A Guide to Choosing the Best Predictive Analytics Platform

Predictive analytics has emerged as one of the most transformational business innovations of the past decade. Powered by statistical modeling and machine learning, predictive analytics enables companies to foresee likely future outcomes and events by learning from historical data patterns and trends.

Predictive insights help organizations prepare optimized plans, allocate resources intelligently, and capitalize on growth opportunities. According to IDC, the predictive analytics software market is forecasted to grow from $7.3 billion in 2020 to over $23 billion by 2025.

However, with the explosion of technologies across the machine learning and artificial intelligence landscape, selecting the right predictive analytics platform for your needs can be challenging.

This comprehensive guide examines the key capabilities to evaluate and presents an unbiased overview of leading solutions complimented by advisory best practices for implementation. Let‘s get started!

An Introduction to Predictive Modeling Approaches

Predictive modeling encompasses an array of statistical and machine learning algorithms for forecasting continuous numerical outcomes or classifying categorical outcomes based on historical precedent in comparable situations.

Predictive Modeling Algorithms

• Linear/Logistic Regression – Estimate relationships between independent variables/features and outcomes
• Time Series – Forecast numerical trends based on past periods
• Decision Trees – Segment data and define logic flows to classify data points and predict values
• Neural Networks – Map complex nonlinear feature combinations to outputs through layered nodes
• Ensembles – Combine multiple models to improve overall predictive performance

Because each dataset and business problem has unique properties, leveraging a diverse set of modeling techniques is key to optimal predictive performance. Blending complementary models together into ensemble models can yield even greater accuracy.

The automated machine learning capabilities in modern predictive analytics platforms streamline exploration across multiple algorithms to determine the optimally performing modeling pipelines tailored to your data environment.

The Model Development Workflow

While models may differ mathematically, the workflows for developing and assessing predictive models are consistent:

1. Data Acquisition – Compile relevant historical data on the outcomes to predict along with corresponding input drivers
2. Data Preparation – Clean, transform, filter, and shape data into analysis-ready structures
3. Feature Engineering – Derive aggregate, lagged, exponential, interaction etc. features that better inform models
4. Model Training – Choose algorithms to analyze feature relationships with outcomes
5. Model Evaluation – Quantitatively assess model performance on holdout test data
6. Interpretability – Determine and visualize key drivers of model outputs
7. Operationalization – Embed model predictions into business systems and processes
8. Monitoring – Continually assess model performance for deterioration

Leading predictive analytics platforms offer capabilities spanning this entire machine learning operations (MLOps) workflow – from data to deployment.

Now let‘s explore the top solutions in the market and key considerations for selection.

Choosing the Right Platform for Your Business

With the surging demand for predictive insights across industries, the landscape of commercial analytics tools continues to grow increasingly crowded. Here are key evaluation criteria to identify the right solution:

1. Types of Algorithms and Models

Verify that prospective tools offer a diverse library encompassing regression, time series forecasting, decision trees, neural networks, ensembles, and multivariate models for structured and unstructured data.

While some solutions specialize in specific techniques like time series analysis,Preference options with versatility across use cases. Also consider BYOD support for advanced custom modeling.

2. Automated Machine Learning Functionality

To empower business analysts without extensive data science expertise, confirm platforms provide autoML capabilities like:

• Automated pipeline optimizations
• Hyperparameter tuning
• Feature engineering
• Model selection, blending and stacking
• Cloud compute scalability

AutoML accelerates development and enhances accessibility for scalable enterprise adoption.

3. Model Monitoring and Re-Training Provisions

Live models decay in accuracy over time as populations and behaviors evolve. Seek solutions with embedded data and model monitoring tools that trigger alerts and guided re-training flows when performance drift is detected.

4. Security and Regulatory Compliance

Data privacy and model IP protection are paramount, especially in regulated industries like healthcare and financial services. Evaluation governance, access controls, encryption, and cybersecurity provisions.

5. Flexible Operationalization Options

The utility of predictive insights is realized when embedded into workflows and processes. Expect platforms to offer a range of APIs, database integrations, connectors to applications, and custom code execution runtimes for flexible model deployment.

6. Intuitive Interface and Collaboration Features

To spur enterprise-wide adoption, analytics tools should offer visually intuitive, nonprogrammer interfaces with guided analytics journeys, smart recommendations, and collaboration features like sharable project libraries.

Now let‘s highlight the leading commercial platforms addressing these predictive modeling requirements:

1. Alteryx Designer: Democratized Predictive Modeling

Alteryx Designer provides an end-to-end analytic workflow to operationalize predictive modeling without coding requirements. The interactive graphical interface accelerates data integration, preparation, feature derivation, model training, evaluation, and deployment.

Alteryx instantly connects to 140+ data sources then performs cleaning, shaping, filtering, and sampling through an intuitive data preparation workflow. The software provides over 300 drag-and-drop building blocks for deriving value-added features like aggregates, ratios, lags, and exponentials tailored to predictive modeling.

The Analytic Library natively integrates the R and Python open-source ecosystems, empowering access to cutting-edge open-source algorithms without programming. Users can leverage regression, forecasting, decision trees, ensembles, deep learning models, and multivariate techniques through point-and-click configuration and comparison.

Automation capabilities like Analytic Pipelines, Hyperparameters Tuning, and Promote AutoML expedite the search for optimally performing modeling pipelines tailored to business goals. Embedded performance metrics and diagnostic visualizations inform iterative improvements while Model Comparison Charts help determine production-ready champions.

With the ability to readily embed predictive model outputs into databases, applications, and BI tools using APIs, Alteryx customers achieve ROI through analytically-driven decisions and experiences.

