How Df2020 could through its platform and new innovation provide solutions to the challenges faced
Human Controlled Precision
The Df2020 Platform is designed for humans to have absolute control of the precision in the way rule-based knowledge is defined in terms of logic and narrative. This is imperative whenever rule-based knowledge needs to reflect a set of values, principles and objectives that are aligned with organisational goals and aspirations. In particular, this applies to policies and the way they are implemented. Policies need to consider political, social, cultural, ethical, and legal factors, so that this precision enables the narrative to be controlled in a comprehensive and nuanced way. It is inconceivable that any organisational Board would relinquish control of these types of rule-based knowledge to machines, let alone accept the risks this would expose to their stakeholders and legal obligations.
Precision Integrity Assured
The integrity of the human-controlled logic and narrative is assured through the configuration of a Knowledge Map. Once complete, a cloud-enabled Digital Agent is automatically published. The automation is based on a WYSIWYG (what-you-see-is-what-you-get) principle, which provides assurance of the precision integrity.
Swarm Intelligence
The Df2020 Platform is designed to build a unique type of multi-agent-based network (MABN). The Digital Agents are autonomous entities. Each Digital Agent can seamlessly pass control to another Digital Agent. Each Digital Agent is likened to a human subject matter expert (SME) that hands off to another SME covering a different topic or nuance of knowledge. The MABN enables the deployment of Swarm Intelligence, which is scalable and flexible.
Automate the Data Supply Chain
The Swarm Intelligence interacts with individuals to identify anti-social risks. Every step of the interaction automatically generates data, which cannot be changed. This data can be put into a Compliance Store as it provides a complete audit trail. The generated data can be used for other purposes such as measurements and predictions. The automation of the data supply chain delivers benefits such as efficiency, accuracy, full transparency, cost savings and better risk management.
Non-Linear Rule-Based Knowledge
Df2020 supports the codification of non-linear rule-based knowledge to support complex relationships and connections. This is because rule-based knowledge is based on conditional statements to reflect the different Pathways. Because of the MABN approach the modulization of the knowledge can be interconnected in numerous ways, enabling an ecosystem that can grow into any direction.
Linear Streamlined Pathways by Masking Complexity
The user experience with the Digital Agent is driven by the step-by-step interaction. This means the user is not exposed to the landscape of the knowledge as they only have visibility of their step-by-step journey. The user journey is linear and is measurable by the user decision journey. This streamlined approach has the advantages of consistency, efficiency, transparency, accuracy, and scalability no matter the complexity of the knowledge.
Measuring the S in ESG
The ability to manufacture new types of data provides the means for a range of new services to be developed around social impact. The data generated from the interaction with the Digital Agent means the data can be used to:
Measure outcomes and social impact
Use emergent evidence for investigations
Perform analytics
Forecast trends
Supply proof of understanding which can be included in contracts
Trigger events for notifications and process automation.
The Benefits
Df2020’s ability to generate non-financial data into hard metrics, provides the potential for a change in basic assumptions when it comes to measuring the S in ESG. Df2020’ approach challenges conventional thinking, as knowledge is treated first and data second. There are therefore many benefits for an organisation such as:
Enhancing brand reputation
Attracting and retaining talent
Reducing the costs of coordination
Collapsing the cost of the data supply chain
Improving productivity
Reducing regulatory and compliance overheads
Sensing early and responding smartly to emergent issues
Improved risk management
Lower legal costs and provisions
Alignment with stakeholder trends
Attracting socially responsible investors
Organisations with fragmented rule-based knowledge could expect to make cost savings of up to GBP 0.5 to GBP 2m for each GBP 10m of OPEX expenditure. These 5% to 20% OPEX potential savings are subject to the condition and importance of the fragmented rule-based knowledge.
Go to Market
Df2020’s go to market strategy is via partnerships that wish to licence our cloud-enabled platform with embedded methodologies.
More Insights
Contact
John Rawlings: jrawlings@df2020.com
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