This is the first in a series of articles explaining how design information governance (IG) adds to the ontological and structural language that creates the 'sensemaking' framework for complex adaptive systems. In doing so, IG provides a foundational enterprise capability which enables adaptive behaviour and organizational resilience in the face of changes in the internal and external environment.
Modern society is enabled by systems, some of them technology-centric like our road and rail networks, some human-centric like our system of parliamentary democracy, and some a more balanced mix like our health system. Successful systems are those that are effective in meeting the needs that they were designed (or emerged) to meet and are sustainable in the face of change.
A key enabler of successful systems is appropriate design information. For example, the number and boundaries of electorates in our parliamentary democracy are periodically adjusted to ensure that the (design) principle of equal representation continues to be applied as our population distribution and demography changes over time. Another example is public infrastructure such as road and rail networks which evolve and are sustained via investment driven by changes in demand, changes in technology, and changes in societal expectation on matters such as safety and reliability. And, of course, the ever-changing health system is informed by changes in the human condition and advances in medical technology.
Typically, the focus of IG is controlling data and information within an organisation; however, IG also applies to the very significant amounts of complex and changing information used to inform design decision-making across our society. At a higher level, IG means maximising the value of information by facilitating use in better informing decision-making for major systems interacting with each other to improve how our complex society works and particularly to avoid catastrophes such as the cascading failures of energy networks leading to widespread transport and health system failures. IG, therefore, is an essential component and foundation of controlled change in any enterprise (using the broad definition of enterprise here as being any deliberate or consciously coordinated socio-technical entity with a relatively identifiable boundary whose constituent parts work together towards a set of defined goals).
What does this mean in practice? The Information Governance Initiative defines IG as the activities and technologies that organisations employ to maximise the value of information while minimising associated risks and costs. Therefore, IG in the design context (‘design IG’) covers concepts such as information provenance (do I know where this design information comes from and can I trust it?) and Information Assurance (confidentiality, availability, integrity, authenticity and non-repudiation).
From a technologies perspective, design IG covers governance of the vast investment in information systems used to store and process design information. These information systems have revolutionised design across all sectors of industry and society in recent decades with the ability to rapidly gather, analyse and draw meaningful conclusions from vast amounts of often dissimilar and unstructured data allowing much more informed design decision-making:
· In public policy, the ability to better understand the lives and opinions of citizens enables more rapid and nuanced public policy design … but also with the associated risk of manipulation per the Cambridge Analytica scandal associated with the most recent US Presidential Election and, locally, targeted ‘robo-calling’ and similar techniques.
· In public infrastructure, the ability to combine demographic, social trend, geospatial and engineering information allows the creation of ‘digital twins’ of current and planned infrastructure. These ‘digital twins’ facilitate much better understanding of overall system behaviours, the effect of design changes and associated costs, and potential points of failure. At an operational level, ‘digital twins’ allow better understanding of current performance and how to deal with system failures such as broken-down trains and external events such as extreme weather. And concepts such as Asset Management (AM) are being implemented in information-intensive Asset Management Systems (AMS) that draw upon the ‘digital twins’ developed during systems design leading to end-to-end IG across the whole lifecycle
· In human health, vast troves of statistical data combined with detailed understanding of the human genome and disease processes are enabling ever more sophisticated and targeted treatments (immunotherapy, for example, which ‘trains’ the immune system to attack cancerous cells). In this way, medical science is moving towards the ‘digital twin’ concepts currently being popularised in infrastructure and engineering more broadly.
Ongoing advances in Artificial Intelligence, Machine Learning, ‘Big Data’ analytics and similar technologies, as well as our fast-evolving societies, will only accelerate these changes.
Author
Shaun Wilson is Founder and Head of Business Development, Shoal Group Pty Ltd.
