What is Datanomics?

(This version is in beta.)


Datanomics is a new field to measure and track value in the age of blockchain by calculating and engineering relative relation-chains.

Datanomics is based on the thory 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. It is just the same with us humans:

The rules that you carry inside of you because your personal experiences and due to the character of your person are different from mine, and they are also different from the rules of every other person out there.

Therefore, we 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 at have the power to be benefitial for all stakeholders involved.

Below you will find a first, very detailed description of Datsanomics tht looks at the topic from an system-engineer's point of view. A wholistic and more understandable explanation of Datanomics for those who are not educasted as Systems-Engineers will be published on Noember 15th,2019 together with a free & brief summary of Datanomics.

Below is an explanation of how effective systems ('systems' are effective systems, referred to as an equivalent to value) that can be built according to their highest usefulness for the user, instead of their highest profitability for financial investors or project owners.


At its core, datanomics organizes accessible knowledge in the best interest of all stakeholders involved by tracking feedback in the best way possible for all stakeholders involved.


The goal of Datanomics is to reach the maximum usefulness for all stakeholders who are involved, as well as for those who should benefit from the respective system.


For Datanomics, you need to consider who is in your network (in your scope of possibilities) and what tools/abilities they have.


A system as it is described in the next chapters to follow can continue to function after the initial life cycle has ended, it can be retired (End) or it can be updated based on the findings and developments that resulted from the initial life cycle.


At the World Datanomic Forum, we design such systems with specific mentioning of the time and the location in which the respective system should function.

Different topics and issues such as requirements engineering, reliability, logistics, coordination of people and resources such as time or other things, testing and evaluation of tries and errors, maintainability and many other disciplines necessary for a personalised and successful system development, design, implementation, and ultimate destruction or retirement of a system become more difficult when dealing with large, complex and unexpected situations. However, with the use of Datanomics, complex systems can be handled easily and effectively with a minimum usage of time.


Alongside this essay, it is recommended that you as the reader spend some time to learn about methods of nonviolent-communication and impulse control because they will help you navigate through the process of forming, managing and changing or retiring a system effectively.

​Datanomics ensures that all likely aspects of a project and of the generation of value of different kinds, be it environmental,  interpersonal, financial are carefully considered are integrated into a whole - alongside the creation of financial value.


Preparing A Design Or Research


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 that you carry while actually living and constantly updating the systems you form or are part of.


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 must begin by discovering all actual problems that need to be resolved, and by identifying the most probable or highest impact failures that can happen so that they can be effectively prevented and mastered.


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, this design evolution 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 - but we will get to that later.


The methods presented here are meant to help you in the better comprehension, design and development control of engineering systems with the use of Datanomics 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.


Datanomic engineering 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 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 actuslly happening. Datanomics puts crystallized intelligence into 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 today.


The aim of this essay 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 project.


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 ,System Life Cycle Model’.


Such a model based on quantitative data must always also include a full understanding of the stakeholders and as much qualitative data as possible.


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, dataanomic 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 performer.


This process proceeds from concept to production to operation and, in some cases, to termination and the letting go or retirement of the solution.


In an acquisition, the holistic integrative discipline combines contributions and balances such cost, schedule, and performance while maintaining an acceptable level of risk taking throughout the entire life cycle both for the performer and for his or her surrounding.


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 at the World Datanomic Forum today include:


Systemic/tech-based architecture,

System model, Modeling, and Simulation,

Optimization of processes and communication in companies,

System dynamics,

Systems analysis,

Reliability analysis, and

Decision making


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 that you 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 you possibly can, 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 (Eudamonomics) deals with the analysis of the interaction between stakeholders who are involved, supported by the transparent use of data.


The methods for interpersonal and personal datanomics can vary but the stakeholders in a design must be taken into account also on a qualitative and not just on a quantitative level.


By applying methods around interpersonal or personal (eudamonomic) 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.


Finally, it is important to note decisions made at the beginning of a project or other details whose consequences are not clearly understood by the creator of a system and which can have enormous implications later in the life of a system, and it is the task of the modern systems engineer to explore these issues and make critical decisions.


While scientists study how nature works and thus use data to understand,engineers aim to create new things, such as products, websites, environments, and experiences.


Engineers and scientists have different objectives, they follow different processes in their work:


Scientists perform experiments using the scientific method whereas engineers follow the creativity-based engineering design process to create  a functional solution, rather than to prove a statement as being either false or correct.


Both processes can be broken down into a series of steps, as seen in the diagrams:

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Keep in mind that although the steps are listed in sequential order, you will likely return to different previous steps multiple times throughout the development and testing of a project.


