Prescriptive Analytics: The Path to Proactive Decision-Making

In this data age, working with data is key for businesses to plan and deliver their services in time, but this process has been cumbersome and costly to users. However, prescriptive analytics intervention has enabled smooth transactions in the process, enabling businesses to stay abreast with market changes and developments. This article will provide insights into prescriptive analytics and reveal its best practices and techniques, facilitating businesses to make more informed decisions.

What is prescriptive analytics?

Prescriptive analytics involves employing sophisticated methods and instruments to scrutinize data and content with the aim of suggesting the best possible plan of action or strategy for future progress.

Types of data analytics

Data analytics is the process of examining raw data to find meaningful insights and draw conclusions about the information they contain. Let’s see what each type of data analytics entails.

How prescriptive analytics works

Prescriptive analytics operates as follows:

• Define the Objective: Clearly establish the specific decision or issue you wish to tackle using prescriptive analytics, such as maximizing pricing strategies or resource distribution.
• Consolidate Your Data: Assemble and unify pertinent data from diverse sources, ensuring you have a thorough and precise dataset for analysis.
• Construct Your Model: Develop a robust algorithm that amalgamates historical data, current variables, and potential outcomes to create possible solutions or suggestions.
• Implement Your Model: Incorporate the algorithm within your company’s systems to analyze real-time data and yield actionable insights as new data is gathered.

Benefits of prescriptive analytics

Utilizing prescriptive analytics in your organization can lead to:

• Enhanced Operational Efficiency: Through the identification and examination of data patterns, businesses can elevate operational efficiency, yielding cost savings and heightened productivity.
• Elevated Customer Experience: Prescriptive analytics fosters a deeper understanding of customer behaviors, leading to elevated customer service and heightened satisfaction levels.
• Effective Risk Management: By utilizing prescriptive analytics, businesses can detect potential risks and devise strategies to mitigate them, safeguarding against losses and fortifying overall resilience.
• Revenue Growth: Prescriptive analytics spotlights growth opportunities, enabling businesses to amplify revenue streams. This could involve unearthing untapped markets, refining product offerings, or optimizing pricing structures.
• Competitive Edge: Enterprises employing prescriptive analytics gain a competitive edge through informed decisions and swift adaptability to market shifts, fostering agility and strategic advantage.

Industrial real-time use case of prescriptive analytics

Prescriptive analytics is applied across various industries; get to know some of its applications:

Marketing

In marketing, real-time prescriptive analytics enhances customer interaction and experience. It analyzes purchase history, online behavior, and demographics, hence providing personalized recommendations. Subsequently, these can help the marketing team devise strategies and make decisions that can maximize sales, enhance client retention, and promote customer engagement. For instance, if a customer frequently buys a particular product, the system may recommend offering a discount on that product to that customer.

Manufacturing

Prescriptive analytics in manufacturing industries is being used for predictive maintenance. Real-time data derived from machine sensors helps identify possible breakdowns or malfunctions before they happen. This allows timely maintenance and prevents costly downtimes, optimizing work processes and boosting productivity. For example, by understanding the optimal conditions for its equipment, a manufacturer could increase output and reduce costs.

Finance

The finance industry uses real-time prescriptive analytics to mitigate risk and fraud, improve financial planning, and enhance decision-making processes. It aids in making portfolio investment decisions by suggesting the best combination of investments to maximize returns and minimize risk. For example, a bank can identify which loans are likely to default and make decisions accordingly.

Supply chain

In the supply chain industry, prescriptive analytics helps in improving operations and decision-making. It uses real-time data to suggest preventative actions. This helps in maintaining the uninterrupted flow of goods, mitigating risks, and improving overall operational efficiency. For instance, during a weather disruption, real-time analytics can allow for rerouting, rescheduling, or changing suppliers to avoid delays or gaps in the supply chain.

Healthcare

In the healthcare industry, prescriptive analytics is used for medical diagnosis and treatment, patient care management, resource allocation, and clinical research. It helps hospitals in optimizing resource utilization by analyzing patient inflow and suggesting the best allocation of resources. For example, an AI system could use patient data to identify health risks and suggest individualized treatment plans.

Challenges of prescriptive analytics

Here are some of the challenges faced while working with prescriptive analytics:

• Understanding the difference: The blurry boundary between prescriptive and predictive analytics creates a challenge in developing effective strategies and techniques.
• Dependence on data quality for reliable insights: The precision of prescriptive analytics is largely determined by the quality of input data. Poor or inaccurate data can lead to erroneous recommendations, undermining the process of making a conclusion.
• Risks associated with automated decisions: In situations where decisions are automated, there’s a potential for algorithms to make inappropriate choices due to unexpected circumstances.
• The requirement for intensive supervision in prescriptive analytics: Implementing prescriptive analytics requires constant monitoring from skilled analysts with expertise in machine learning, which could be both time-consuming and costly.
• Complexity: Prescriptive analytics involves complex algorithms and models. It requires a high level of expertise to develop and interpret these models.
• Difficulty in predicting the future: While prescriptive analytics can suggest possible outcomes, it cannot guarantee them because the future is inherently unpredictable.
• Integration challenges: Integrating prescriptive analytics into existing systems and processes can be challenging.

Using business intelligence tools to overcome prescriptive analytics challenges

Using business intelligence tools can help your organization overcome various prescriptive analytics challenges in the following ways:

• Pre-built analytical models: BI tools with advanced analytical capabilities come with premade algorithms and models, making prescriptive analytics less complex. Non-technical individuals can utilize these tools for complex analysis and straightforward interpretation of recommendations.
• High scalability and efficient performance: BI tools are designed to manage large datasets and provide scalable solutions. They support the high computational needs of prescriptive analytics with necessary computing power and resources.
• Real-time data processing: BI tools with real-time analytics allow businesses to make a judgement and react fast to market developments based on up-to-date information.
• Scenario analysis: BI tools facilitate “what-if” scenario analysis, which effectively tackles the challenge of uncertainty, allowing organizations to prepare for various possibilities.
• Visualization: BI tools can present complex data and analytical results in a visual, easy-to-understand format. This can help decision-makers understand the implications of different actions and make better decisions.
• Automated reporting: Automated reporting features ensure the latest prescriptive insights are readily available to decision-makers.

Now, let’s see how utilizing Bold BI for prescriptive analytics is essential.

How Bold BI supports prescriptive analytics

Bold BI, as a business intelligence tool, supports prescriptive analytics through its robust features and functionalities. Whereas prescriptive analytics is a form of advanced analytics characterized by techniques such as graph analysis and complex event processing, Bold BI’s data visualization enables users to produce interactive reports and dashboards, presenting difficult information in a clear and accessible format. The collaboration feature of Bold BI allows for the seamless sharing of insights and ideas among team members.

Additionally, Bold BI’s predictive analytics feature is directly tied to prescriptive analytics, as it uses historical and current data to forecast future events. These predictions can form the basis for prescriptive actions. Each of these features enhances the prescriptive analytics process that enables companies to make data-driven decisions that can optimize their operations and drive growth.

In conclusion, leveraging Bold BI’s prescriptive analytics capabilities enables organizations to not only predict future outcomes but also get recommendations on the best course of action to achieve their business objectives. This not only enhances operational efficiency but also increases profitability, making Bold BI a vital tool for any data-driven organization.

Originally published at https://www.boldbi.com on September 13, 2023.