Figure C5f Data cycles in a pool of data flow in a project funnel

Filters (filtering)

Filtering helps us deal with a large amount of information and data we have available. We try to filter the information and data to end up with something we don't need.

Filtering helps us learn (understand) the flow of information and data that flows to us.

It helps us solve the associated problems and creates new assumptions (hypotheses) for the future situations we anticipate or expect.

Filtering procedures are known steps (delete, add, distort, generalize, postpone, etc.). However, their implementation is a complex task and often needs consensual solving.

The link to the SPC Concept to its proposed project funnel are two basic filters discussed – filters A and B.

• Filter A selects data and information related to SED, DRR, and HA projects.

• Filter B lets entrance into the Funnel only for projects accepting the growing machine support opportunities and internal needs of the project pipeline development strategy.

Machine application and machine learning installations in project preparation and implementation stages are improving the life cycle processes.

Filter B respects the goals, strategy (design), tactic (implementation), and operations of the SPC Utility and SPC Drivers (for more details, see Chapters D and E and some examples in Chapter F).

Data and information behind Filter B are inputs of Data Pools (DPs). Data on the inputs side are used for internal processes in the DP and are transformed into inputs of the following DP.

All are in one Data Lake (DL). The Data Lake (DL) distinguishes two data orientations. Project data and Production data. The whole process contains five DPs.

This web book is interested mainly in the volume, structure, and availability of data created in connection with the preparation and implementation of the project and distinguishes two stages:

1. Project data, including services and works for preparation and implementation of projects.

2. Production data, including services and work needs, is based on the first stage (project preparation and implementation).

All these data have a solid link to the owner of the project work who the project as an added value financed (created).

Through the project life cycle, the data are inputs for any following (other) owners of any new organization (owner) of the work completed (recreated) and reflect the added value of their interest. The Filter's role (A and B) is to create a sustainable environment for all processes in the DL structured as a distributed data system in the responsibility of data management and storage of each DP.

DP 1 (Data Pool 1)

DP1 consists of project data in chains (in the steps) of individual project preparation or sets of project portfolios.

DP1 can have loosely structured data of activities associated with project initiation (ideas, demand from local people, analysis, and finding ways to a consensus to accept a hierarchy of tasks and financial resources in the master plans on-site, e.g., in provinces). 

In the Data Lake (DL) environment, DP1 (a segment of a project) has the opportunity to generate, store and confront (measure) its quality, volume, and sustainability in the broader vicinity of investment and capital changes not only in its province but also in other provinces.

All this through standard procedures with support for an everyday data environment (both on own computers and in clouds international networks). Data DP1 is an environment with links to Data Project Management (DPM) and the global data development of Data Engineering (DE) and Data Science (DS).

Its connection can accelerate ties to digitize project preparation on-site in cooperation with local universities and local government participation (Local Government Units, LGU).

These are the essential preconditions for involving the local Target Group (TG), motivating Local Entrepreneurs (LE) to participate and Local Families (LF) to politically support the development and protection of the place where they live and work.

SPC Concept proposes SPC Utility as a project preparation data integrator. It places SPC Utility as a center of new knowledge and, simultaneously, a place that can and has its financial resources and skills to act in favor of local demand (need) for projects.

Last but not least, SPC Utility will be a place to absorb new practices from the development of artificial intelligence (AI) in the world. There are several reasons for the absorption of AI in low-income regions. It connects the processing, management, and usability of data needed for global development. 

It enables the building of  (organizationally and technically accessible) data paths and their transformation into on-site applications (e.g., see WEMAF and HEDEC project portfolio in the framework of the SPC Drivers by Chapter E).

DP 1 is a data environment in which local SPC Utilities (subsequently the entire SPC Utilities network) can gain the necessary authority on an international scale. Such a spot creates and supports new relationships of investment stakeholders with new end beneficiaries of projects and their drivers.

DP1 has room to be a place (or in the life cycle project funnel of local organizations (e.g., in a business cycle of a province) that takes care of data on new procedures and conditions for local development. 

For example, it can provide the necessary long-term conditions for scaling the local data complex (as a segment from the global package of projects SED, DRR, HA). Can transform (generalize) and test data favor digital transformation in low-income provinces worldwide.

DP1 can offer data that will meet the questions of who and how is involved in local projects and support local initiatives (their feasibility, profitability, and overall benefit) for local businesses and families. 

DP1 collects data for evaluation on which project proposal is practical, its position in the hierarchy of all received submissions, and the order of all required (necessary) projects.

These steps (algorithms) provide time for public project discussions, preparation, and enforcement of civic initiatives, e.g., Citizen's Charters. They lead to an agreement between the key participants in the project, which ultimately results in signing a contract, the essential input for DP2, and other DPs. 

