Making critical business decisions can be extremely challenging. With so many complex factors to weigh, it‘s easy to become overwhelmed by uncertainty. This is where decision-making platforms come in – they leverage data, analytics, and AI to provide structure, insights, and confidence to the decision process.
In this comprehensive guide, we‘ll explore the top decision-making platforms available today and how they can transform the way your business evaluates options to drive better outcomes.
Table of Contents
- Evolution of Decision Platforms
- Key Platform Capabilities
- 12 Leading Providers Compared
- Implementation Best Practices
- Measuring Value and ROI
Evolution of Decision Intelligence Platforms
The market for decision intelligence platforms has rapidly innovated over the past decade by incorporating new technologies like machine learning, leveraging decision science research, and responding to emerging business needs.
In 2010, early options were built as extensions of business intelligence tools, offering basic visualization, reporting, and spreadsheet-based models tailored to specific decisions. They provided insights but little prescriptive guidance.
By 2015, increasing data volumes led platforms to focus more on automation, algorithms, and integrations to keep up. Custom modeling and simulation became more common but still required IT skills.
In 2022, AI-augmentation now powers most platforms. Turnkey machine learning delivers insights and recommendations without coding. Robust process workflows coordinate inputs across the business for aligned decisions vs one-off analysis.
The next era promises more comprehensive augmentation – weaving real-time guidance and feedback into the actual moment of decision. More autonomous systems will continually synthesize data to adapt over time vs just producing static recommendations. Prescriptive applications in verticals like healthcare, finance, and marketing will aim to customize key sub-processes that commonly resist traditional automation.
Key Capabilities of a Modern Decision Intelligence Platform
Decision platforms have evolved from basic reporting tools into sophisticated capabilities. Here are key elements to look for:
Intelligent frameworks simplifying and guiding users through key decision steps:
- Frame issue
- Gather inputs
- Model tradeoffs
- Weigh options
- Align on action
Analytics empowering data exploration from basic business intelligence to predictive modeling:
- Data connectivity and preparation
- Custom reporting
- Interactive visualization
- Simulation and forecasting
Augmentation systems applying automation, algorithms and AI to provide enhanced support:
- Surface insights from data not possible for humans
- Quantify risks
- Recommend ideal options
- Learn from past decisions
- Adapt over time
Collaboration enabling inclusive support across groups with different views:
- Context sharing
- Discussions
- Real-time feedback
- Consensus building
- Permissions and workflow
The most effective platforms tightly integrate these elements into a single solution. Purpose-built offerings concentrate the highest sophistication around a specific decision domain while enterprise tools offer broader customization for diverse use cases.
12 Leading Decision Platform Vendors Compared
Now let‘s explore 12 platforms, from targeted solutions up to full-scale enterprise decision hubs, and key selection criteria:
| 1000Minds | Cloverpop | Apres | FineReport | Loomio | Actico | GiniMachine | FlowForma | D-Sight | SAP Analytics Cloud | DecideAct | Optimax | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Use Case Focus | Decision Analysis | Collaboration | AI Automation | Reporting/BI | Inclusive Decisions | Flex Rules/Models | Credit Risk | Workflow Decisions | Risk Management | Integrated BI Stack | Guided Decisions | Strategy Decisions |
| Ease of Use | Moderate | High | Moderate | Moderate | High | Low | High | High | Low | Low | High | Moderate |
| Data Connectivity | Low | High | High | High | Low | High | Moderate | Low | High | High | High | Moderate |
| Collaboration | Low | High | Moderate | Moderate | High | Low | Low | Moderate | Moderate | Moderate | High | High |
| Customization | Low | Moderate | High | Moderate | Low | High | Low | Low | High | High | Moderate | High |
| AI Capabilities | Low | High | High | Low | Low | Moderate | High | Low | High | Moderate | High | Moderate |
| Typical Price Range | $20k+ | $15/user/mo | Custom | $700/mo | $10/user/mo | $40k+ | Custom | $15/user/mo | Custom | $15+/user/mo | Custom | Custom |
Decision Analysis: 1000Minds
1000Minds is an expert in value-focused decision analysis. Their methodology quantifies the relative importance of needs, wants and preferences to recommend optimal choices.
Pros
- Unique decision modeling approach
- Unrivaled context behind recommendations
Cons
- Rigid methodology less customizable
- Very expensive
Collaboration: Cloverpop
Cloverpop is an intuitive, collaborative decision-making platform combining data, human insights, and AI guidance.
Pros
- Very user-friendly interface
- Strong data connectivity and visualization
Cons
- Can get pricey for larger organizations
- Steep learning curve for advanced analytics
AI Automation: Apres
Apres leverages AI and machine learning to build autonomous decision-making models tailored to your business goals.
Pros
- AI provides unique continuous learning approach
- Automates recurring decisions
Cons
- Significant upfront effort to build effective models
- Less flexible group collaboration features
Reporting/BI: FineReport
FineReport excels in centralized BI and reporting with decision-making bolted on top.
