Design Meta, governing data

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📚   BPM   SDLC   BIAanl   Data   Meta   Math   📚
⚖   Intro   Info-101   Viewpoints   modelling   securing   What next   ⚖

Contents & topics Data governance

ICT at an organization by layers.

Mindmap master data devops meta design data design math devops data devops math 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


Business demo J.Dietz

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.
  1. Strategy goal: transport of person(s).
    • From ocation A to location B.
    • Applicable transport option: a car.
  2. 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.
    • ymap sdlc Wanting to use functions:
      wheels (includes steering),
  3. 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.
    • ymap sdlc Creating and maintaining:
      wheels (including steering),

  4. ymap sdlc
    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:
    The definition of data quality depends on the customer for the interpretation.

Data Perspectives.

ontological PDCA cycle
PDCA cycle redefined as ontology
There are three main power lines in the enterperise. The processes circle is using four quadrants. 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:
pyramid top down pyramid bottom up
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:
revZ lifecycle Z lifecycle
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.

Logic in understanding of data.

Deployment - Release - Versions
More links associated - entry/exit
Is used at:
👓 threats for data & tools Proces Life Cycle.
👓 Release management SDLC - release management.
👓 Business Intelligence,Analytics .
👓 Data Information Flow.
Details to be found at:
👓 Math Software engineering.

Describing data - information
data_admin01.jpg Understanding the logic in information is requiring:

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.

Data Controller
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.

Data Processor
Forgotten at security design: the infrastrcuture, high privileged roles.
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.
SAM (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.
DMBOK segmentation

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
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)

Mission: 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


Following steps

Missing link design math design data devops data devops meta devops math
These are design meta modelling concepts, others:

Theoretical Math, 👓 next
bianl previous, generic data processing.

Others are operational realisations: 👓
data meta -& security in practice- math

⚖   Intro   Info-101   Viewpoints   modelling   securing   What next   ⚖
📚   BPM   SDLC   BIAanl   Data   Meta   Math   📚

© 2012,2020 J.A.Karman
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