necessity for organizations seeking to gain a competitive advantage and make the most of information.

However, there is often confusion when it comes to distinguishing the types of methodologies, the benefits of each, use cases, or how they complement each other. We review each of their characteristics and the future prospects they represent.

Predictive Analytics

Predictive analytics is a branch of advanced analytics that makes predictions about outcomes, based on historical data combined with statistical models, data mining techniques, and Machine Learning.

This type of analytics is very useful for finding patterns in data to identify risks and opportunities at a time when companies are flooded with data. To gain useful insights from data, data scientists use Deep Learning and ML algorithms to find patterns and make predictions about future events.

Some of the techniques included in predictive analytics are linear and logistic regression models, neural networks, and decision trees.

Benefits of Predictive Analytics

Relying on predictive analytics brings numerous benefits, both commercially in inventory management, workforce, marketing campaigns, and operations:

• Security: Combining automation and predictive analytics improves data security. Specific patterns associated with suspicious or unusual behavior can trigger specific security procedures.
• Risk reduction: A key pillar for businesses is to reduce their risk profiles. Predictive analytics helps to understand whether enterprise coverage is adequate.
• Operational efficiency: more efficient workflows translate into better profit margins.
• Improved decision-making: running any business involves making good decisions, so predictive analytics provides information to help you make the best decisions and gain a competitive advantage.
• Productivity: by improving decision-making based on the analysis of historical data, organizations achieve higher productivity and profits.
• Higher profits: predictive analytics identifies areas of value in each department and can suggest the best and fastest way to leverage this value for the benefit of the company, such as converting leads, predicting defaults, better offers, etc.

How Predictive Analytics works

Data scientists use predictive models to identify correlations between different elements of given data sets. Once this data collection is complete, a statistical model is created, trained, and modified to make accurate predictions.

To create predictive analytics frameworks, according to Google Cloud, five steps must be followed:

• Determining the problem: good prediction starts with a good thesis and a set of requirements that determine the objectives. Delimiting the problem to be solved allows for determining which predictive analytics method should be used to solve it.
• Obtain and organize the data: before developing predictive analytics models, it is necessary to identify the data flows and organize the data sets in a repository.
  Preprocess the data: raw data alone is of little use. To prepare the data for predictive analytics models, it must be cleaned to remove anomalies or outliers, which may be the result of input or measurement errors.
• Develop predictive models: data scientists have various tools and techniques to develop predictive models depending on the problem to be solved and the nature of the data set: ML, regression models, and decision trees.
• Validate the results and apply modifications accordingly: the accuracy of the model must be checked and adjusted as necessary. When acceptable results are achieved, make them available to collaborators in an application, web, or data dashboard.

Predictive Analytics Use Cases

This type of analysis can be used to optimize operations, increase revenues, and mitigate risks. Examples include:

• Fraud detection: Predictive analytics examines all actions on a company’s network in real-time to detect anomalies that indicate fraud and other vulnerabilities.
• Conversion and purchase prediction: Companies can take action, such as retargeting, based on data that predicts a higher likelihood of conversion and purchase intent.
• Risk reduction: predictive analytics is able to assess and determine the likelihood of future imagery.
• Customer segmentation: by partitioning customer databases, analytics can be used to make forward-looking decisions to tailor content to unique audiences.
  Maintenance forecasting: Data can be used to predict when routine equipment maintenance should be performed. This can be scheduled before any malfunctions occur.

Prescriptive Analytics

Prescriptive analytics offers the ability to understand past and present data and make data-driven decisions for the future, improving operational efficiency and profitability.

By providing actionable recommendations based on actionable AI and optimizing decision-making processes, it helps to proactively address threats and seize opportunities in a rapidly evolving business landscape.

However, running prescriptive analytics presents a number of challenges, and overcoming them requires a holistic approach involving technology, processes, and people. Collaboration between data specialists, experts, and managers is essential to successfully implement and benefit from prescriptive analytics solutions.

Use cases Prescriptive Analytics

Some of the most common cases of prescriptive analysis include:

• Customized pricing: statistical analysis of user preferences helps to assign weightings that reflect business priorities, as well as maximize margins and customize pricing.
• Portfolio optimization: Evaluating performance correlations between asset classes and incorporating targets, constraints, and demand leads to mathematically constructed baskets that balance stability, income, and growth to meet customized needs.
• Supplier selection: complex algorithms that balance decision variables such as cost, quality, lead times, and location preferences suggest optimal supply chain partners that ensure timely logistics.

Challenges and Solutions for Prescriptive Analytics

Running prescriptive analytics presents a number of challenges, and overcoming them requires a holistic approach involving technology, processes and people. According to Qlik, these are some of the most important ones:

