Design Meta, governing data
Contents & topics Data governance
ICT at an organization by layers.
Metadata, what is in it?
Just having data, there are a lot of questions to answer:
📚 Information data is describing?
⚙ Relationships data elements?
🎭 Who is using data for what proces?
⚖ Inventory information being used ?
Too fast .. previous
| Reference || Topic || Squad |
| Intro ||ICT at an organization by layers. ||01.01 |
| Info-101 ||Enterprise Ontology 101. ||02.01 |
| Viewpoints ||Data Perspectives. ||03.01 |
| 👓 data-proces ||deep dive proces data layers ||03.xx |
| modelling ||Logic in understanding of data. ||04.00 |
| 👓 Data-Admin ||Describe Data, Data Administration ||04.x1 |
| 👓 model-data ||Data modelling ||04.x2 |
| securing ||Data - Software, Security Access, SAM. ||05.01 |
| 👓 access-security ||Deep dive modelling security ||05.xx |
| What next ||Change ICT - Transformations ||06.00 |
| ||Following steps ||06.02 |
- 2020 week:40
- Adjusted page with Demo ontology.
- Aligning better with the SDLC page as strongly related.
- 2019 week:18
- starting to get the page filled.
It will be similar to sdlc als the life cycle processes are similar.
- The different approach to "data" is a result of following SAME, not DMBOK. Content filled chronologically counterclockwise.
Enterprise Ontology 101.
There is a claim of a "single version of the truth" for describing something what is going on for achieving a goal.
The problem is several people are having a different perspective on the goal an the context of actions.
Multiple interpretations of an element
This is a different understanding in metadata, ontology. In a document dated 2006 enterprise engineering J.Dietz an example is given.
- Strategy goal: transport of person(s).
- From ocation A to location B.
- Applicable transport option: a car.
- Car driver goal: using a car to be able to go from A to B.
- Needing information on useable roads.
- Expected behaviour of the car.
- How to avoid unwanted situations during trnsport.
Wanting to use functions:
wheels (includes steering),
- Car technician goal: having the car workable for the driver.
- Adjusting technical impementations as far as possible on requests by te driver.
- Only the way it should behave explaining to the driver.
Creating and maintaining:
wheels (including steering),
VMAP - DTAP dependicies
Realizing a DTAP implementation using the VMAP. (👓 click figure)
Consideration: Agile project planning.
Note the SDLC similarity. Good alignment of the lifecysles is needed.
⚠ The word data is used diffuse.
A more clear classification is:
- strategy, data content: information - content
- tactical, data context: quality, relationships, meaning.
- operation, data storage: DBMS, Warehouse, data lake.
The definition of data quality depends on the customer for the interpretation.
PDCA cycle redefined as ontology
There are three main power lines in the enterperise.
- Strategy (blue).
- Operational Business processes (red).
- Changing business processes (green), initiated in accordance to strategy.
The processes circle is using four quadrants.
- I, make an inventory of new proposals from a existing processes
- II, preparing new proposals
- IV, realising the proposals
- III, Implementing changes as new processes and execute those
Combining those in a single picture is complicated. An attempt using parts of others of the mind map, process cycle and data ontology is given.
Running, Maintaining - Developping Building
Before you can build something you have to organize the working force.
There is a reversed order, starting at the bottom it looks like:
Building, starting at the bottom (right).
Organizing the workforce, starting at the bottom (left).
Building a ICT system is more easy when it is an new one. Maintaining and changing whats is operational is more challenging.
It is like:
Maintaining, starting multiple at the top ltr (right).
New System, starting at the top ltr (left).
Details on perspectives dimensions, 👓 click one of the figures.
Starting Left to right (ltr) working to an end.
- The two Infra (Infrastructure green) lines are being joined although there are important differences in their life Cycle, release management, behavior.
- The Analytics line (orange) is left open for the choice of human algorithmic decisions in the proces "model-2" or rolling out in a robotic proces "model-3".
- business line (blue) is in the diagonal, always having areversal in time somewhere (requirements realizations).
Logic in understanding of data.
Deployment - Release - Versions
Describing data - information
Understanding the logic in information is requiring:
- basic elements artifacts and objects are labeled, described, correctly in a way users of that have the same meaning.
- relations between artifacts objects are correctly described in a way users of that have the same meaning.
- descriptions and relations documetns must be easily realized in tools so users can access and review those with good experiences.