Pros
- Excellent embedded BI capabilities
- Very scalable to large data volumes
Cons
- Less hand-holding on decisions
- Steeper learning curve than other options
Inclusive Decisions: Loomio
Loomio excels at structured, inclusive, and collaborative decision-making.
Pros
- Easy to get started
- Promotes inclusiveness
Cons
- Freemium version very limited
- Less structured analytics compared to others
Flex Rules/Models: Actico
Actico offers a highly flexible intelligent decision management platform built on the Drools rule engine.
Pros
- Extreme flexibility to model advanced logic
- Seamless integration with APIs
Cons
- Very complex for beginners
- Steep learning curve
Credit Risk: GiniMachine
GiniMachine leverages machine learning to build customizable credit risk models in minutes without coding.
Pros
- Low/no code machine learning
- Accelerated model development
Cons
- Very niche focus on credit modeling
- Less flexible beyond core application
Workflow Decisions: FlowForma
Flowforma offers a business process management tool with integrated decision features called Decision Point.
Pros
- Affordable and easy to use
- Good for structured approvals
Cons
- Light on advanced data analytics
- Steep learning curve past basic features
Risk Management: D-Sight
D-Sight leverages augmented intelligence to guide decision-making before action is taken.
Pros
- Augmented intelligence applied pre-decision
- Balance automation with human oversight
Cons
- Very involved consulting style process
- Time intensive to implement
Integrated BI Stack: SAP Analytics Cloud
As a large BI platform, SAP Analytics Cloud offers capabilities to support data-driven decisions.
Pros
- End-to-end analytics in one vendor
- Global enterprise scale
Cons
- Expensive compared to specialized tools
- Steep learning curve
Guided Decisions: DecideAct
DecideAct offers a purpose-built decision intelligence platform combining data, models, collaboration tools, and AI.
Pros
- Easy to use app-based UX
- AI guidance throughout
Cons
- Limited public information as new market entrant
- Less flexibility than open platforms
Strategy Decisions: Optimax
Optimax leverages experiential group simulations to provide immersive decision-making grounded in data.
Pros
- Creative experiential approach
- Stakeholder alignment and buy-in
Cons
- Manual development of simulations
- Narrow application vs. full platforms
This sampling of 12 vendors provides options across key capability segments, use cases, and price points. There are over 50 technology vendors offering some form of decision tools – with more innovation on the horizon.
Developing a Structured Decision Platform Strategy
Transitioning from ad-hoc decision practices to data-driven decision intelligence takes planning and discipline. Based on experience implementing these solutions over the past decade, here is a phased approach I recommend:
1. Evaluation
- Document known pain points in key processes – lost revenue, risk, waste, etc.
- Interview stakeholders and map out high impact recurring decisions
- Define measurable value – direct cost savings, improved outcomes, etc.
- Research platforms aligned to use cases and gather proposals
2. Foundation
- Start with structured decisions requiring cross-functional input
- Focus on consistent participation over comprehensive data connectivity
- Leverage decision templates to socialize effective practices
3. Maturity
- Expand to additional decisions eats facing the business
- Increase data sources, access controls, automation
- Quantify impacts over baseline performance
4. Network
- Connect decisions into workflows and systems
- Centralize documentation and insights
- Continuously improve models
Getting started on a few foundational use cases allows building momentum with stakeholders before tackling complex integrations. Patience is critical – evolving decision culture gradually over several quarters.
Measuring Platform Value and Calculating ROI
Given the strategic nature of improved decision making, quantifying value goes beyond simple payback analysis. Frameworks like Total Economic ImpactTM combine cost savings, productivity gains, risk reduction, and estimated created value into a single ROI measure.
Example metrics to track
Cost Savings
- Consolidated systems
- Automating recurring analyses
- Reduced waste from poor choices
Productivity
- Accelerated routine decisions
- Increased capacity from centralization
Risk Mitigation
- Reduced revenue volatility
- Lower likelihood of fines, errors
Value Creation
- Uplift on key process KPIs
- More productive employees, customers
Combined ROI
For a composite view, the total value measured across categories is divided by implementation and ongoing platform costs. Top-tier platforms typically generate composite ROI between 300-800% in the first three years.
The Future of Decision Intelligence
The next generation of decision intelligence capabilities we can expect will drive more comprehensive automation, pervasive augmentation, and broad connectivity:
- Platforms embedded directly into operational systems
- Continual learning models that adapt predictions
- Immersive simulations for virtual collaboration
- Expert guidance accessible across devices
- Decision architectures mapped to neural processes
As making good decisions becomes more complex – technology will continue evolving to simplify the process, reduce risk, and unlock new levels of performance.
Summary
With the exponential growth in data, businesses require purpose-built tools to convert raw information into confident action. Decision intelligence platforms enable you to combine numbers, knowledge, and relationships – maximizing the unique capabilities of both artificial and human cognition.
The key is finding the platform approach that aligns to your specific decision needs in terms of analytics depth, integrations, collaborative requirements and budget.
Hopefully this guide provides a useful perspective on the history, current landscape, implementation roadmap, and future trends shaping the decision platform ecosystem.
By leveraging the latest decision technology tailored to your environment – significant operational improvements and competitive advantage await.