• Use Cases: Customer lifetime value predictions, targeted marketing, predictive maintenance, demand forecasting

• Customers: Experian, Ford, United Airlines

• Pricing: Annual subscription fees starting at $4,760 per designer

2. SAS Visual Forecasting: Automated Time Series Analytics

As a recognized leader in statistical modeling for over 40 years, SAS offers proven predictive analytics capabilities. SAS Visual Forecasting provides an automated, visually interactive environment specialized for time series forecasting using exponential smoothing models, ARIMA modeling, regressors, neural networks, ensembles, and more.

Through an intuitive point-and-click interface, users input their series of interest, historical date ranges, adjustments for holidays, missing observations, etc. then launch automated model identification and estimation procedures to evaluate hundreds of potential models. Built-in diagnostics spotlight potential improvements while several accuracy measures determine the best performing models for a given series and horizon.

The software supports multivariable forecasting through automated exploratory modeling to identify cross-series and temporal relationships, transformations, and rich reconstructions. Users can incorporate model recommendations or overwrite parameters to account for business knowledge. Results export via API or deploy natively into SAS solutions.

Recent enhancements like natural language time parsing, decomposition plots, model management, and integrations with planning systems demonstrate SAS’s continued commitment to evolving innovation. SAS Visual Forecasting provides proven, reliable automated time series analytics.

• Use Cases: Supply chain forecasting, financial budgeting, workforce planning, SaaS metrics predictions

• Customers: Driscoll’s, Tim Telecom Italia, Citibank

• Pricing: Available bundled into SAS subscription packages with custom quote pricing

3. DataRobot: End-to-End Enterprise AI Platform

DataRobot delivers a complete automated machine learning platform to accelerate predictive modeling and ensure models remain continuously effective over time. The end-to-end solution spans data access and preparation, feature engineering, machine learning feature store, automated modeling, interpretability, deployment infrastructure, and monitoring.

The Augmented ML (AML) capability combines automated modeling excellence with guidance from human subject matter experts. Users can direct DataRobot’s model development process by adjusting preferential weights, constraints, training durations, admin boundaries, etc. resulting in highly accurate custom models aligned to unique business environments.

In addition, DataRobot’s MLOps allow models to remain relevant through concept drift detection, automated monotonic constraints to prevent bias, and one-click retraining routines. With leading visionary recognition from top analysts, DataRobot drives cutting-edge innovation in scalable Enterprise AI solutions.

• Use Cases: Probability of close predictions, customer lifetime value, personalized product rankings

• Customers: Kaiser Permanente, Deloitte, LinkedIn

• Pricing: Quote-based packages tailored for enterprise scale

Evaluating and Comparing Platform Capabilities

With functionality spanning data connectivity, preparation, feature engineering, modeling, evaluation, deployment, and monitoring stages, leading platforms share considerable overlap – yet meaningful differences manifest.

Here is a high-level comparative analysis across key dimensions:

Platform	AutoML	Time Series	Interoperability	Embedded Monitoring	BYOD Options	Learning Resources
Alteryx	✅	✅	✅			✅
SAS	✅	Specialized	✅	✅		✅
DataRobot	✅	✅	✅	✅		✅
Qlik		✅	✅		✅	✅
RapidMiner	✅	✅	✅	✅	✅	✅

Interoperability reflects the ability to operationalize within surrounding data infrastructure while BYOD refers to abilities for advanced users to customize modeling code.

As evidenced above, Alteryx enables no-code predictive modeling accessibility while RapidMiner and DataRobot provide low-code options for extensibility. SAS dominates time series analytics with specialist precision.

部署模型驱动业务价值

Transitioning from theoretical models to tangible value requires embedding predictions, alongside key drivers and certainty estimates, into operational decisions and workflows.

This model operationalization stage involves:

• API Integration– Real-time retrieval of predictions from models behind APIs
• Business Rules – Mapping confidence thresholds to actions
• Conditional Logic – Incorporating predictions into segmentation schemes
• Alert Creation – Notifications based on predicted outcomes
• User Interfaces – Interactive dashboards for forecast visualization

Leading analytics platforms offer a range of APIs, database integrations, RPA connectors, Power Platform extensibility, etc. to fulfill customized deployment requirements. Microapp architectures that securely publish models to be invoked from numerous downstream environments provide scalability.

However, extracting value goes beyond infrastructure mechanics. The business application should aim to influence specific KPIs through analytically optimized interventions on targeted populations. Quantify the utility – Lives saved, days less inventory carrying costs, customers retained etc. – to validate ROI and inform continual enhancement.

Best Practices for Maximizing Predictive Performance

While modern solutions greatly simplify development, applying predictive modeling effectively involves both science and art. Here are research-backed best practices to consider:

• Leverage Cross-Validation techniques during evaluation to rigorously measure generalization, not overfit peculiars
• Allocate sizable holdout data for final testing – 15-30% partitions
• Handle class imbalance through up/down-sampling to prevent bias
• Counteract concept drift with continuous automated monitoring and scheduled retraining every 3-12 months
• Apply human-in-the-loop guidance to optimize automated pipelines
• Blend multiple Friendly models into Ensembles for improved robustness
• Quantify model value impacts on KPIs as the objective, not accuracy alone

The platforms profiled all facilitate following these vital performance optimization principles.

The Democratization of Predictive Power

Predictive modeling was historically a dark art – the exclusive domain of PhDs, but no longer! Modern automation, machine learning, and MLOps combine to offer business leaders and analysts of all backgrounds access to this transformative technology.

Predictive analytics shifts organizations from reactive to prescient strategic planning. Top platforms like Alteryx, DataRobot and SAS accelerate development and enable scalable value generation.

Previously cost and skill constrained, predictive insights are now a democratized engine fueling competitive separation and lucrative growth through analytically optimized business initiatives. The question now becomes not who CAN leverage predictive analytics, but who will be the last one to still decide and operate blindly?