Revolutionizing Decision Making with Collaborative Modeling and Predictive Analytics

Eric Torkia
Sep 26
3 min read

When building models, we are often confronted with assumptions that evolve over time. Capturing these changes is critical to keeping models relevant and useful. Over the past decade, Business Intelligence (BI) solutions have fostered a culture of self-service information access. This democratization of data has created powerful opportunities, but also significant pitfalls for business analysts and decision-makers.

This article outlines the benefits and challenges of shared modeling in a distributed environment, and suggests strategies to get started with integrated predictive and collaborative planning.

Opportunities for Collaborative Predictive Analytics

Proximity to the process – People closest to operations can directly contribute their assumptions.
Decision-making flexibility – Direct access to data allows faster scenario testing and course corrections.
Collaborative decision processes – Teams can collectively refine models rather than relying on isolated analysts.
Whitespace opportunities – Shared insights can spark innovative projects and uncover new business options.
One version of the truth – Centralized, shared models reduce discrepancies across teams.
Group estimates – Diverse perspectives can improve accuracy through crowd-based forecasting.

Challenges and Pitfalls

Traceability of assumptions
- Who made the assumption?
- What methodology was used?
- What data sources were referenced, and are they legitimate within the organization?
Data governance issues
- Lack of master data management (MDM) increases the risk of inconsistent results.
- Different business units or geographies may consolidate data differently, leading to “apples vs. oranges” comparisons.
- Multiple definitions for similar concepts can create confusion.
Conflicting analysis
- Two analysts may run the same scenario but arrive at very different results, eroding trust in the process.
Implementation costs
- Process redesign to support collaborative modeling.
- Training teams to adopt new practices.
- Change management to drive adoption across the organization.
- Software and infrastructure investments to enable integration.

Integrating System Data into predictive analytics models built in Excel

Predictive Analytics planning and budgeting process

Source: Oracle Corporation, 2010

One of the most compelling applications of collaborative modeling is the Planning and Forecasting Process (illustrated above). There are two main ways to integrate predictive modeling into planning: the classical spreadsheet-driven approach and the modern BI-integrated approach.

Source: Oracle Corporation, 2010

1. The Classical Approach

Build a spreadsheet model with imported tables and pivot tables from BI and operational systems.
Run/simulate the model locally using tools such as Crystal Ball, @RISK, or ModelRisk.
Extract results at the desired risk level (e.g., 90%).
Export single-point estimates back into the planning system via flat files or CSV.

Note: ModelRisk Professional can streamline step one through DataObjects, which can be sourced directly from worksheet ranges or SQL queries. This reduces manual manipulation but does not eliminate the need for exporting and re-importing results.

2. The Power BI / Database-Integrated Approach

A more modern alternative leverages Power BI (or other enterprise BI tools) as the integration layer. Models can still be developed in Excel, but results are exported directly into a database or pushed into Power BI for visualization, collaboration, and integration with organizational planning processes.

Key benefits:

Results are shared in a governed, organization-wide environment rather than siloed spreadsheets.
Forecasts can be validated against official planning datasets, business logic, and process rules stored centrally.
Enables near real-time visibility for decision-makers across the organization.

Drawback:

Requires disciplined data governance and infrastructure to manage imports/exports (CSV or database connectors).

Moving Forward

Organizations moving toward distributed modeling need to balance the flexibility of democratized analytics with the rigor of governance and documentation. A high-level integrated predictive planning process should:

Capture and document evolving assumptions.
Enforce data standards through MDM.
Provide transparency around methodologies and sources.
Support iterative collaboration without sacrificing accountability.

By acknowledging both the opportunities and pitfalls, businesses can harness collaborative predictive analytics to make better, faster, and more resilient decisions

Best Practices for Collaborative Modeling Success

Integrate BI and modeling tools – Ensure your BI applications or data integration layer can seamlessly interact with modeling platforms.
Adopt an integrated planning process – Forecasting should follow a coherent, end-to-end process that connects strategy, budgets, and operational plans.
Define clear business rules – Establish guidelines on how and where to source data for different types of analysis.
Map and communicate data sources – Power users should have visibility into approved data sources to avoid inconsistencies.
Establish a governance process – Provide review and support structures so power users can validate and justify their analysis.
Set validation rules for managers – Equip leaders with simple criteria to confirm or challenge analysis, based on the legitimacy of data sources and methodologies.