We are putting anything into coantainers, finding back what is in a dedicated container requires adequate labeling.
Details , 👓 click figure
Inspiring, mamazing things like Delphi oracle´s could happen.
Important support for decisions, not being explainable neither accountable faded away in history.
Data modelling relationships
Data modelling was born in the busines intelligence tool usage. This has the technical and political history of building it up without alignment to the enterprise data information.
Seeing data and information as an &enterprise asset" it should support the entrprise not only analysts for manually inventing process improvement.
A shift to new data models is started by applying machine supported process improvement into using machine supported inprovements into operational use. The shift from ALC type2 to ALC type3.
Data lake, DataWareHouse the basic functionality, receiving items, storing items, delivering when needed, nothing more than that.
Details, 👓 click figure for data modelling.
Data - Software, Security Access SAM.
Using the segregation in: "data controller" and "data processor" following the GDPR.
Security starts at the top of the organizational hierarchy. They are responsible and accountable.
A common misperception is that information securiyt is an ICT issue.
ICT is processing the data not being in control - lead of the organization.
The "Data Controller" abbreviation DC has very little relationship with "Data Center".
Going for a position in an organization an person can get several roles.
Note: A position is not the same as a role. Every role has responsibilities, getting their realization in business applications and the connected uses technology.
The proces of boarding persons is controlled by the HR staff being ordered by line management. Operational staff is executing wat HR staff is ordering.
Note, ICT delivery: tools, documents how to achieve security business requirements.
Forgotten at security design: the infrastrcuture, high privileged roles.
- the many administrator functionalities.
- service accounts needed for system processes.
- testaccounts simulating intended business users fucntions.
Within the SDLC this should take care for.
The "Data Processor" abbreviated as DP has very little with "Design Professional".
Security information model
Details , 👓 click figure for modelling the relationships and building realizations.
SAM Software asset managment.
(wikipedia) Part of controlling who uses what kind of software / tools.
Software asset management (SAM) is a business practice that involves managing and optimizing the purchase, deployment, maintenance, utilization, and disposal of software applications within an organization.
According to the Information Technology Infrastructure Library (ITIL), SAM is defined as "?all of the infrastructure and processes necessary for the effective management, control and protection of the software assets?throughout all stages of their lifecycle."
Fundamentally intended to be part of an organization?s information technology business strategy, the goals of SAM are to reduce information technology (IT) costs and limit business and legal risk related to the ownership and use of software, while maximizing IT responsiveness and end-user productivity.
Change ICT - Transformations
Engineering an enterprise is more than an defining a list of "best practices" of what is usual being done.
DMBOK - segmentation perspectives
- Data Architecture Management
- Data Development
- DataBase operations Management
- Data Security Management
- Reference & Master Data Management
- DWH & BI Management
- Document & content management
- Metadata Management
- Data Quality Management
- Big Data & data science (2nd ed)
Not every segment needs to get filled. Dwh & BI, data quality and data science are not standard processes.
Data security with the idea "solved by the DBMS" is too limited. Software is also data to be secured, that includes the DBMS, Api´s - Entrprise Bus information exchange, (middleware) and the Operating System (OS)
DAMA International´s primary purpose is to promote the understanding, development and practice of managing data and information as key enterprise assets to support the organization.
Rearranging the DMBOK pie.
The focus with that dama dmbok data knowledge is on "data as key enterprise asset".
I will have to rearrange them, conforming the mindmap mapped to SAME (AIM model), into other chapters.
| chapter || Topic || other used words |
| design data || Naming conventions ** || (not seen yet as some standard) |
| - || content management || Information |
| - || data quality * || Available Validity Timelines Reasonable |
| - || document management || retention policy, PIA, BIA |
| devops data || data delivery || Integration interoperability consolidation |
| - || data quality * || Complete Integrity Conistent Accuracy |
| - || data operations ||DBA DataBase Administration |
| design meta || Data model || Relationships, normalization, ontology |
| - || metadata DA || Data Administration ontology |
| - || Security Management * || (Split up) model & control |
| devops meta || data lineage || information-version transformation |
| - || Security Operations * || (Split up) monitor & operate |
These are design meta modelling concepts, others:
Theoretical Math, 👓
previous, generic data processing.
Others are operational realisations: 👓
data meta -& security in practice-
© 2012,2020 J.A.Karman