• Data quality and availability: ensuring the accuracy and accessibility of data can be a major obstacle, so investing in governance and quality processes to ensure data accuracy and integrity will be the best solution.
  • Implement data integration and data warehousing solutions.
  • Use data cleansing and validation.
• Security and privacy: Protecting sensitive data and ensuring compliance with privacy regulations while using prescriptive analytics is crucial to avoid breaches and legal issues. Therefore, it is best to implement strong encryption and access controls to protect sensitive data.
  • Comply with GDPR and conduct regular audits to verify compliance.
  • Educate employees on data privacy and security best practices.
• Integration: integrating analytics into an organization’s existing systems and processes can be a difficult and important task. The solution could be to develop APIs and connectors to seamlessly integrate analytics into existing systems.
  • Collaborate with IT teams to ensure that data flows smoothly between systems.
  • Consider adopting an enterprise-wide data integration strategy.
• Complex modeling: Developing accurate models can be complex and require advanced mathematical algorithms and techniques. The key is to employ skilled analysts who are proficient in advanced modeling techniques in areas requiring high sophistication.
  • Leverage automated ML tools to simplify model development for a broader set of less complex use cases.
  • Collaborate with subject matter experts to develop specialized models where necessary.
• Computational resources: solving complex optimization problems in real-time can require considerable computing power and resources.
  • Explore cloud services that can provide scalability.
  • Optimise algorithms and models to reduce computational requirements.
  • Consider distributed computing frameworks to handle large-scale optimization problems.
• Explainability: It is very important to make the results of prescriptive analysis understandable and actionable, although this is a complicated task. Therefore, the key will be to use explainable AI to make the results more understandable, showing what and why.
  • Provide clear explanations and visualizations of recommendations.
  • Encourage collaboration between data scientists and experts.
• Uncertainty and variability: It is essential to account for uncertainty and variability in data to make better decisions and achieve robust prescriptive solutions.
  • Incorporate uncertainty quantification methods into models to account for variability.
  • Conduct sensitivity analyses to understand how changes in data affect recommendations.
  • Develop contingency plans to deal with unexpected outcomes.
• Change management: Implementing prescriptive recommendations may require changes to organizational processes and culture, which may be resisted. The key is to create a change management plan that includes communication, training, and support for affected teams.
  • Involve key stakeholders early in the decision-making process.
  • Monitor and address resistance to change and adapt plans.
• Ethical considerations: Balancing commercial objectives with ethical and social concerns in decision-making can be a complex challenge, but establishing guidelines to ensure that models conform to these principles will be a key pillar.
  • Conduct ethical audits to identify potential biases.
  • Use experts to address complex ethical issues.
• Scalability: Another challenge is to extend analytics solutions to handle large data sets and operations.
  • Choose scalable cloud technologies and architectures that can handle increasing data volumes and user demands.
  • Continuously monitor system performance and adjust resources accordingly.
• Real-time decision-making: Some applications require rapid decision-making, which requires real-time processing capabilities.
  • Use real-time data processing technologies such as stream processing and databases.
  • Pre-calculate and store possible prescriptive scenarios to accelerate decision-making.
  • Develop algorithms that can provide near real-time recommendations.
• Continuous improvement: Models must adapt to changing conditions and data and therefore require continuous maintenance and improvement.
  • Establish a feedback loop to monitor model performance and capture changing conditions.
  • Update and retrain models periodically to maintain accuracy.
  • Implement a model lifecycle management process to track and manage model releases.

Differences between predictive and prescriptive analytics

We have already discussed them separately, and although they are used interchangeably, predictive and prescriptive analytics are distinct disciplines that play complementary roles in enriching the decision-making process.

The main differences can be listed in five:

• Techniques used: While both leverage statistical modeling, data mining, and ML, prescriptive modeling employs additional mathematical methods such as linear programming, simulation, and optimization algorithms to derive ideal actions.
• Data dependency: Predictive analytics relies on large historical data sets that reveal influential trends and propensities. Prescriptive augments these data inputs with constraints, business rules, and contextual metrics that guide decisions.
• Analytical approach: Predictive deduces probabilities, but does not directly optimize business metrics. Prescriptive uses target variables such as revenue, risk, or resource utilization to model scenarios that balance trade-offs.
• Output type: Predictive techniques generate probabilities in the form of forecasts, ranges, or confidence intervals quantified by probabilities. Prescriptive recommendations do so deterministically as precise actions for implementation.
• Scope: Predictive offers broader and more open-ended knowledge about trends and propensities. Prescriptive provides specific, localized suggestions that can be customized to unique constraints.

Predictive and prescriptive analytics trends and forecasts

According to Report Prime, the global predictive and prescriptive analytics market is expected to grow from USD 12.3 billion in 2022 to USD 60.39 billion in 2023, at a CAGR of 22%.

This market spans various industries such as banking, healthcare, retail, manufacturing, etc., and its growing demand for accurate and real-time information to support decision-making processes is a major driver for the market’s revenue growth.

Key trends observed are:

• Adoption of AI and ML technologies: these enable the analysis of large volumes of data and the identification of patterns, correlations, and trends, thereby enhancing their predictive and prescriptive capabilities. In addition, the integration of predictive and prescriptive analytics with other emerging technologies such as the Internet of Things (IoT) and big data analytics is also contributing to the growth of the market.
• Integration of these two types of analytics: companies are now integrating these two analytical techniques to predict future outcomes and get actionable recommendations on how to optimize those outcomes.
• Increasing demand for real-time analytics: With the growing need for rapid decision-making, there is a shift towards real-time analytics capabilities in analytics tools.
• Growth of cloud-based analytics solutions: these are becoming popular due to their scalability, flexibility, and cost-effectiveness, allowing businesses to easily access and use advanced analytics.
• Focus on personalized analytics: Companies are focusing on personalized analytics to deliver personalized recommendations to customers, improving customer satisfaction and loyalty.

Disruptive product launches, such as AI-based predictive analytics tools or industry-specific solutions, can also drive market growth by offering unique capabilities and addressing niche market needs.

To ride this wave of advanced analytics, one of the best options is to rely on an experienced partner to implement a plan that best fits your needs. At Plain Concepts we focus on getting you a data-driven strategy, solving technological, cultural, and organizational challenges.

Our advanced analytics services will help you unleash the full potential of your data and turn it into actionable information, identifying patterns and trends that can shape your decisions and drive your business forward.

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