For example, it is often necessary to revisit stages or steps in order to improve a certain aspect of a project.


Why are there two processes?


Both scientists and engineers contribute to the world of human knowledge, but in different ways.

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Keep in mind that although the steps are listed in sequential order, you will likely return to different previous steps multiple times throughout the development and testing of a project.


For example, it is often necessary to revisit stages or steps in order to improve a certain aspect of a project.


Why are there two processes?


Both scientists and engineers contribute to the world of human knowledge, but in different ways.

Scientists use the scientific method to make testable explanations and predictions about the world.


A scientist asks a question and develops an experiment, or set of experiments, to answer that question.


Engineers use the engineering design process to create solutions to problems.

This is what Datanomics is about.

It's not about if something can happen. Datanomics explores the question of under which circumstances something can happen.


An engineer identifies a specific need:












And then, he or she creates a solution that meets the need.


Which process should you follow for your datanomic system design?


In real life, the distinction between science and engineering is not always clear.


Scientists often do some engineering work, and engineers frequently apply scientific principles, including the scientific method.


Much of what we often call "computer science" is actually engineering— programmers creating new products.


Your project may fall in the gray area between science and engineering, and that's OK.


Many projects, even if related to engineering, can and should use the scientific method.

If your objective is to invent a new product, computer program, experience, or environment, then it makes sense to follow the engineering design process.



The Scientific Method: Datanomic Questioning


The datanomic method is a process for experimentation that is used to explore observations and answer previously unanswered questions.


Does this mean all scientists follow exactly this process?


No. Some areas of science can be more easily tested than others. For example, scientists studying how stars change as they age or how dinosaurs digested their food cannot fast-forward a star's life by a million years or run medical exams on feeding dinosaurs to test their hypotheses.


When direct experimentation is not possible, scientists usually try to modify the scientific method. In fact, there are probably as many versions of the scientific method as there are scientists!


But even when modified, the goal remains the same: to discover cause and effect relationships by asking questions, carefully gathering and examining the evidence, and seeing if all the available information can be combined into a logical answer.


Even though we show the scientific method as a series of steps, keep in mind that new information or thinking might cause a scientist to back up and repeat steps at any point during the process. A process like the scientific method that involves such backing up and repeating is called an iterative process.


Whether you are doing a science fair project, a classroom science activity, independent research, or any other hands-on science inquiry: understanding the steps of the scientific method will help you focus your scientific question and work through your observations and data to answer the question as well as possible.



Steps of the Scientific Method    

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Ask a Question: The scientific method starts when you ask a question about something that you observe: How, What, When, Who, Which, Why, or Where?


You might require that the question be something you can measure, preferably with precise numbers.


Create a table with the description of the things you are conducting research on, and then assign the values of the numbers that seem most likely to provide clear results.


Examples of such values can be:


-Numbers of people affected

-Budget available


Next to the numbers and the values you assign to a certain thing you are observing, you may want to also note details that remind you later on of why you chaw this exact number or value.


Next to the details, you may also want to list the sources that lead you to the respective value or number.



How To Do Background Research For Your Question


Rather than starting from scratch in putting together a plan for answering your question, you want to be a savvy scientist using library and Internet research to help you find the best way to do things and insure that you don't repeat mistakes from the past.    


Construct a Hypothesis:


A hypothesis is an educated guess about how things work. It is an attempt to answer your question with an explanation that can be tested. A good hypothesis allows you to then make a prediction:


"If _____[I do this] _____, then _____[this]_____ will happen."


State both your hypothesis and the resulting prediction you will be testing to the stakeholders.


Predictions must be easy to measure.


Test Your Hypothesis by Doing an Experiment:


Your experiment tests whether your prediction is accurate and thus your hypothesis is supported or not.


It is important for your experiment to be a fair test.


You conduct a fair test by making sure that you change only one factor at a time while keeping all other conditions the same.


You should always try to repeat your experiments several times to make sure that the first results weren't just an accident.


Analyze Your Data and Draw a Conclusion:


Once your experiment (as suggested in the first flow-diagram) is complete, you collect your measurements and analyze them to see if they support your hypothesis or not.


Scientists often find that their predictions were not accurate and their hypothesis was not supported, and in such cases they will communicate the results of their experiment and then go back and construct a new hypothesis and prediction based on the information they learned during their experiment. This starts much of the process of the scientific method over again. Even if they find that their hypothesis was supported, they may want to test it again in a new way.


Conclusions & Communication Of Results


To complete your project, you will communicate your results to others in a final report and/or a display board that can form the base for the building of a prototype in th engineering phase.