The contract will set out the basic rules for implementing the project (its scope, price, and time of performance) associated with the complete conclusion of all work related to this project.

The contract will set out procedures for this work (e.g., rules for the deployment of Blockchain, Smart Contract, and other technologies) according to SPC utility methodologies for projects within the scope of SPC Drivers in the range of the investment area of ​​the SPC Utility (a site of a province size according to the SPC concept proposal).

Finally, SPC Utility will be the place to absorb new practices from the development of Artificial Intelligence (AI).

It will support digital inclusion on the ground (for end recipients, especially for children of local families) and intervene in changes in Human Behavior in the GT on the path from the bottom-up approach (to survive, not to fool down).

Readers will find further details in Chapters D and E and against the background of selected examples of planned or already implemented projects, especially in low-income areas in Chapter F. 

DP 2 (Data Pool 2)

DP2 consists of data blocks compiled according to project implementation or portfolio steps. The contract determines the structure of DP2. Blocks in this structure are filled with data of standard operations:

• Contract agreement and related agreements to supply materials, products, services, and construction work.

• Preparation and execution (orchestration) of construction, monitoring, reporting performed goods and services supplies, and building works performance.

• Project closing and project final financial statement.

• Project evaluation data and stored in the catalog, in the prescribed format).

DP2 data files follow the data structure in the Data Lake (DL) environment according to DP1. In DP2, the volume of data is growing, and the requirements for their quality are increasing. The share of details (data hierarchy) is growing, and the requirements for their flexibility (data prioritization) are growing.

The data storage regime should allow the data permanent and sustainable confrontation to have an environment of comparable investment and capital operations in the province and elsewhere (using both own computers and the international cloud networks).

Data DP2, like DP1, is an environment with a connection to Data Project Management (DPM) and the global development of Data Engineering (DE), and the progress of Data Science (DS). In DP2, data is more specific and often measurable, creating more room for digitization and project preparation and implementation.

The data can be further elaborated and confronted (tuned) in cooperation with local universities and the participation of local government (Local Government Units, LGU).

DP2 data attract more attention from local target groups (TG), local entrepreneurs (LE), and local families (LF). The engine of their interest should be curious about what is going on and how it will turn out. This public interest deserves attention, and local authorities should care about its sustainability (not only officials but mainly media societies).

The contract closes DP1, and the data as the output passes to DP2. Data takes on new roles; management, consulting, and rescue. Their support comes from SPC Utility (see more in the following Chapters).

The SPC utility is proposed to be the authority to manage and control project implementation, the consulting and financial service for projects implementation, and their immediate surrounding (in the SPC Drivers framework services).

Finally, SPC Utility is also a place that should have the tools to manage bankruptcies and lockdown organizations that create or change their business or public service profile through the project.

The data in DP 2 have a chance to gain the ability to absorb new stimuli from AI in the conditions of low-income areas. It follows by connecting the processing, management, and usability of data needed for global development.

According to Chapters D and E, this data pool, DP2, enables the construction of (organizationally and technically available) data paths and their transformation into on-site applications (e.g., the WEMAF and HEDEC projects within the SPC Drivers portfolio). 

DP1 and DP2 are data environments with the potential for SPC Utilities to gain the necessary authority internationally. The spot creates and supports new investor relationships with the final beneficiaries of projects with new drivers.

It is mainly the development and application of machines for project preparation (Project Preparation Machine, PPM) and their implementation (Project Implementation Machine, PIM).

Further examples are Internal Financial Control Machine (IFCM) and Internal Audit Integrity Machine (IAIM). Similarly, the growing demands for big data processing make it necessary to look for new tools, such as the Project Truth Detector (PTD). For a more detailed description of these tools, please see Chapter D. 

The synergy of DP1 and DP2 has the space to support work with large volumes of data on-site, e.g., in and province, navigate investors, perform benchmarking, and maintain the necessary data care of new procedures and conditions of local development and security. SPC Utility's task is to ensure this on the spot connected to what is happening in the global package of projects SED, DRR, and HA.

DP2 can offer data that answers the questions of what, how, where, and with whom it takes place in the province (urban and rural). Works to support local initiatives to develop and secure local businesses and family responsibilities (entry into local media, marketing, and politics through education and knowledge confrontation).

The contract (outputs of DP1) sets up a structure and steps (algorithms) for working with data in individual blocks (e.g., from procurement to project closing) and chaining blocks and their parts to support the physical execution, testing, and handover of the project as a final product (with an added value on the construction level).

