Harnessing the Decision-Making Power of Prescriptive Analytics
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.
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 Unlike earlier times when data was scarce and often outdated, today’s digital age sees a constant stream of real-time, diverse data.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.
