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jakarman - ICT - My profession Experiences

My early working years

IVO (ISS, Individual Sales Support)

📚 The business goal was delivering pc´s to the sales people (600) at their home having all the information they needed for their customers (1984).
🎭 The avaibable technology was: What was done:

Performance & Tuning, Mainframe

old days computer 📚 The goal was setting up, delivering Management Infomation (MIS EIS) by applications and tools in system resource usage.
Multiple goals: 🎭 The avaibable technology was: Having done:

JST, a Generic approach for automated testing

📚 It is automating manual work of several IT-staff lines.
This is a very unusual part of IT optimization as it is internal IT. This is a solution I implemented around 1996 and is was still used in 2011 (unchanged).
Still after all this years is considered to be a very modern approach.
Experiences: 🎭 Used technology and limitations: The solution design and realization:

Database IDD - IDMS DB/DC

This was using an advanced approach using Integrated Data Dictionaries (IDD's). The more modern word would be Metadata Databases doing some kind of Masterdata management. integrated backend (DB Database) and frontend (DC Datacommunication) tool middleware system. Being executed
storage cabinet
These dictionaries had to be setup in a master (sys:11) and based from that master a logical DTAP segregated configuration (sys:66 sys:88 sys:77 sys:01).
Each of them having definitions: The supplier Cullinet was positioned as being prefered. In previous years the front end was not available from the supplier. An in house build front-end middelware system was still running (VVTS).

📚 Goal operational support (system programmer) in a small team setting.
🎭 Used technology: suppliers Cullinet later CA. and the IBM toolset.
What was done:

Security (ACF2 RACF - MS AD )

Bringing Access to resources into roles, organizing who is allowed to what role and getting to is that person realy who is saying he is, is known as security. There is a master security administrator task (designing) and a restricted security amdinistrator task (execuring requests) for the required segregation in duties.

The old common approach is doing input validation before handing over to the tool by lack of integrated security. Roscoe was an multi user mainframe program editor. The roscoe adminstrator tried to implement security by parsing and than reject or allow the command. It never succeeded to became reliable (1985). In modern times we are parsing code (preventing code injection) trying to secure browser-usage (2020).

📚 Goals implementing security with tools:
🎭 Used technology: suppliers "ADR" later "CA", IBM, Microsoft (AD).
What was done:

Scheduling (UCC7 TWS - homebuild)

lost in meaning
Scheduling Jobs in the old times was all manually hard work. Operators had to carry all the physical paper cards in time to the readers to execute them. Planning was done as prepartions on paper using previous experiences ont load and durations. The operator was the person you sould be friends with.

This all changed when that work became automated and shifted for some parts to persons inside the business (functional support, prodcution support).
There is big shift in time impacting jobs. Many kind of jobs have gone and replaced by others in te era of applying computers AI ML.

📚 Goals:
🎭 Used technology and limitations: The realization being adjusted in level of acceptance at departments.

👓 intro   contact 👓-⚒ early years   big company    hired role ⚒-🚧 references 🚧

Being insider at a big company

Operational Risk (OpRisk),

lost in meaning This departments and the work on their support is interesting. OpRisk is doing things like Advanced Measurement Approach. Monte Carlo simulation modelling with public known situations is the way to go.
It requires releasemangement, full testing (including DR) and security policy alignment. At the moment of the delivery deadline (quarterly) it is a critical proces.

📚 Goals
🎭 Used technology used and limitations: Having done:

Hosting, multi tenancy - *AAS - stacks

Software As A Service (SAAS) is a great idea. Implement it by yourself if your requirements are more strictly then can be fulfilled with ´SAS on demand eg cloud services. Dot it by yourself when you are you are big enough to do a SAAS implementation yourself. Another reason can be having multiple buisness lines (tenants) needign tehe same solution teh same application.

Sharing computer resources can bring huge benefits. There is a whole industry based on that. When doing it in the wrong way the possible risks are also high.
Solving the SAS environment challenges with all my knowledge and experience it is brought to a much higher level as is common practice.

📚 Goals
🎭 Used technology doesn´t really matter, it is about dependicies in the stack.
Having implemented:

Release management (versioning)

lost in meaning Within the Information technology guidelines and technics are evolving fast. Recent hyping tools like GIT are getting the most attention.
A generic DTAP Approach DTAP - seeing the leveled three layers is far more important.

Being in a silo you are having just one layer, your own layer.
Doing middleware support you are seeing three layers.
📚 Layered DTAP Release management
🎭 Used technology don´t really matter as long goal release management be met.
💣 That goal alignment being experienced in practice as a problem because the goal often got lost and replaced by a tool implementation.
Having done and being involved with:

DWH, BI, Data connections, Unix

In a growing enviroment this topics became the only working area.
The first problem to be solved was a generic desktop roll out for SAS cliŽnts as the desktop got another standard.
The next one was adding and consolidation of midrange servers using SAS (see hosting *AAS stacks). This was a Unix environment (AIX) using a SAN (not NAS). Is has not very much differences in the approach compared to Linux. It makes to set of having experiences with operating systems complete.