The contract as DP1 outcomes further sets out the application of Blockchain, Smart Contract, and other technologies according to the organizational procedures of the SPC Utility and the SPC Drivers project standards.

DP1 and DP2 data are the critical stock of the proposed services of the SPC Utility Internet server. This communication environment must be the priority of all leaders of the developing countries, mainly their Universities and other stakeholders, and at the start-up phase involvement of the philanthropists.

Readers will find further details in Chapters D and E and against the background of selected examples of planned or already implemented projects, especially in low-income areas in Chapter F.

DP 3 (Data Pool 3)

DP3 covers the interface between the preparation and implementation works and the project's new future when it is starting. In the end, the success of the project mission depends on financial or non-financial revenue from work done on the project in advance.

In other words, success depends on the yields of an organization, on economic profit or social benefit associated with a specific territory, and on staying on it for further use (e.g., for a province).

In the Data Lake (DL) environment, the DP3 data files (set by DP1 and DP2 data) react to the confrontation of the technical condition of the acquired work with the expectations of how the work will withstand its operational time.

It is time to settle construction guarantees and seek guarantees for new work functions, pass through technical, legal, and financial auditing, and accept individual parts of the project by the new owner (owners).

The set of data in the DP3 will show how the current findings of Data Project Management (DPM) have been incorporated into the project and which innovations were brought into the project by the inputs of Data Engineering (DE) and Data Science (DS). The DP3 data will indicate with sufficient precision how the project has succeeded (as a new work) and how it is progressing to its next stage (as a ready-to-use work).

DP3 is offering benchmarking data and confrontation with local universities and government (Local Government Units, LGU) to cooperate. Data from DP3, especially in the hands of the media, will strongly influence local public opinion (especially local entrepreneurs and the local families touched by the project). 

Data from DP3 in the hands of SPC Utility determine the procedures for further negotiations on the physical and social impacts. It is about the surrounding infrastructure (roads, electricity, water, labor, security, etc.) and about the social and societal strata in the place where the work begins to operate, including impacts of expected synergies (e.g., other clashes between existing and planned projects according to by a national and local Masterplans).

The DP 3 are measurable and verified (based on the result in specific data space in a time-synchronous environment with the environment they enter). These are data with the ability to absorb new stimuli from AI into informal relationships, characteristics of low-income areas (e.g., criteria for the value of acquired assets, criteria for bank loans, precedents for litigation on a local, transnational scale, etc.).

The DP3 has the power to bring input data for national and local planning operations (investing or only organizational). To support indicators building and maintenance for more comprehensive applications of new administrative technologies (e.g., Blockchain and Smart Contract technologies and their machine applications for a broader utilization scale by local organizations and public and private project investment).

The DP3 data in connection with the DP1 and DP2 forms a base for consolidating the DP4 and DP5 data. Consolidation of data in this sense means linking the data from the preparation, construction processes, and data from the ex-post evaluation into a massive whole with the necessary informative value.

Such consolidation is attractive for continued digitalization and more deep machine involvement in local administration and project management. The SPC Concept proposes that the individual SPC Utilities be responsible for the quality and timeliness of these methods. 

DP 4 (Data Pool 4)

DP 4 creates a database of the operational phase of the project (values ​​obtained by completing the project, e.g., objects such as industries zones, factories, residential areas, roads, hospitals, schools, etc.). At this stage, the project has changed into a physical asset.

All functions inserted into the project (as paperwork) are now in operation (as a processed material, jobs, business, and needed resources). It is not always the case; however, we can ignore such deviations on the general level of the DP4 (mainly if they are recorded in the project appendix).

DP4 has a specific mission and works in a virtual environment with independently set capabilities for its timeliness and sustainability. DP4 records and stores data related to operational functions during their transformation (modernization) and changes (reconstruction) so that the current (actual) owners of the assets perform them at the time of their responsibility.

The mission of DP4 is the long-term and sustainable security of the database, which monitors the usability of the acquired value by the project and provides data for evaluating the ecological and safety suitability of the work in the given time in the given area (in the framework of the SPC Utility and SPD Drivers jurisdiction).

DP4 provides inputs (necessary assurances) for the results of analyzes related to the development of Data Project Management (DPM) applications, for the information of current knowledge and practices in the field of Data Engineering (DE) and Data Science (DS) for universities and other local workplaces involved in the strengthening of vulnerability and resilience of the territory of a province.

DP4 supports the quality of projects related to the modernization and reconstruction of already acquired and used assets in the province (e.g., new technologies dissemination like Blockchain and Smart Contracts or other influencers like innovative technologies or impacts of unwelcome catastrophe). It creates preconditions for creating the necessary data stock (on-site in a section and linking to similar comparable) to stabilize accepted and newly prepared local and national masterplans.

