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TDWI Upside - Where Data Means Business

Harnessing the Decision-Making Power of Prescriptive Analytics

Exploring the transformative power of data-driven decision-making and seven steps to help you implement prescriptive analytics.

Once an obscure field known only to specialized mathematicians and data analysts, prescriptive analytics has become a cornerstone of modern business strategy. From workforce scheduling and portfolio optimization to supply chain design and everything in between, prescriptive analytics is at work across virtually every industry.

For Further Reading:

Improve Your Business by Leading the Transformation to Prescriptive Analytics

The Shortcomings of Predictive Analytics 

5 Skills You Need to Build Predictive Analytics Models

Whereas predictive analytics tells us what might happen by identifying patterns in data to forecast future events, prescriptive analytics goes a step further by using mathematical modeling to provide actionable recommendations that can help us reach a specific goal.

This prescriptive analytics boom has been driven by three pivotal factors: the explosion of data availability, advancements in computational power and affordability, and significant improvements in algorithmic approaches.

Data availability. In the past two decades, in particular, the surge in data generation and collection has been unprecedented. Unlike earlier times when data was scarce and often outdated, today's digital age sees a constant stream of real-time, diverse data. This abundance provides a rich bedrock upon which prescriptive analytics can thrive, offering deeper insights and more accurate predictions.

Computational speed and affordability. The advent of faster, more affordable computing has been a game-changer. Earlier, the computational cost and time required to process complex data were prohibitive. Now, even small businesses can harness powerful computing resources thanks to cloud computing, enabling them to solve optimization problems that were once the exclusive domain of large corporations.

Algorithmic advancements. Algorithms are the heart of prescriptive analytics. Over the years, there has been a significant leap in the sophistication of these algorithms. From linear to nonlinear optimization, and from deterministic to stochastic models, the advancements have broadened the scope and applicability of prescriptive analytics, making it more efficient and versatile.

Case Studies: Uber and Amazon's Secret Weapon

In the realm of prescriptive analytics, the stories of Uber and Amazon stand out not just for their use of technology but for how they revolutionized their industries -- transit and retail, respectively. By delving deeper into these case studies, we can better understand the transformative power of prescriptive analytics.

Uber Redefines Urban Mobility

Uber's journey is a testament to the power of prescriptive analytics in transforming the transit industry. Before Uber, the traditional taxi system was often inefficient and inconvenient. Passengers had to physically locate a taxi or call a dispatch center, with no guarantee of -- or even insight into how to achieve -- quick service. Uber revolutionized this experience by leveraging prescriptive analytics in several key ways:

  • Dynamic ride-matching. Uber's core functionality is its ability to match drivers with riders in real-time. This system uses a complex algorithm that considers current traffic conditions, the proximity of drivers to riders, and historical data on ride requests to prescribe optimal routes that reduce wait times, leading to a more efficient experience for passengers and drivers.
  • Surge pricing model. Uber employs a surge pricing algorithm that uses prescriptive analytics to adjust fares in real time based on supply and demand. During periods of high demand, prices increase to encourage more drivers to enter the area. This dynamic pricing model ensures a balance between ride availability and rider demand.
  • Driver and rider behavior analysis. Say there’s a big concert or football game in town. With predictive analytics, Uber can analyze historical data and predict the demand for rides given the event, then use prescriptive analytics to recommend the best positioning for drivers, in areas where they are most likely to find passengers. This reduces idle time and increases earnings for drivers. 

Amazon Revolutionizes Retail

Amazon's use of prescriptive analytics has set a new standard in the retail industry. Before Amazon, consumers were limited to the inventory of local stores, and obtaining uncommon items often involved lengthy special orders. Amazon changed this landscape through:

  • Optimized inventory management. Amazon uses prescriptive analytics to manage its vast inventory. By analyzing purchasing patterns, regional preferences, and shipping logistics, Amazon can predict which products are likely to be in high demand in different areas, and then use that insight to recommend optimal inventory levels, reducing delivery times and costs.
  • Global fulfillment network. Amazon's global network of fulfillment centers is strategically located based on prescriptive analytics. This network ensures that even the most unusual items are within reach, often deliverable within a day or two.
  • Customer experience enhancement. Amazon's recommendation engine is another example of prescriptive analytics at work. By analyzing past purchases, search history, and customer preferences, Amazon can recommend products that are more likely to be of interest to individual customers, enhancing the shopping experience and increasing sales.

Navigating the Path to Data-Driven Decision-Making

For Further Reading:

Improve Your Business by Leading the Transformation to Prescriptive Analytics

The Shortcomings of Predictive Analytics 

5 Skills You Need to Build Predictive Analytics Models

Implementing prescriptive analytics in your own business can seem daunting, but by following a strategic, step-by-step approach, your organization can unlock its transformative potential. This journey involves careful planning, resource allocation, and a clear understanding of your business objectives and constraints. The following road map can guide you through the process.

Step 1: Define clear objectives and scope

Begin by identifying the specific business problems or opportunities where prescriptive analytics can have the most impact. Clearly define your objectives, such as minimizing costs, minimizing waste, or maximizing resource usage.

Step 2: Gather and prepare your data

The foundation of any analytics project is data. Collect relevant data from various sources within your organization, ensuring it is accurate and up to date. This may include sales figures, customer interactions, inventory levels, or logistical information. You can then also add external data, such as weather, economic information, or other relevant factors as needed. Data preparation is critical -- cleaning, normalizing, and structuring your data to make it suitable for analysis.

Step 3: Build or acquire the right tools and technology

Choose the tools and technology that best fit your needs. This could involve building custom solutions, purchasing off-the-shelf software, or using cloud-based platforms. Consider factors such as scalability, ease of use, and integration with existing systems. The choice of technology should support the complexity of your analytics tasks and your team's expertise.

Step 4: Develop or hire analytics expertise

Prescriptive analytics requires a certain level of expertise in areas like operations research and mathematical optimization. Depending on your organization's capabilities, you may need to train existing staff, hire new talent, or partner with external experts. Investing in the right expertise is crucial for effectively implementing and interpreting prescriptive analytics.

Step 5: Create models and run simulations

Develop mathematical models that reflect your business processes and objectives. These models should incorporate your decision variables, constraints, and goals. Running simulations can help you validate the models’ performance under various scenarios and refine them for accuracy and effectiveness.

Step 6: Integrate prescriptive analytics into your decision-making processes

The ultimate goal of prescriptive analytics is to inform and enhance decision-making. Integrate the insights and recommendations from your models into your business processes. This might involve automating certain decisions or providing decision-support tools for your managers and executives.

Step 7: Monitor and continuously improve your prescriptive analytics

Implementing prescriptive analytics is not a one-time effort. Regularly monitor the performance of your models and gather feedback from users. Use this information to refine and improve your models and processes continually. Stay abreast of new data sources, algorithmic advancements, and technological developments that can enhance your analytics capabilities.

Embrace the Power of Optimized Decision-Making

By following these seven steps, you can effectively implement prescriptive analytics and harness its power for optimized decision-making. This requires a blend of strategic planning, technological investment, and analytics expertise. With these elements in place, your organization can unlock the full potential of prescriptive analytics, driving efficiency, innovation, and competitive advantage in today's data-driven world.

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