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⚒    Intro    Goal BI    datapreparation    BI omissions    tech focus    What next    ⚒ 👐    top bottom   👐

Design Data - BI, Analytics

information, data: improving orgnsiations BI Analytics.

BI, Analytics reporting.

BI life VI analytics technology bi analytics infotypes The data explosion. The change is the ammount we are collecting measuring processes as new information (edge).

📚 Information questions.
⚙ measurements data figures.
🎭 What to do with new data?
⚖ legally & ethical acceptable?

🔰 Most logical back reference.


Reference Topic Squad
Intro BI, Analytics reporting. 01.01
Goal BI The goal of BI Analytics 02.01
datapreparation Preparing data for BI Analtyics. 03.01
BI omissions Omissions in BI, Analytics reporting. 04.01
tech focus Technology push focus BI tools. 05.01
What next Changing the way of informing. 06.00
Combined pages as single topic. 06.02

Combined links
Combined pages as single topic.
👓 info types different types of data
👓 Value Stream of the data as product
👓 transform information data inventory
🚧 data silo - BI analytics, reporting
🕶 bi tech Business Intelligence, Analytics


dual feeling

The goal of BI Analytics.

The understandable goal of BI reporting and analytics reporting is rather limited, that is:
📚 Informing management with figures,
🤔 so they can make up their mind on their actions.

dashboard classic
Reporting Controls (BI).
When controlling something it is necessary to:
👓 Knowing were it is heading to.
⚙ Able to adjust speed and direction.
✅ Verifying all is working correctly.
🎭 Discuss destinations, goals.
🎯 Verify achieved destinations, goals.
It is basically like using a car.

Building "dwh" for BI.
The present something with BI to managers, data is needed.
Take notice of the omission of the core business value stream.
It is starting the become a technology driven push approach.

dashboard BI
Presenting data using figures as BI.
The information for managers commonly is presented in easily understandable figures.

Using smileys in several levels from happy to unhappy-angry is sometimes asked and seeing as suffciënt.

When used for giving satisfying messages or escalations for problems there is bias to prefer the satisfying ones over the ones alerting for possible problems.

Too complicated and costly BI.
When trying to answer every possible question:
💰 requiring a of effort (costly)
❗ every answer 👉🏾 new questions ❓.
🚧 No real endsituation
continus construction - development.

The simple easy car dashboard could endup in an airplane cockpit and still mising the core business goals to improve


Preparing data for BI Analtyics.

BI datavirtualization.

Almost all data in BI is about periods. Adjusting data matching the differences in periods is possible in a standard way.
The data virtualization is on the "data vault" dwh 2.0 dedicated build for BI reporting usage.
It is not virtualization on the ODS, or original data sources.

This is en very affective way on existing build dwh 2.0. The limiatations are that is not well suited withr different goals of analytics using other data assumptions.

Differences BI reports - Analytics ML.

BI Analytics reports.
BI Business Intellgence has for long claiming the patent of being the owner of the E-DWh.

The Dimensional and the Data Vault for building up some storage as singel source of the "truth".

The "truth" is defined as the dwh no matter what has left out by inconsistency or being missed to get complete or modified by assumptions.

No testing and validation processes being necessary as nothing is operational just reporting to managers.

OLap modelling and reporting on the production data for delivery new infomation for managers.

The biggest change for a dwh 3.0 approach is the shared location of data information being used for the whole organisation, not only for BI.

Applied operational Machine learning (AI).
Analytics, Machine Learning, is changing the way of inventing rules to only human invented to humans helped by machines.

When only a report research made once is the goal the long way of waiting on data deliveries of the old dwh 2.0 methodology is acceptable. Acceptable when: are acceptable.

Using a more direct approach with a denormalization of operational data, at least two data streams are needed: Those are semantically very close to be able to test validate and promote and release models easy seamlessly using the same generated code.
The biggest change is the ALC type3 approach. This fundamentally changes the way how release management should be implemented.


Omissions in BI, Analytics reporting.

Securing information
As the goal of BI Analytics was delivering reports to managers, securing informations and runtime performance was not relevant.

Using BI analytics in the security operations center (SOC).
This technical evironment of bi usage is relative new. It is demanding im a very good runtime performance wiht well defined isolated and secured data.

ETL ELT - No Transformation.
Transforming data should be avoided.
The data-consumer process should do the logic processing.
Offloading data, doing the logic in Cobol before loading, is an ancient one to be abandoned.
idea lightbulb
Logistics of the EDWH - Data Lake. EDWH 3.0
Processing objects, information goes along with responsibilities.
❗ A data warehouse is allowed to receive semi-finished product for the business process.
✅ A data warehouse is knowing who is responsible for the inventory being serviced.
❗ A data warehouse has processes in place for deleivering and receiving verified inventory.

CIA Confidentiality Integrity Availability. Activities.
CSD Collect, Store, Deliver. Actions on objects.

The two vertical lines are managing who´s has access to what kind of data, authorized by data owner, registered data consumers, monitored and controlled.
The confidentiality and integrity steps are not bypassed with JIT (lambda).

There is no good reason to do this also for the data warehouse when positioned as a generic business service. (EDWH 3.0) In a picture:

handy tool

Technology push focus BI tools.

DBMS changing types
Once upon a time there were big successes using BI and Analytics. The success were achieved by the good decisions, not best practices, made in those projects.
To copy those successes the best way would be understanding those decisions made. As a pity these decisions and why the were made are not published.
The focus for achieving success changed in using the same tools with those successes. dbmsstems_types01.png

Just an inventarization on the tools and the dedicated area they are use at: Mat Turck on 2018 , bigdata 2019 An amazing list of all,kind of big data tools at the market place.
2019 Matt Turck Big Data Landscape

 horse sense

Changing the way of informing.

Combined pages as single topic.
Combining the datatransfer, microservices, archive requirement, securtiy requiements and doing it like the maturity of physical logistics goes into the direction of a centralized managed approach.

valuestream Reuse of standard solutions in a standard way has always been promoted as better and cheaper.
Why would we not do that for: EDWH-3.0 (?) -- Connect that to the value stream of the information.

Combined pages as single topic.
Combined links
👓 info types different types of data
👓 Value Stream of the data as product
👓 transform information data inventory
data silo - BI analytics, reporting
🕶 bi tech Business Intelligence, Analytics

🔰 Most logical back reference.

⚒    Intro    Goal BI    datapreparation    BI omissions    tech focus    What next    ⚒ 👐    top bottom   👐
📚    BPM    SDLC    BIAanl    Data    Meta    Math    📚 👐 🎭 index - references    elucidation    metier 🎭

© 2012,2019 J.A.Karman