Customers are usually interested in your findings regardless of whether or not they support your original hypothesis! It is valuable information!


Throughout the step 1 of the datanomic prototyping process, you should also keep a journal which lists all of your important ideas and information.


The Engineering Process


The engineering design process bears some basic similarity to the scientific method.


Both processes begin with existing knowledge, and gradually become more specific in the search for knowledge (in the case of "pure" or basic science) or in the search for new solutions and systemic answers to a problem statement or hypothesis.


The key difference between the engineering process and the scientific process is that the engineering process focuses on designing a fully functioning solution while the scientific process emphasizes Discovery.


Steps of the Engineering Design Process    


Define the Problem.


The engineering design process starts when you ask the following questions about problems that you observe:


What is the problem or need?

Who has the problem or need?

Why is it important to solve?

[Who] need(s) [what] because [why].


Background Research For The Engineering Process


Learn from the experiences of others — this can help you find out about already existing solutions to similar problems, and help you with avoiding mistakes that have already been made by others in the past. So, for the datanomic engineering of a design, do background research of the following:


Users or customers

Existing solutions

Location(s) of interest

Barriers of entry (legal hurdles, monopolies etc.)


Specify Your Requirements:


Design requirements state the important characteristics that your solution must meet to succeed. One of the best ways to identify the design requirements for your solution is to analyze the concrete example of a similar, existing product, noting each of its key features.

(This is called a comparative analysis)


Brainstorm Solutions: There are always many good possibilities for solving design problems. If you focus on just one before looking at the alternatives, it is almost certain that you are overlooking a better solution. Good designers try to generate as many possible solutions as they can.


Brainstorm Multiple Solutions


Choose the Best Solution: Look at whether each possible solution meets your design requirements. Some solutions probably meet more requirements than others. Reject solutions that do not meet the requirements.


Develop the Solution: Development involves the refinement and improvement of a solution, and it continues throughout the design process, often even after a product ships to customers.


And now:


Build a Prototype

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A prototype is an early, very basic operating version of a solution. Often it is made with different materials than the final version, and generally it is not as polished. Prototypes are a key step in the development of a final solution, allowing the designer to test how the solution will work.




Test and Redesign: The design process involves multiple iterations and redesigns of your final solution. You will likely test your solution, find new problems, make changes, and test new solutions before settling on a final design.


Test and Redesign


Communicate Results: To complete your project, communicate your results together with your prototype to others in a final report and/or a display board. By documenting your solutions, they can be manufactured and supported together with a team or partners.


Other Datanomic Testing Methods:


Feasibility study


An assessment of the practicality of a proposed project or system.


A feasibility study aims to objectively and rationally uncover the strengths and weaknesses of an existing business or proposed venture, opportunities and threats present in the natural environment, the resources required to carry through, and ultimately the prospects for success. In its simplest terms, the two criteria to judge feasibility are cost required and value to be attained.


A well-designed feasibility study should provide a historical background of the business or project, a description of the product or service, accounting statements, details of the operations and management, marketing research and policies, financial data, legal requirements and tax obligations.


Generally, feasibility studies precede technical development efforts and the actual project implementation.


A feasibility study evaluates the project's potential for success; therefore, perceived objectivity is an important factor in the credibility of the study for potential partners, supporters or investors.


It must be conducted with an objective, unbiased approach to provide information upon which decisions can be safely based as securely as possible.


Formal definition of a feasibility study


A project feasibility study is a comprehensive report that examines in detail the five frames of analysis of a given project. It also takes into consideration its four Ps, its risks and POVs, and its constraints (calendar, costs, and norms of quality). The goal is to determine whether the project should go ahead, be redesigned, or else abandoned altogether.


The five frames of analysis are: The frame of definition; the frame of contextual risks; the frame of potentiality; the parametric frame; the frame of dominant and contingency strategies.


The four Ps are traditionally defined as Plan, Processes, People, and Power. The risks are considered to be external to the project (e.g., weather conditions) and are divided in eight categories: (Plan) financial and organizational (e.g., government structure for a private project); (Processes) environmental and technological; (People) marketing and sociocultural; and (Power) legal and political. POVs are Points of Vulnerability: they differ from risks in the sense that they are internal to the project and can be controlled or else eliminated.


The constraints are the standard constraints of calendar, costs and norms of quality that can each be objectively determined and measured along the entire project lifecycle. Depending on projects, portions of the study may suffice to produce a feasibility study; smaller projects, for example, may not require an exhaustive environmental assessment.


The acronym POLES refers to the five areas of feasibility - Political, Economic, Legal, Operational and Scheduling.


Operational Feasibility    


This assessment is based on an outline design of system requirements, to determine whether the user has the operational expertise to handle completion of the project.