DP4 serves the public and private sectors as a picture (snaps) of development and security (linked to the findings of the comprehensive package of SED, DRR, and HA projects). Helps Local Governance Units (LGUs) better respond to central government mandates, market supply, and threats from climate change, current pandemics, and impending local disputes and wars (linked to the effects of possible migration, excessive poverty, and loss of independence in securing primary living conditions).

DP4 enters the financial flows of the project life cycle in the time zone of modernization, reconstruction, and other changes of the work acquired by the project. It is a time of changes in owners and changes in the technique of life and social ties with impacts on previously acquired works (apartments, roads, parts, factories), which cannot and does not make sense to monitor and predict in this context.

DP4 (and others DP) are opening the fundamental question of ensuring the sustainability of data collection and management in such a big (vast) environment. The answer is: via the "organism" tasks of the SPC Utility. For example, through milestones in the administration of SPC Utility (linked to the Smart Contract principle).

The idea is related to enforcing obligations beforehand known (under sanctions) when the actual need has come. In such an environment, the chain of enforcing obligations must-have machines support the administration of services and generally support the trade growth with data in the investment operations.

DP4 deserves individual attention. Like DP 5 for at least two reasons. The first is that the data in this profile has not been systematically treated, and generally, data are not used in a correct business modus. The second reason is the need to address digital transformation. It requires dealing with changes in habits and vices and how one works with data. Currently, we are only consuming data and do not use it fully. 

It, too, can be accepted as a justification for why current economic and social developments, in particular current organizations, need a broader view of projects and their entire life cycle:

• why we need a new project paradigm (e.g., see figure C5e), 

• why SPC Concept proposes work to set up and master the functions of SPC Utility and SPC Driver in low-income countries.

Exaggerating means - starting with a clean head in the least contaminated environment).

DP 5 (Data Pool 5)

DP 5 is at the end of the project life cycle. It closes the path from ideas, plans, preparation, and implementation, from the project's transformation into a work.

He/she uses the acquired asset, modernizes it through reconstructions, and transforms the functions of the results according to random impulses until its moral or physical end. The end of the project life cycle generally brings comprehensive evaluations and new perspectives. It applies to all projects.

DP5 is ready to gather data on what the Human is preparing for himself, how he values ​​the values ​​acquired through the project, and what impacts the project has had on the Great Triad (GT). 

In summary, DP5 (with a sufficiently large amount of data from multiple SPC Utilities) will begin to create a picture of what Man takes from Nature and Earth, what damage he does, and how he deals with them. In this sense, nothing new compared to today's practice.

DP 5 captures, manages and uses data in the specific Data Lake of the global package of SED, DRR, and HA projects. DP5 is a particular segment of the data funnel of the SPC Utility. DP 5 takes care of the end dates of the project cycle and offers them for further use.

For example, to carry out ecological liquidation or removal of a specific object from the landscape and return the landscape to nature and man. Briefly, the data fill the activities we call Human Care.

DP5 stands for the structure of data acquired by the activities of the SPC Utility and SPC Drivers networks.

It is one of the ways to obtain structured data (in the necessary details and volume), work with them (within a unified methodological framework), and use and evaluate in the flow of preparation and implementation of the following projects.

Again, we could say nothing new; current practice knows this. But it's not true. He/she doesn't know and doesn't do that. The difference is in the availability and richness of the data structure that SPC Concept offers and that it lacks in current practice.

DP5 must contain a data structure and algorithmic operations (like other DPs) for the rapid preparation and implementation of liquidation caused by physical or moral usability or disasters when there is no point in repairing, reconstructing, or transferring the damaged object to new operational functions.

DP5 enters the financial flows of other projects as feedback on the environment at its implementation and as a set (snapshot) of data entering national and global environmental care systems. It answers how to ensure the sustainability of data collection and management through a single SPC Utility and across Data Lake (DL).

At both levels, by distributing commands to provide inputs to machines (today, we would say inputs to a network of computers) using cloud technology.

DP5 (like the other DPs already mentioned) offers a solution. It recalls that the narrow path of data collection and management centralization does not work. And underline that the open approach of decentralization is generally unobtrusive.

It deals with distribution (today very often recommended in other fields as). It can lead to milestones in the administration of SPC Utility and the administration of organizations of various donors (financial institutions, banks, ministries of finance, etc.) in connection with the Smart Contract principle.

DP5 offers a way to integrate the collection obligation (under sanctions) with uncompromising and insolvency disputes in global digital transformation efforts. Machines must collect data, process it, and link it to further machining and trading (up to a specific financial return).