📚 Supporting DWH BI Goals:
🎭 Used technology: Implemented:

Policies - Sox-404 ITSM Cobit IEC/ISO 27001, 27002 - GDPR

modern datacenter Policies standards are becoming mandatory (legal requirement), but are having a lot of documental work to do.

You need to know their goals and than how to get to implement those. That is the top down way.
The technology is having a history of doing hings some way. External suppliers have their own "best practices", sometimes they are very bad practices. That is a bottom up way.
There is need for agreement and archiving on what and how to do it in the own organisation.

The mentioned guidelines are starting points thre are many more of them.
📚 Goals
🎭 It is about compliant processes, not the details in technology.
Having worked on:

Data mining, Customer Intelligence (CI)

Data mining, data science is hyping in 2016. The department CI was in my early years (1990´s) one of the busienss lines to support with tools.
Cross selling, customer segmentation, churn rate and more are words they are using. The developmnent being indicated with the word modeling, operational usage of a model using the word scoring. That is using another language to communicate for known processes.

Marketing. Customer Intelligence is commonly using more data sources than are available internally. Geo locations, external open and closed data for inputprocessing bringing into correlations with internal business processes.
Bringing these marketing operations into departments executing the normal classic mass operations is a challenge. The "Analytics Life Cycle" (ALC) is not settled yet.

Customer Intelligence (CI), data mining:
📚 Goals
🎭 Used technology: Having done:

👓 intro   contact 👓-⚒ early years   big company    hired role ⚒-🚧 references 🚧

Hired role, limited periods

ML, Machine Learning, Scoring, Explainable AI (I)

The environment is still sensitive information (2016). A generic description.
📚 Goals:
🎭 Used technology used and limitations: The solution design and realization:

Grid computing - performance load balance

This is a hot topic for performance reasons of business solutions.
modern datacenter The Free Lunch Is Over: A Fundamental Turn Toward Concurrency in Software Grid computingframe (Herb Sutter) Applications will increasingly need to be concurrent if they want to fully exploit continuing exponential CPU throughput gains Efficiency and performance optimization will get more, not less, important
Scaling up:
📚 Goals:
🎭 Used technologies: Solutions and realization: Scaling out:
📚 Goals:
🎭 Used technologies: Solutions design and realization:

ML, Machine Learning, Scoring, Explainable AI (II)

The environment is sensitive information. I can share some approach details.
📚 Goals:
🎭 Used technology used and limitations: The solution design and realization:

XML messages, object life cycle data model

Data modelling is a confusing challenge. The requirement is undertanding how information is delivered and what information is needed. These can be complete differnt worlds of context.
translation communication
The classic dwh approach is based on modelling very detailed elements optimizing the transactional dtabase proces and saving as much as posible storage. The disadvantage is the complexity in relationships.

Blockchain is a hyping approach (2018) for archiving and processing al detailed information and all history in chained blocks (ledger).

In practice contracts, legal agreements, are describing fully the most recent situation. Their history is only in special cases relevant. Those special cases are the most interesting ones when a goal is able to detect illegal activity or fraud.

Use case "real estate":
📚 Goals
🎭 Used technology used and limitations: The solution design and realization:

Dwh & datalakes, monitoring, Privacy by design

Storing objects for use at some later moment is warehousing. Just collecting a lot of things not knowing whether you will used it, is another approach.
The datawarehouse (Bill Inmon 1970, Ralph Kimball 1996) is not having the same goal and same functionality as a physical datawarehouse. In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments.
What it is describing is the fucntional equivalent of a quality assurance laboratorium and / or a research laboratorimum.
💣 The words "datawarehouse" and "data lake" are confusing in their associations with their physical counterparts

Using the datawarehouse, data lake in line with the physical counterpart.
📚 Goal - change in thinking
🎭   ⚙ Solution and realization : The analytics usage BI (Business Intelligence) descriptive, ML (Machine Learing) and others are just customers of a warehouse like any other operational usage. Being another level of "How to do things" these are the not visible parts in running those other projects.


Patterns are basic building blocks and shoud match some issue to solve. ordering patterns
While working on several projects there is a statement "out of the box" predifined transforms are mandatory used.

In reality those "out of the box" steps are lacking some really essential logic (2017).

Several examples of not well suited "out of the box" transforms:
📚 Logical Issues to solve:
🎭 Used technology: SAS DI (ETL ELT tool) but generic designthinking.
Solutions, user generated transforms:

Missing link

jakarman - ICT - My profession profile

👓 click returning to profile.

👓 intro   contact 👓-⚒ early years   big company    hired role ⚒-🚧 references 🚧
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