When writing a feasibility report, the following should be taken to consideration:


A brief description of the stakeholder to assess more possible factors which could affect the study


The specific part of the stakeholder entity being examined


The human and economic factor

(which other reasons or which people could be an alternative source of the problem)


Different possible solutions to the problem

At this level, the concern is whether the proposal is both operationally and legally feasible (assuming planned cost and material or human resources)


The operational feasibility assessment is focused on gaining an understanding of the present resources of the stakeholder entity and their applicability to the expected needs of the proposed system.

It is an evaluation of the current structure of the specific analysed person or resource, and of how it meets the need of the proposed system.


Method of production/creation


The selection among a number of methods to produce the same success should be undertaken first. For example, factors that make one method being preferred to another method in agricultural projects can be the following:


Availability of inputs or raw materials and their quality and prices.

(Availability of markets for outputs of each method and the expected prices for these outputs.)


Various efficiency factors such as the expected increase in one additional unit.


Production technique


(After we determine the appropriate method of production of a prototype, it is necessary to look for the optimal technique to produce this prototype.)


Other Project requirements    


Once the method of production and its technique are determined, technical people have to determine the projects' requirements during the investment and operating periods.


These include:


Determination of tools and equipment needed for the project.


Determination of a projects' requirements of constructions such as buildings, storage, and roads …etc. in addition to internal designs for these requirements.


Determination of projects' requirements of skilled and unskilled labor and managerial labor.


Determination of implementation period and of costs of tools, requirements and labour.


Determination of storage of information, as well as cash money to cope with operating and contingency costs.


Project location    


The most important factors that determine the selection of a project location are the following:


Availability of location (approachability and costs).

The impact of the project on the environment and the approval of the concerned institutions for license.

The costs of transporting inputs and outputs to the project's location (for example the distance from a market).

Availability of various services related to the project such as availability of extension services, good traffic connections...etc.


Legal feasibility


Determines whether the proposed system conflicts with legal requirements, e.g., a data processing system must comply with local and international data protection regulations and a project must be acceptable in accordance to the laws of the land.


Operational feasibility    


Operational feasibility is the measure of how well a proposed system solves the problems, and takes advantage of the opportunities identified during scope definition and how it satisfies the requirements identified in the requirements analysis phase of system development.


The operational feasibility assessment focuses on the degree to which the proposed project fits in with the existing business environment and objectives with regard to development schedule, delivery dates, corporate culture and existing business processes.


To ensure success, desired operational outcomes must be voiced during design and development. These include such design-dependent parameters as reliability, maintainability, supportability, usability, producibility, disposability, sustainability, affordability and others. These parameters are required to be considered at the early stages of design if desired operational behaviours are to be realised. A system design and development requires appropriate and timely application of efforts to meet the previously mentioned parameters. A system may serve its intended purpose most effectively when its technical and operating characteristics are engineered into the design at the same time to ensure syncronisity and harmony of people and tools or other resources. Therefore, operational feasibility is a critical aspect of systems engineering that needs to be an integral part of the early design phases.


Schedule feasibility    


A project will fail if it takes too long to be completed before it is useful. Typically this means estimating how long the system will take to develop, and if it can be completed in a given time period using some methods like payback period. Schedule feasibility is a measure of how reasonable the project timetable is. Given our technical expertise, are the project deadlines reasonable? Some projects are initiated with specific deadlines. It is necessary to determine whether the deadlines are mandatory or desirable.


Even more feasibility factors    


Resource feasibility    


This involves questions such as how much time is available to build the new system, when it can be built, whether it interferes with normal business operations, type and amount of resources required, dependencies, and developmental procedures with company revenue prospectus.


Financial feasibility    


In case of a new project, financial viability can be judged on the following parameters:


Total estimated cost of the project

Financing of the project in terms of its capital structure, debt to equity ratio and promoter's share of total cost

Existing investment by the promoter in any other business

Projected cash flow and profitability

The financial viability of a project should provide the following information:


Full details of the assets to be financed and how liquid those assets are.

Rate of conversion to cash-liquidity (i.e., how easily the various assets can be converted to cash).

Project's funding potential and repayment terms.

Sensitivity in the repayments capability to the following factors:

Mild slowing of sales.

Acute reduction/slowing of sales.

Small increase in cost.

Large increase in cost.

Adverse economic conditions.


Market research studies


This is one of the most important sections of the feasibility study as it examines the marketability of the product or services and convinces readers that there is a potential market for the product or services.


If a significant market for the product or services cannot be established, then there is no project.


