The fundamentals of predictive analytics

Like all forms of data analytics, predictive analytics rests on a foundation of massive datasets. Three of the five Vs of big data—volume, variety, and veracity—apply here, as it's not feasible for even a cutting-edge analytics engine to generate realistic predictions without a great deal of information as its baseline. The data in some of these sets can be weeks, months, years, or decades old; other sets may include key performance indicators (KPIs) generated as recently as several hours ago.

Parsing these vast swaths of data requires the careful use of various mathematical, statistical, and data science techniques. Some of these principles, like linear and logistic regression analysis, are based on centuries-old—but still sound—mathematics, while others, like decision tree modeling and data mining, are considerably more modern. Coupled with parameters—variables that address the entirety of specific categories under examination within data sets—these methods help pinpoint patterns to predict occurrences and situations that will likely occur. Sometimes—though not always—predictive insights include roughly specified times at which various future outcomes will unfold.

In a way, predictive analytics is a fascinating fusion of the classical and the cutting edge—primarily because it wouldn't be possible without state-of-the-art technologies. This stems most obviously from the sheer volume of data, which is too great for human data scientists and analysts to contend with manually. But the pace, efficiency, and efficacy that even smaller enterprises require to gain actionable insights and tangible business value from analytics also can't be left to human manual analysis. Database technologies and business intelligence (BI) software solutions were among the earliest tools used to help automate and streamline data analysis at scale, and these days, few tech trends have greater influence on analytics than artificial intelligence (AI) and machine learning (ML).

Predictive vs. prescriptive analytics

Predictive analytics and prescriptive analytics are often compared, but they serve different roles within a broader analytics strategy. Predictive analytics focuses on estimating what is likely to happen in the future by analyzing historical data and identifying patterns. Prescriptive analytics builds on those predictions by recommending specific actions an organization should take in response.

In practice, prescriptive analytics depends on predictive models as one of its key inputs. It combines those forecasts with optimization techniques, mathematical modeling, business rules, and often machine learning algorithms to evaluate possible decisions. The result is guidance on how to achieve a desired outcome, whether that means maximizing revenue, reducing risk, improving operational efficiency, or meeting regulatory constraints.

Rather than replacing predictive analytics, prescriptive analytics extends it. Predictive models highlight potential outcomes and probabilities, while prescriptive analytics helps determine the best course of action given those projected scenarios.

Dimension	Predictive Analytics	Prescriptive Analytics
Core question answered	What is likely to happen?	What should we do about it?
Primary goal	Forecast future outcomes based on patterns in historical data	Recommend optimal actions to achieve desired results
Key inputs	Historical data, statistical models, machine learning predictions	Predictive model outputs, business rules, constraints, optimization models
Typical techniques	Regression, classification, time-series forecasting, machine learning models	Optimization algorithms, simulation models, decision analysis, rule-based systems
Output	Probabilities, risk scores, forecasts, predicted behaviors	Recommended actions, decision paths, optimal resource allocations
Example use case	Predict which customers are likely to churn	Recommend the best retention offer for each at-risk customer
Role in analytics maturity	Identifies likely future scenarios	Determines best actions within those scenarios

 

Major benefits of predictive analytics

Reliability and accuracy set modern predictive analytics apart from past methods used to forecast sales, inventory, scheduling, occupancy, revenue, and a wide variety of other critical business KPIs. Predictive analysis is especially useful for helping companies find and take advantage of patterns within data—whether to highlight risks and opportunities, examine relationships with third-party partners, or optimize supply chain management.

The heart of decision-making

Arguably, the most important benefit of predictive analytics—regardless of specific application—is its potential to majorly improve enterprise decision-making.

Company leaders and the heads of various business units can't rely solely on direct knowledge, training, and intuition to make the right calls in critical situations—or small ones that can snowball into larger issues. These turning points may affect any or all of the following: bottom-line margins, jobs, key business operations, the customer experience—and even, in fields like healthcare, patients' lives. Only with the foundation of sound data, experienced data professionals, and a predictive analytics solution capable of leveraging past events to envision future possibilities can enterprise-scale organizations enable consistent, reliably strong decision-making.

Other noteworthy benefits of predictive analysis are tied to decision-making in one way or another. Risk mitigation, demand forecasting, and predictive maintenance all exemplify this fact.

• A large enterprise using a predictive analytics model trained and configured to analyze the soundness of acquiring a subsidiary company knows the precise level of risk it faces if the model projects that the startup's bad fundamentals will overcome its promising R&D.
• A retailer with projections of strengthening consumer confidence understands that it may need to increase inventory but that it can raise prices on certain goods.
• A manufacturer using a supervised machine learning model to assess equipment health can plan to repair or replace any individual device that shows signs of trouble.

