📚    BPM    SDLC    BIAanl    Data    Meta    Math    📚 👐 🎭 index - references    elucidation    metier 🎭
⚒    Intro     Why Bi&A (I)     Why Bi&A (II)     BI_proces     ANA_proces     What next     ⚒ 👐    top bottom   👐

Design BiAnl- Business Intelligence - Analytics

Improving processes - change transitions

Explaining - predicting, processing operations.

BI life The world of BI and Analytics is challenging.
The Application Life Cycle model2 is underpinning decisions for in the boardroom. To know what is going on and what is needed, distance on details is required.

🔰 Too fast .. previous.


Reference Topic Squad
Intro Explaining - predicting, processing operations. 01.01
Why Bi&A (I) Why BI & Analytics (I). 02.01
Why Bi&A (II) Why BI & Analytics (II). 03.01
BI_proces Business Intelligence Proces. 04.01
ANA_proces Analytical Proces. 05.01
What next Inventing change Transformations. 06.00
Following steps. 06.02


THis chapter is partially fresh, partially converted. Topics are:
dual feeling

Why BI & Analytics (I).

Informing the decision makers (business).
Supporting Business proces optimization.
Business Intelligence (BI), Management Information System (MIS), Executive Information System (EIS) are alle the same, just using different words.
The goal is only informing management with figures so they can make their mind up with what do in the future.
The spreadsheet use by mangement doing the analyses for decisions.

At other layers "BI & analytics" is used: BI & Analytics is not a "one size fits all" solution.

Informing by descriptive reporting.
In the years before BI was normal, the standard reports were delivered as being part of the proces job log. The reports being printed on paper were archived for a long period, requiring a lot of space and cabinets. Extracting those numbers, figures, was later done by archiving those prints in an electronic way (datasets). The conversion of those electronic datasets was the first dwh being build. The spreadsheet being a key enabler replacing most of manual work.
Why Business Intelligence
📚 short list:
* standard
* ad hoc
* drill down
* alerts

dual feeling

Why BI & Analytics (II).

Informing by predictive, prescriptive reporting.
Analytics, Operations Research (BI) is coming wiht a lot of uncertainties. Monte Carlo simulations being optional.
This is augmenting reporting, doing extrapolations, regresions in probablities what could happen.
Why Analytics, Machine Learning
short list:
* statistics
* forecasting
* predictions
* optimizations

Analytics and Bi differences

Different type of BI&A usage:
  1. Operations using BI tools
    No preparation on what is needed neither evaluation what has done.
    Focus: running the operations.
  2. BI, Business Intelligence (dashboard-report)
    📚 Information is being gathered wiht a discussed goal to achieve.
    ⚙ ⚒ Data is being processed. Prepare for the goal.
    🎭 Results are evaluated, planning what do next.
  3. AI, BI enhanced with analytical tools (dashboard-report)
    ⚖ ⚙ Information is being gathered having no clear goal to achieve.
    📚 ⚒ Modelling - analyzing using data, to find new unknown opportunities.
    🎭 Results are evaluated, planning what do next.
  4. AI, ML Machine Learning, operational scoring
    ⚖ ⚙ Information is being gathered with a discussed goal to achieve.
    📚 ⚒ Modeling - analyzing data for the best approach.
    🎭 Results associated wiht tested models are evaluated.
    Data is processed using accepted chosen models.


Business Intelligence Proces.

BI&A being software development.
Running BI &mp Anlytics is using ICT processes, one of those the SDLC.
More links associated - entry/exit
Is used at:
👓 Release management SDLC - release management.
👓 Details multiple layers (SDLC sub-page)
👓 The full ICT Business Pyramid. (SDLC sub-page)
Details to be found at:
👓 Data Information Flow.
👓 Meta data Naming - versions.

ALC model2 BI&A.
Business Intelligence Proces
In this infographic flow:

Analytical Proces

Automatization of decisions within predefined settings and predefined limits of variation. Any automated decision has to be explainable and able to possible be correct by human intervention. (profiling GDPR)
analytics proces
In this infographic flow:

Inventing change Transformations.

handy tool
Using Bi-tools
💣 The tools originally reserved for BI only use have become far more generic. With the type of BI&A usage there is already a list of four different type of users a "datawarehouse" - "data lake" should able to provide.

Inventing change Transformations.

💣 Analysing data is requiring real operational production data. Building decisons on faked information would generate very wrong results. Whether it is basic analytics doing reporting or automatized ML it is =level 3= (orange).

EMC - Big Data infographic (2013)
A nice review on this, "The big data journey rivisited" Bill Schmarzo 2016.
emc big data storymap

Following steps

Missing link

These are high level considerations.

Intermezzo 👓 for what I couldn´t classify in the six basic topics: bpm sdlc bianl - data meta math.

What is not there are: 👓 the details how you do it.

⚒    Intro     Why Bi&A (I)     Why Bi&A (II)     BI_proces     ANA_proces     What next     ⚒ 👐    top bottom   👐
📚    BPM    SDLC    BIAanl    Data    Meta    Math    📚 👐 🎭 index - references    elucidation    metier 🎭

© 2012,2019 J.A.Karman