Typically, market studies will assess the potential sales of the product, absorption and market capture rates and the project's timing.


The feasibility study outputs the feasibility study report, a report detailing the evaluation criteria, the study findings, and the recommendations.


The degree to which the internal environment of the firm matches with the external environment is expressed by the concept of strategic fit.


Strengths: characteristics of the business or project that give it an advantage over others


Weaknesses: characteristics of the business that place the business or project at a disadvantage relative to others


Opportunities: elements in the environment that the business or project could exploit to its advantage


Threats: elements in the environment that could cause trouble for the business or project

Identification of SWOTs is important because they can inform later steps in planning to achieve the objective. First, decision-makers should consider whether the objective is attainable, given the SWOTs. If the objective is not attainable, they must select a different objective and repeat the process.


Users of SWOT analysis must ask and answer questions that generate meaningful information for each category (strengths, weaknesses, opportunities, and threats) to make the analysis useful and find their competitive advantage.


SWOT analysis aims to identify the key internal and external factors seen as important to achieving an objective. SWOT analysis groups key pieces of information into two main categories:


Internal factors – the strengths and weaknesses internal to an organization

External factors – the opportunities and threats presented by the environment external to the organization

Analysis may view the internal factors as strengths or as weaknesses depending upon their effect on the organization's objectives. What may represent strengths with respect to one objective may be weaknesses (distractions, competition) for another objective. The factors may include all of the 4Ps as well as personnel, finance, manufacturing capabilities, and so on.


The external factors may include macroeconomic matters, technological change, legislation, and sociocultural changes, as well as changes in the marketplace or in competitive position. The results are often presented in the form of a matrix.


SWOT analysis is just one method of categorization and has its own weaknesses. For example, it may tend to persuade its users to compile lists rather than to think about actual important factors in achieving objectives. It also presents the resulting lists uncritically and without clear prioritization so that, for example, weak opportunities may appear to balance strong threats.


It is important not to eliminate any candidate SWOT entry too quickly. The importance of individual SWOTs will be revealed by the value and results of the strategies they generate.

A SWOT item that produces valuable strategies is important.

A SWOT item that generates no strategies is not important.




The usefulness of SWOT analysis is not limited to profit-seeking organizations. SWOT analysis may be used in any decision-making situation when a desired end-state (objective) is defined. Examples include non-profit organizations, governmental units, and individuals. SWOT analysis may also be used in pre-crisis planning and preventive crisis management. SWOT analysis may also be used in creating a recommendation during a viability study/survey.


Strategy building    


SWOT analysis can also be used effectively in datanomic systems engineering to build an organizational or personal strategy for the management or the implementation of a design. Steps necessary to execute strategy-oriented analysis involve identification of internal and external factors (using the popular 2x2 matrix), selection and evaluation of the most important factors, and identification of relations existing between internal and external features.

For instance, strong relations between strengths and opportunities can suggest good conditions in the team, family, group of friends or company and justify the use of a more aggressive strategy. On the other hand, strong interactions between weaknesses and threats could be analyzed as a potential warning and advice for using a defensive strategy.


Matching and converting    


One way of using SWOT is matching and converting. Matching is used to find competitive advantage by matching the strengths to opportunities. Another tactic is to convert weaknesses or threats into strengths or opportunities. An example of a conversion strategy is to find new markets. If the threats or weaknesses cannot be converted, a company should try to minimize or avoid them.


Datanomic planning    


As part of the development of strategies and plans to enable an organization r or group to achieve the desired objectives, that organization will use a systematic/rigorous process known as datanomic planning. SWOT alongside PEST/PESTLE can be used as a basis for the analysis of business and environmental factors.


Set objectives – defining what the organization is going to do

Environmental scanning

Internal appraisals of the organization's SWOT, this needs to include an assessment of the present situation as well as a portfolio of products/services and an analysis of the product/service life cycle

Analysis of existing strategies, this should determine relevance from the results of an internal/external appraisal.


This may include gap analysis of environmental factors


Strategic Issues defined – key factors in the development of a corporate plan that the organization must address


Develop new/revised strategies – revised analysis of strategic issues may mean the objectives need to change


Establish critical success factors – the achievement of objectives and strategy implementation


Preparation of operational, resource, projects plans for strategy implementation

Monitoring results – mapping against plans, taking corrective action, which may mean amending objectives/strategies.




In many competitor analyses, marketers build detailed profiles of each competitor in the market, focusing especially on their relative competitive strengths and weaknesses using SWOT analysis. Marketing managers will examine each competitor's cost structure, sources of profits, resources and competencies, competitive positioning and product differentiation, degree of vertical integration, historical responses to industry developments, and other factors.


