Discussion on Data and its Significance in various Enterprise Fields
Adolfo Huaman
Founder of MOS? and its EVA? Framework |Behavioral Economics | Operational Excellence | Business Improvement | Asset Management | Change Management Practitioner | PAM | BBLSS |Value Analytics | Value Innovation |AI.
Below we are going to address some key aspects of Big Data Analytics applied to the current business context as a result of the onboarding process on more than 140 companies around the world:
These key aspects that we have found over the last 12 years may vary depending on the context and purpose of the specific business model (BSP), but for the purposes of maximizing economic results they are the minimum required.
Now, we are going to try to explain each of them, in the most agile and understandable way possible.
Understanding Data-Driven Decision Making
Data-driven decision making involves using factual data and applied analytics to inform and guide critical business decisions for improved outcomes. Hard data, also called factual data, refer to reliable and methodologically sound data taken from official or organisational statistics that are comparable and roughly independent from the way they were measured. However, we have been able to reveal in more than 140 companies that although each and every one of them makes decisions, there is still no structured process for this, digitally speaking.
Adolfo Huamán Díaz’s Digital Operational Excellence DOE model emphasizes using big data analytics through Data Driven Framework to enable agile, informed, and collaborative decision-making processes. The model proposes a structure that integrates key components to maximize the value of big data in decision-making processes as follows below:
1. Data-Centric Organizational Design
? Decentralized Decision-Making: Empower teams across all levels with access to actionable data. This approach allows for quicker and localized responses.
? Centralized Oversight: A central hub ensures data governance, standardization, and strategic alignment across the organization.
2. Intelligent Data Ecosystem
? Big Data Infrastructure: Utilize scalable platforms to collect, store, and process massive volumes of data in real-time.
? AI and Machine Learning (ML): Implement predictive analytics and ML algorithms to extract liquidity, patterns, forecast trends, and offer actionable insights.
? Integration of Systems: Link operational systems (e.g., ERP, IoT sensors) through available operational technology with advanced analytics to provide real-time insights into operational processes.
3. Decision-Making Framework
? Data-Driven KPIs, KROs and/or KRAs: Define metrics aligned with organizational objectives to guide decision-making.
? Scenario Analysis and Simulations: Use advanced models to predict outcomes of different strategies, enabling informed risk management.
? Digital Twin Technology: Create digital replicas of management operating systems for testing and optimizing decisions in a virtual environment and autonomous way.
4. Collaborative Decision-Making Tools
? Unified Dashboards: Provide role-specific dashboards to deliver personalized, real-time data to be visualized by all roles and at each of their results levels.
? Collaborative Platforms: Encourage cross-functional collaboration through tools like digital whiteboards and digital management systems.
? AI-Augmented Insights: Offer prescriptive analytics (anticipated scenaries) to support decision-makers in choosing optimal actions at the level of individual activity.
5. Agile Decision-Making Culture
? Empowered Teams: Train employees to interpret data and integrate insights into their workflows using the exploratory, recursive and improve-ability processes.
? Continuous Feedback Loops: Implement mechanisms to learn from decisions, refine strategies, and improve over time through the Analysis and Improve process.
? Experimentation and Iteration: Encourage testing innovative approaches based on actual data insights without fear of failure.
Business Transformation Value Workflow Explanation
Our Business Transformation Value for DOE? framework based on our real findings will provide you:
1. Big Data Integration
This component focuses on managing and structuring data to make it actionable.
? Primitive & Entropy Data: Refers to raw, unstructured data and data variability that needs refinement for better usability considering its same polymorphism, degree of abstraction, encapsulation and inheritance of the context where it has been generated.
? Generate, Refine & Merge: The process of collecting data from multiple sources, cleaning it, and integrating it into a unified format and/or new structure to be analyzed.
? Prioritize: Assigning importance to datasets based on their relevance to business objectives (Data- Liquidity).
? Big Data Operational: Ensuring data is operationally aligned for business use.
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? Data Process Automation: Streamlining repetitive tasks like data entry, analysis, and reporting using automated processes.
2. Big Data Analytics BDL?
This layer focuses on deriving insights from the processed data.
? Big Data Interaction: The process of creating meaningful interactions between datasets to derive patterns (actual and/or emergent).
? Big Data Liquidity: Refers to the ability of data to flow freely and efficiently across systems for decision-making. Data liquidity refers to the ease with which data can be reused and recombined across an organization. It can also refer to data that is no longer restricted to databases or silos. Data liquidity is important because it can help organizations unlock the value of their data. Data is reusable and can become more valuable when used in different ways. However, companies often use data in linear cycles, which can lead to data becoming incomplete, inaccurate, or poorly defined.
? Anticipated Prediction: Using historical data and AI models to predict future trends or or data behaviors, in different business scenarios.
? Data Profitability: Analyzing how data can drive revenue growth or cost optimization on the same economic operating flow of the entire business value chain.
? Multiple Scenarios: Developing predictive models for various scenarios to aid decision-making.
? Autonomous Decisions: Leveraging Applied AI for decision-making without people intervention where appropriate.
3. Value Transforming
This is the stage where analytics drive financial and operational performance.
? ROIC (Return on Invested Capital): Measures profitability and efficiency in generating returns on real time.
? AISC (All-In Sustaining Costs): Commonly used in industries like mining, manufacturing and/or capital intensive companies representing the total cost of sustaining operations.
? FCF (Free Cash Flow): Cash available after expenses, indicating financial health and flexibility which includes the return or delivery value from embedded digitalization.
4. Reporter and BI (Business Intelligence)
? Focuses on delivering actionable insights through visualizations and reports, ensuring that stakeholders at all levels can access and interpret data to choose the best alternative option and maximize the total net benefit.
5. Advanced Analytics & Big Data Liquidity BDL?
? Integrates advanced analytical techniques like machine learning and ensures seamless data flow for agility and informed decision-making.
6. DBM and LDBM
These components represent the data-based models for monetization.
? DBM (Data-Based Models): Organizing datasets to drive insights and decision-making frameworks.
? LDBM (Liquidity-Driven Business Models): Focused on optimizing data liquidity for financial and operational efficiency.
7. OPTI? and MAXI?
These terms relate to optimizing and maximizing outcomes:
? OPTI (Optimization): Using data insights to fine-tune operations for efficiency and cost savings and/or cost optimization.
? MAXI (Maximization): Scaling up successful digital strategies to achieve the highest possible value in the short, medium and long term.
8. People, Ownership, Processes, and Unlocking Value
These four pillars highlight the people (human) and structural elements crucial for DOE?:
? People: Empowering individuals with the right data and tools.
? Ownership: Establishing accountability for data management and decision-making at every response level. People are still the only component that cannot be digitized, but on the contrary, they are the ones that ensure the delivery of value in the company.
? Processes: Defining workflows that integrate big data analytics seamlessly. In an agile, simple, dynamic, and flexible way.
? Value: Ensuring that every decision and process adds measurable value to the organization from its unlocking, assessment, prioritization, articulation, transformation and final delivery of value.
This framework emphasizes a data-driven, automated, and financially focused approach to achieving Digital Operational Excellence DOE?. Each component works synergistically to drive strategic decisions and business transformation.
This structure aims to leverage big data as a strategic asset, enabling fast, evidence-based decisions while maintaining alignment with long-term goals.
In the second part of this article, I will explain how big data analytics is transforming companies and the results they are delivering.
Welcome with this first part of this first article, to the economy of the future in a digital economy environment...