BEIS Knowledge Hub

The connection between two seemingly disparate concepts, Entropy and the Golden Ratio (phi) is not one of direct causality, but of powerful analogy. If business entropy is the relentless march towards chaos, the golden ratio (phi) provides a blueprint for building systems and structures that are inherently resilient, balanced, and capable of sustained growth.

In BEIS, Business Entropy (specifically Shannon Information Entropy, Es) is a quantifiable measure of the disorder, uncertainty, complexity, and potential for information loss within organizational processes, systems, and their environment. A higher Es can indicate greater unpredictability or instability. BEIS aims to track this to provide insights into system performance and potential risks.

Traditional analytics often focus on historical KPIs in isolated functional areas. BEIS takes a more systemic, network-centric approach.

• Holistic View: BEIS models the entire organization (or relevant unit) as an interconnected network, analyzing how different parts influence each other. Traditional tools often look at metrics in silos.
• Emergent Properties: BEIS scores (Eb, Es, Rs) are calculated as emergent properties of this network's structure and state, offering deeper insights than individual metrics.
• Predictive & Proactive: Advanced BEIS aims for diagnostic, predictive, and even prescriptive analytics, moving beyond descriptive reports. It seeks to identify leading indicators of future performance or disruptions.
• Quantifies New Aspects: BEIS introduces novel metrics like Systemic Friction (Eb), Systemic Unpredictability (Es), and a proactive Resilience Score (Rs).

A network-centric BEIS implies potentially extensive data requirements, categorized as:

• A. Structural Data: For network topology and edge properties (e.g., process maps, org charts, system architecture, strategic plans, expert knowledge).
• B. Performance & State Data: For node states and metric values (e.g., existing KPIs, process performance data, resource performance, qualitative assessments).
• C. Time-Series Data: Historical data for quantitative metrics is essential for volatility, trends, Es calculation, and dynamic analysis.
• D. Event & Shock Data: Logs of past disruptions and their impacts for resilience (Rs) and Es calibration.

A phased data integration strategy is proposed, starting with core domains and leveraging expert knowledge where hard data is initially scarce.

The Resilience Score (Rs) is a proactive measure of the system's (network's) capacity to withstand and adapt to disruptions while maintaining core functionality. It's a key differentiator of the next-gen BEIS.

It assesses the ability to: Absorb shocks, Adapt behavior effectively, and Recover to a stable state in a timely manner.

Importance: In an increasingly volatile world, understanding and improving resilience is crucial for business continuity and competitive advantage. Rs provides a quantifiable way to:

• Benchmark and track resilience efforts.
• Make informed investment decisions for resilience-building initiatives.
• Proactively identify vulnerabilities before major disruptions occur.

Getting started with a BEIS pilot involves an initial consultation to understand your specific challenges and objectives. The typical steps include:

• Scoping Workshop: Define the business unit or key processes to be modeled and identify critical objectives and known pain points.
• Data Identification & Collection: Work with your team to identify available data sources (as per BEIS requirements) and plan for any necessary expert elicitation.
• Initial Network Model Construction: Build a baseline network model, often starting with core domains and high-level influences.
• Metric Calculation & Analysis: Calculate initial Eb, Es (and conceptual Rs) scores based on available data and the model.
• Insight Generation & Validation: Review findings with your team, validate insights, and identify potential areas for intervention or deeper analysis.
• Iterative Refinement: Gradually add more detail to the model (processes, resources) and refine scores as more data becomes available.

The goal of a pilot is to demonstrate the value of BEIS in your specific context and build a roadmap for broader implementation. Click here to request a consultation.