Marketing management often finds it necessary to invest in research to collect the data required to perform accurate marketing analysis. Accordingly, management often conducts market research (alternately marketing research) to obtain this information. Marketers employ a variety of techniques to conduct market research, but some of the more common include:


Qualitative marketing research such as focus groups

Quantitative marketing research such as statistical surveys

Experimental techniques such as test markets

Observational techniques such as ethnographic (on-site) observation

Marketing managers may also design and oversee various environmental scanning and competitive intelligence processes to help identify trends and inform the company's marketing analysis.


SWOT for community analysis


The SWOT analysis has been used in community work as a tool to identify positive and negative factors within organizations, communities, and the broader society that promote or inhibit successful implementation of social services and social change efforts.


It is used as a preliminary resource, assessing strengths, weaknesses, opportunities, and threats in a community served by a nonprofit or community organization.


This organizing tool is best used in collaboration with community workers and/or community members before developing goals and objectives for a program design or implementing an organizing strategy.


The SWOT analysis is a part of the planning for social change processes and will not provide a strategic plan if used by itself. After a SWOT analysis is completed, a the results can be turned into a SWOT list of a series of recommendations that are to be considered before developing a strategic plan.


Strengths and weaknesses:


These are the internal factors within an organization or body of staff


Human resources - staff, volunteers, board members, target population

Physical resources - your location, building, equipment

Financial - grants, funding agencies, other sources of income

Activities and processes - programs you run, systems you employ

Past experiences - building blocks for learning and success, your reputation in the community

Opportunities and threats:

These are external factors stemming from community or societal forces.


Future trends in your field or the culture

The economy - local, national, or international

Funding sources - foundations, donors, legislatures

Demographics - changes in the age, race, gender, culture of those you serve or in your area

The physical environment (Is your building in a growing part of town? Is the bus company cutting routes?)

Legislation (Do new federal requirements make your job harder...or easier?)

Local, national, or international events



Although the SWOT analysis was originally designed as an organizational method for business and industries, it has been replicated in various community work as a tool for identifying external and internal support to combat internal and external opposition.


The SWOT analysis is necessary to provide direction to the next stages of the social change processes at hand. It has been used widey by community organizers and community members to further social justice in the context of Social Work practice.


Application in community organizations    

Elements to consider    


Elements to consider in a SWOT analysis include understanding the community that a particular team or group is working with. This can be done via public forums, listening campaigns, and informational interviews. Inclusive and clear collection if data and information will help inform the community members and workers when developing the SWOT analysis. A needs and assets assessment are tooling that can be used to identify the needs and existing resources of the community. When these assessments are done and data has been collected, an analysis of the community can be made that informs the SWOT analysis.


Steps for implementation    


A SWOT analysis is best developed in a group setting such as a work or community meeting. A facilitator can conduct the meeting by first explaining what a SWOT analysis is as well as identifying the meaning of each term.


One way of facilitating the development of a SWOT analysis includes developing an example SWOT with the larger group then separating each group into smaller teams to present to the larger group after set amount of time.


This allows for individuals, who may be silenced in a larger group setting, to contribute. Once the allotted time is up, the facilitator may record all the factors of each group onto a large document such as a poster board, and then the large group, as a collective, can go work through each of the threats and weaknesses to explore options that may be used to combat negative forces with the strengths and opportunities present within the organization and community. A SWOT meeting allows participants to creatively brainstorm, identify obstacles, and possibly strategize solutions/way forward to these limitations.


When to use SWOT analysis    


The uses of a SWOT analysis by a community organization are as follows: to organize information, provide insight into barriers that may be present while engaging in social change processes, and identify strengths available that can be activated to counteract these barriers.


A SWOT analysis can be used to:


Explore new solutions to problems

Identify barriers that will limit goals/objectives

Decide on direction that will be most effective

Reveal possibilities and limitations for change

To revise plans to best navigate systems, communities, and organizations

As a brainstorming and recording device as a means of communication

To enhance "credibility of interpretation"to be used in presentation to leaders or key supporters.


Benefits and advantages    


The SWOT analysis in social work practice framework is beneficial because it helps organizations decide whether or not an objective is obtainable and therefore enables organizations to set achievable goals, objectives, and steps to further the social change or community development effort. It enables organizers to take visions and produce practical and efficient outcomes that effect long-lasting change, and it helps organizations gather meaningful information to maximize their potential. Completing a SWOT analysis is a useful process regarding the consideration of key organizational priorities, such as gender and cultural diversity and fundraising objectives.




Some findings from Menon et al. (1999) and Hill and Westbrook (1997) have suggested that SWOT may harm performance and that "no-one subsequently used the outputs within the later stages of the strategy".


