What is Datanomics?
Datanomics are the economics of the data-century.
At its core, Datanomics are based on the theory of relativity of Albert Einstein, who explained that the rules inside of a full glass of liquid are different from the rules outside of the glass.
The theory of relativity says that the rules are different for everyone and everything - simply because your inner life is different from mine, your surrounding is different from mine, your abilities are different from mine etc.
Making your data economically useful for you - this is Datanomics.
The rules that you carry inside of you because of things such as your personal experiences and the character of your person are different from mine, and they are also different from the rules of every other person out there.
Therefore, you and I value different things in different ways.
If we learn to understand who values what in which way, we can find healthy ways towards more synergies that have the power to be benefitial for all stakeholders involved.
Below is an explanation of how datanomics can help build cohesive social systems that can be built according to their highest usefulness for the people involved, instead of their highest profitability for financial investors or other project owners.
At its core, datanomic systems engineering organizes personal or private information and knowledge.
The goal of datanomic systems engineering is to reach the maximum usefulness for all stakeholders who are involved, as well as for those who should benefit from the respective system.
If the respective system which was built can be used repetetively, it is called a Constructur (https://www.youtube.com/watch?v=d8KCXgzqhSQ).
A Constructor is the blueprint of a system that is likely to function in a respective ecosystem based on previous and current performance rates and results.
A system that is a Constructor can continue to function after the initial life cycle has ended, it can be retired (End) or it can be updated based on new findings and developments. A constructor is a pre-planable system.
The retirement of a system, or of a system of systems, can also be challenging.
Here again, datanomics can be helpful.
For Datanomic Systems Engineering, you need to consider who is in your network.
Name the stakeholders.
Are you also a stakeholder?
What abilities do you/they have?
What tools do you have available and accessible, also through your network?
Now, map out your area of possibilities:
Try and take into account all possibilities that could happen in your defined ecosystem.
Out of this area of possibilities, define your area of interest.
...Meaning: what's the goal?
What do want to achieve eventually?
Are you trying to achieve a collective vision or are you trying to solve one specific problem?
Datanomic systems engineering ensures that all likely aspects of a project and of the ecosystem (be it environmental, interpersonal, financial or other) are carefully considered and integrated into a whole.
The Datanomic Design Process
The datanomic design process is a data-driven design & discovery process that must precede all actual (,real-world’) processes so that risks can be taken into account from early on.
Basically, what this means is that a datanomic design can be understood as a blueprint or a foundation to test a datanomic system while it is still in the manufacturing process.
In the economy, a manufacturing process is usually defined as a process which is focused on repetitive activities that achieve high quality outputs with minimum costs and time.
The datanomic systems engineering process includes the discovery of all actual problems that need to be resolved by identifying the most probable or highest impact failures that can happen so that they can be effectively prevented and mastered.
This process of 'failure-identification' must follow shortly after the vision-building process mentioned above.
Why Datanomic Systems Engineering is so important
Because it is no longer possible to rely on given structures blindly, mainly because we want to understand the context of our surroundings, of our peers and of our own abilities, the datanomic revolution aims to provide new methods with which we can address and deal with the complexity directly, intimately and effectively.
The continuing evolution of systems engineering also means a constant flow of development and identification of new methods and modeling techniques for personal and interpersonal or social engineering based on data.
The methods presented here are meant to help you in the better comprehension, design and development control of engineering systems with the use of data as they grow more complex.
The aim of this book is to formalize various approaches as simply as possible, so that they are easy to be understood.
Datanomics focuses on using data as a neutral base for the creation of awareness of value, of qualitative decision making, of communication and of dialogue with the outside but also for the creation of financial, social and ecological value.
Datanomic systems engineering is the creation of systems with the use of different types of data with the aim to solve clearly defined problems and/or achieve a clearly defined vision.
Effective datanomic systems engineering furthermore focuses on analyzing and understanding community or individual stakeholder needs of all parties involved early in the development cycle as a form of justice-security.
As people, we should not just look at what’s possible, but on what we actually want to happen - and on what is actually happening.
Datanomics puts crystallized intelligence into use as an an understandable framework of stakeholders, abilities, tools and requirements.
In the current moment in time, we are still focusing too much on our financial possibilities, rather than on the goals and outcomes we want to have or that we can envision.
We were never taught to dream in other forms of value and to celebrate individually.
This is the biggest problem of people everywhere.
The aim of this book is to empower readers everywhere to begin and envision or to create possible solutions so as to ensure low risks, high success rates and low maintenance rates for the creators and all other stakeholders involved in a datanomic system.
Functionality is best ensured when requirements are carefully documented by the creator of a datanomic design, who then proceeds with a design synthesis and the system validation (or: testing) while considering the complete problem or opportunity in a so called ,Datanomic System Life Cycle Model’ - a datanomic blueprint that is ready to be tested for one life-cycle, for the first time.
Such a model must always also include a full understanding of the stakeholders and as much qualitative data as possible.
Money is only one resource that is needed in a respective ecosystem. This is why datanomics shifts value from finance to other sectors such as education or healthcare.
It gives less importance to money and more importance to the accessibility of tools, abilities and other resources based on a common vision of the stakeholders.
The goal of the Datanomic Management Process is to effectively organize and monitor your efforts and the efforts of those around you, while the technical process includes assessing available information, defining effectiveness measures, creating behavior models for teams or groups of people, creating a structure model, performing trade-off analyses (finding the best ways forward), and creating sequential ‘build & test’ plans that help you keep track with the usefulness of your system as you build, grow, test and constantly improve it.
Although there are several models that are used, all methods for a modelling process aim to identify the relation between the various stages of testing or of implementation so that you can get a better understanding of where you stand, how the systems you are building are entangled with one another and how you can (and should!) always incorporate feedback.
By providing a complete view of the development effort, datanomic systems engineering helps mold all the individual working areas and fields of contribution into a unified effort picture, forming a somewhat structured development process for the performers.
This process proceeds from concept to production to operation and, in some cases, to termination and the letting go or retirement of the solution.
Keep in mind that the need for datanomic systems engineering came up with the increase in complexity of systems and projects. This also increases the possibility of component friction, and therefore the unreliability of the design.
At the same time, a system can become more complex due to an increase in size as well as with an increase in the amount of information, stress and stakeholders that are involved in the design.
Datanomics encourages us to use tools and methods that better comprehend and manage complexity of systems. Some example areas in which datanomic systems engineering is being used today include:
System model, Modeling, and Simulation,
Optimization of processes and communication in companies,
Reliability analysis, and
Taking a datanomic approach to engineering systems is complex because the behavior of and relationship between system participants is not always immediately well understood.
For this exact reason, it is important to make an active effort to collect and evaluate data and information on which you can rely as much as possible, and to organise it as well as possible, and in the most understandable way possible for other parties involved.
In the datanomic design process, characterizing systems or subsystems (systems of systems), and the interactions among them is one part.
Interpersonal datanomics (social rating systems) deal with the analysis of the interaction between stakeholders who are involved, supported by data.
Stakeholders in a design must be taken into account also on a qualitative and not just on a quantitative level.
Personal datanomics deal with the understanding of ones' own inner data in order to help an individual with taking the best decisions possible for her-, or himself.
Yes, datanomic systems can also be useful for personal vision building processes.
By applying methods around interpersonal or personal datanomics and cross-systemic (systems-linking) datanomics, the gap that exists between different systems can be effectively, responsibly and successfully bridged.
It is advisable that safety measures be included into the design process from the very beginning of the datanomic engineering process.
Safety measures can vary in respect to each system.
Written by Paula Schwarz
March 3rd, 2020.