Ultimately, human choice—likely an alliance of key stakeholders—will determine how each of these organizations proceed from the findings of their models. But regardless of outcome, predictive analytics gave enterprise leaders the support they need to make via actionable insights in all of these situations.

Predictive analytics modeling use cases

Predictive analytics is widely used across industries because it allows organizations to anticipate behavior, demand, and risk before events occur. By combining historical data with machine learning models and statistical techniques, organizations can uncover patterns that inform marketing strategies, operational planning, customer engagement, and risk management.

Different industries apply predictive models in different ways depending on the data available and the outcomes they need to forecast. Marketing teams often focus on customer behavior and campaign performance. Healthcare providers analyze patient data to identify risk and improve care decisions. Travel and hospitality companies forecast demand and pricing opportunities, while financial institutions and insurers rely on predictive modeling to detect suspicious activity and reduce fraud.

The following examples illustrate how predictive analytics models are commonly applied across industries.

Common predictive analytics use cases by industry

Industry / Function	Predictive Analytics Use Case	Data Analyzed	Business Value
Marketing and sales	Customer segmentation, churn prediction, and campaign targeting	Purchase history, customer demographics, engagement data, support interactions	More personalized campaigns, improved conversion rates, stronger customer retention
Healthcare	Patient risk prediction and care optimization	Patient records, treatment history, diagnostic results, lifestyle indicators	Earlier intervention, improved patient outcomes, more efficient resource allocation
Travel and hospitality	Demand forecasting and dynamic pricing	Booking patterns, travel behavior, seasonal trends, market demand data	Optimized pricing strategies, higher occupancy or booking rates, improved customer experience
Financial services and insurance	Fraud detection and risk monitoring	Transaction data, device information, geolocation, behavioral patterns	Faster fraud detection, reduced financial losses, improved regulatory compliance
Retail and e-commerce	Product recommendation and demand forecasting	Purchase behavior, browsing history, inventory levels, seasonal patterns	Higher average order value, improved inventory planning, better customer engagement
Operations and supply chain	Predictive maintenance and demand forecasting	Equipment sensor data, production metrics, supply chain data	Reduced downtime, optimized maintenance schedules, improved supply planning

The importance of machine learning in predictive analysis

While forms of automation and AI have been a part of analytics since fairly early in the discipline's history, the rise of machine learning is nothing short of an analytics game-changer.

Because ML models constantly grow more effective by ingesting new data and being periodically retrained to improve their performance, they make analytics more dynamic than ever. Also, due to ML's enablement of natural language processing (NLP), a business user doesn't have to be a data scientist or analyst to input queries: They can type—or, often as not, use voice recognition—to submit data requests in sentences that they'd regularly type or speak, instead of in snippets of Python or R.

Furthermore, within the ML subcategory of deep learning, predictive analysis using neural networks becomes sophisticated enough to be valuable in complex fields like oncology and pharmaceuticals: For example, a convolutional neural network (CNN) can accurately classify skin cancer with the veracity of a dermatologist, while a deep feed-forward neural network (D-FFNN) can predict drug toxicity more effectively than other deep learning models. Although deep learning requires a great deal of computing power and the technology is still in development, the evidence for its potential thus far is quite promising.

Operationalizing predictive analytics

Making predictive analytics work for your organization starts with envisioning a clear objective: You must have at least one intended application for this practice in mind, or it will become a resource drain. It can be relatively straightforward to start—e.g., conducting sentiment analysis regarding a particular product to gauge the likelihood of customers buying similar goods in the future. You can move on to other use cases if this first one is a success.

Next is data collection and preparation, which entails not only identifying every relevant data set but also filtering out redundant or noisy data that will skew results. Model development follows—devising appropriate algorithms and pairing them to the data you wish to predictively analyze. Then comes training, in which the model "learns" the data inside and out, which will eventually enable it to make predictions. Various assessments test the accuracy of the model's initial predictions, and recoding or other tweaks take place as needed. Finally, it's deployed in its working environment—and brought back for retraining and maintenance when necessary.

Carrying all of this out, of course, requires strong enterprise data architecture, dedicated data pipelines, effective data warehousing, object stores to enable data lakes for unstructured data, and more. You'll also need a foundation of cutting-edge technology—an effective cloud deployment, AI/ML solutions, and a powerful data platform.

Maximize predictive analytics' business value with VantageCloud

Teradata VantageCloud enables your enterprise to operationalize predictive analytics in the manner most valuable to the business. The complete cloud and data analytics platform integrates and streamlines data from disparate sources across your enterprise so it's easily accessible to predictive models. With its AI/ML-driven ClearScape Analytics^TM engine, VantageCloud can help launch numerous predictive models quickly and support analytics operations that generate actionable insights and form the basis of sound strategy.

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