Create Preliminary Design Concept:


The datanomic engineering design process is a methodical series of steps that social engineers use in creating functional goals. The process is highly iterative - parts of the process often need to be repeated many times before another can be entered - although the part(s) that get iterated and the number of such cycles in any given project may vary.


It is a decision making process (often iterative) in which the basic sciences, mathematics, and engineering sciences are applied together with social sciences and data analytics/data monitoring to convert resources optimally and to thereby meet a stated objective. Among the fundamental elements of the design process are the establishment of objectives and criteria, synthesis, analysis, communication with stakeholders, marketing, construction, testing, monitoring, updating and evaluation.


research, conceptualization, feasibility assessment, establishing design requirements, preliminary design, detailed design, production planning and tool design, and production.


problem definition, conceptual design, preliminary design, detailed design, and design communication.


clarification of the task, conceptual design, embodiment design, detail design.




Various stages of the design process (and even earlier) can involve a significant amount of time spent on locating information and research.


Consideration should be given to the existing applicable literature, problems and successes associated with existing solutions, costs, and marketplace needs.


The source of information should be relevant, including existing solutions. Reverse engineering can be an effective technique if other solutions are available on the market.[5] Other sources of information include the Internet, local libraries, available government documents, personal organizations, trade journals, vendor catalogs and individual experts available.


Design requirements    

Establishing design requirements and conducting requirement analysis, sometimes termed problem definition (or deemed a related activity), is one of the most important elements in the design process, and this task is often performed at the same time as a feasibility analysis. The design requirements control the design of the product or process being developed, throughout the engineering design process. These include basic things like the functions, attributes, and specifications - determined after assessing user needs. Some design requirements include hardware and software parameters, maintainability, availability, and testability.


Various generated ideas must then undergo a concept evaluation step, which utilizes various tools to compare and contrast the relative strengths and weakness of possible alternatives.


Preliminary design


The preliminary design, or high-level design includes (also called FEED), often bridges a gap between design conception and detailed design, particularly in cases where the level of conceptualization achieved during ideation is not sufficient for full evaluation. So in this task, the overall system configuration is defined, and schematics, diagrams, and layouts of the project may provide early project configuration. (This notably varies a lot by field, industry, and product.) During detailed design and optimization, the parameters of the part being created will change, but the preliminary design focuses on creating the general framework to build the project on.


S. Blanchard and J. Fabrycky describe it as: “The ‘what’s’’ initiating conceptual design produce ‘hows’ from the conceptual design evaluation effort applied to feasible conceptual design concepts. Next, the ‘hows’ are taken into preliminary design through the means of allocated requirements. There they become ‘whats’ and drive preliminary design to address ‘hows’ at this lower level.”




In some cases, a feasibility study is carried out after which schedules, resource plans and estimates for the next phase are developed. The feasibility study is an evaluation and analysis of the potential of a proposed project to support the process of decision making. It outlines and analyses alternatives or methods of achieving the desired outcome. The feasibility study helps to narrow the scope of the project to identify the best scenario. A feasibility report is generated following which Post Feasibility Review is performed.


The purpose of a feasibility assessment is to determine whether the engineer's project can proceed into the design phase. This is based on two criteria: the project needs to be based on an achievable idea, and it needs to be within cost constraints. It is important to have engineers with experience and good judgment to be involved in this portion of the feasibility study.




A concept study (conceptualization, conceptual design) is often a phase of project planning that includes producing ideas and taking into account the pros and cons of implementing those ideas. This stage of a project is done to minimize the likelihood of error, manage costs, assess risks, and evaluate the potential success of the intended project. In any event, once an engineering issue or problem is defined, potential solutions must be identified. These solutions can be found by using ideation, the mental process by which ideas are generated. In fact, this step is often termed Ideation or "Concept Generation." The following are widely used techniques:


trigger word - a word or phrase associated with the issue at hand is stated, and subsequent words and phrases are evoked.


morphological analysis - independent design characteristics are listed in a chart, and different engineering solutions are proposed for each solution. Normally, a preliminary sketch and short report accompany the morphological chart.

synectics - the engineer imagines him or herself as the item and asks, "What would I do if I were the system?" This unconventional method of thinking may find a solution to the problem at hand. The vital aspects of the conceptualization step is synthesis. Synthesis is the process of taking the element of the concept and arranging them in the proper way. Synthesis creative process is present in every design.


brainstorming - this popular method involves thinking of different ideas, typically as part of a small group, and adopting these ideas in some form as a solution to the problem

Various generated ideas must then undergo a concept evaluation step, which utilizes various tools to compare and contrast the relative strengths and weakness of possible alternatives.


Detailed design    


Following FEED is the Detailed Design (Detailed Engineering) phase, which may consist of procurement of materials as well. This phase further elaborates each aspect of the project/product by complete description through solid modeling, drawings as well as specifications.


Design for manufacturability    

Design for manufacturability (DFM) is the general engineering art of designing products in such a way that they are easy to manufacture.


Operating parameters

Operating and nonoperating environmental stimuli

Test requirements

External dimensions

Maintenance and testability provisions

Materials requirements

Reliability requirements

External surface treatment

Design life

Packaging requirements

External marking

Computer-aided design (CAD) programs have made detailed design phase more efficient. For example, a CAD program can provide optimization to reduce volume without hindering a part's quality. It can also calculate stress and displacement using the finite element method to determine stresses throughout the part.


Production planning    


The production planning and tool design consists of planning how to mass-produce the product and which tools should be used in the manufacturing process. Tasks to complete in this step include selecting materials, selection of the production processes, determination of the sequence of operations, and selection of tools such as jigs, fixtures, metal cutting and metal or plastics forming tools. This task also involves additional prototype testing iterations to ensure the mass-produced version meets qualification testing standards.

Front-end engineering


This includes:


Front end development

Front-End Engineering (FEE), or Front-End Engineering Design (FEED).


It is an engineering design approach used to control project expenses and to thoroughly plan a project.


It may also be referred to as Pre-project planning (PPP), front-end loading (FEL), feasibility analysis, or early project planning.


The FEE design focuses the technical requirements as well as rough investment cost for the project. The FEE can be divided into separate packages covering different portions of the project. The FEE package is used as the basis for bidding the Execution Phase Contracts (EPC, EPCI, etc) and is used as the design basis.


A good FEE will reflect all of the client's project-specific requirements and avoid significant changes during the execution phase.


Front-End Engineering focuses on technical requirements and identifying main costs for a proposed project.


It is used to establish a price for the execution phase of the project and evaluate potential risks. It is typically followed by Detailed Design (or Detailed Engineering). The amount of time invested in Front-End Engineering is higher than a traditional quote, because project specifications are thoroughly extracted and the following typically developed in detail:


Project Organization Chart

Project Scope

Automation strategy

Process Flow Diagrams

Project timeline


All of these documents would be developed in detail during a design review after a prototype has been agreed upon.


Degree of automation – depending on the application being considered, automation may or may not be appropriate. Determining the amount of automation in the project will help determine equipment, labor costs, layout, and design.

Rates and levels – to hit a certain rate or level of, for example, production, a certain amount of equipment, materials, and automation may be required. Determining key rates and parameters will have great effect on overall project costs and timeline

Material specifications – Not all materials work well together, or can withstand the physical application. A basic engineering discipline is determining materials of construction, material compatibility etc.

Standards and guidelines – every industry has standards and guidelines, and many industries are regulated. Any equipment, production facilities, manufacturing lines etc. developed for these industries must meet these standards and regulations and can have major impact on costs/time to project completion

Assumptions, Exclusions, and potential problems: FEE seeks to identify potential problems, assumptions or exclusions that could affect the project during execution. Identifying these during the front-end planning stage so they can be accounted for is the goal of FEE.


Axiomatic Product Development Lifecycle (APDL) (also known as Transdisciplinary System Development Lifecycle (TSDL), and Transdisciplinary Product Development Lifecycle (TPDL) ) is a systems engineering product development model that extends the Axiomatic design[2] (AD) method.


APDL covers the whole product lifecycle including early factors that affect the entire cycle such as development testing, input constraints and system components.


APDL provides an iterative and incremental way for a team of transdisciplinary members to approach holistic product development. A practical outcome includes capturing and managing product design knowledge. The APDL model addresses some weak patterns experienced in previous development models regarding quality of the design, requirements management, change management, project management, and communication between stakeholders. Practicing APDL may reduce development time and project cost.


Data Usage Example:


Money (Total Budget)

People (Name/Age/Personal Info)

Resources (Type/Quantity)

Abilities Needed (Type/Level of experience)


Timeline & Roadmap with specific goals you want to achieve in a certain period of time (Depending On The Operating Period Of The Project)


Types Of Orders That Must Be Given

Access Level To Individual Stakeholder

Risk Level Of Each Step

Ideal Steps Taken

Measurement Of Results

-The End-

Published by PS

The World Datanomic Forum 

In case you wish to deepen your know-how about Datanomics or propose an addition to the set of methodologies listed above, send your request to Press@WD-Forum.org.