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BI Business Intelligence - Analytics


why bi BI Business Intelligence is very wide area within Information Technology.

The word BI was Hyping and now being replaced with Big-Analytics.

Many suppliers are active on the market.
They are not always clear in their relationships.



BI basics



Why BI


complex

Governance


Reporting, Analyzing, Forecasting are key factors of BI (Business Intelligence).

Analytics and Big Data as the new hyping words. >

Data mining, text mining , operational research are more difficult to understand.

save light

financial regulations

Ever wondered why so much has gone wrong within financial services?
There have been regulations developed, when reading this these must be missed by the banking-crisis started with the wrong qualifications of loans.

Financial securities My glossary (gathered references)

save light

health care regulations

Health care My glossary (gathered references)

weakest link

IT disasters related to BI

Spreadsheet horror stories (link as part of mistakes & abuse of statistics) Collection of Generic failed operations (link to gathered collection)




wiki dwh bi
dirsign

Classic BI, Business Intelligence

The dwh (DataWareHouse) concept was designed in the time computers did not have that much capacity. IT was (capacity) and still is (responsiblity) important to segregate the operational process from decision support systems.
According to the pciture it is always about dataprocessing in several steps, enriching the data, quality validation and tranforming it.
The way primary operational process is used is requiring a quite different approach of how to handle and store the data at a DWH. Normalized forms (third) of an DB are used at operational processes, eliminating duplicates as much as possible.
Cubes Olap Star-schema are used at dwh level. Having many duplicates to get fast easy views.
A common pitfall is to use BI-tools in the primary operational process becaus of the nice look and feel or the way of easy to use.

Data Ware Housing My glossary (gathered references)





Data Mining, Operations research, Forecasting, Business Intelligence, Statistics



Why BI (1) reporting
legal
Business & security
The security to business data can´t be the same as the normal segregation ion operational processes.
Of course it must be facilitate the normal conventions with the segregation of duties.

Some reasons are:



Why BI (2) analytics
modern

Analytics, Big data


Analytics:
see why BI. It is also visualisation forecasting.. optimization
seven dirty secrets data visualisation (netmagazine feb 2013)
Big data:
It is analyses of Business Data that is the main source to analyze.
Just having a database in not enough to do analytics.
big datas management revolution (sept 2012)
predictive - modeling
It is analyzing all kind of data own busienss external data, gis, to get to some predictions by models. Data Mining Group is a consortium with an exchange standard (pmml).

This is the area of all trends in positive and negative. Customer behavior (churn rate) to dedicate proposals. Stock market predictions with profits or losses by mistakes.




oldtime

Degradation of BI

In the beginning of usage of BI (Business Intelligence) it was assumed as the next thing solving everything. As the promise is used by marketing effort even the most simple report-listing is classified as BI.
The originally goal of well defined information to based major decision on is lost. So a new name buzz-words must be introduced.

These are: Analytics and Big-Data.

Type of analytics can be different.
The GPU (CUDA DirectX) ia having a lot of capacity measured within flops. (Floating operations- calculations). CUDA is a different stage of analytics compared to analyses of databases. Cuda can be used to do Matrix computations, it can´t be used to analyse databases.

In the analyses of data you mostly have to deal with strings not only crunching floating point data.

The degradation of BI, has been evolved, so the lifting of new Buzzing words. Why this change?



BI 2.0

modern
Renew by versioning
A common try out to renew is the addition of version number.

archive
OLTP
The OLTP and SQL are classic ways of Storing data. A DBMS (DataBase Management System) is a common used tool.
Online_transaction_processin (wiki)
Is meant to deal with a relative very small number of updates and retreives, should perform well. A common approach is:

With the goal of better performance the Partition_(database) (wiki) is done.
Still a DWH used to retrieve all data often in a predefined sequentially way does not fit the requirements an OLTP DBMS was designed for.

archive
DWH (nonOLTP)
An other the get data better served is searched in the way of storing data. Emerging “vertical” database systems in support of scientific data (2008) (http://www.cwi.nl/)
Keeping the variables as close as possible together in the storage.

archive
OLAP (nonOLTP)
  • Online_analytical_processing (wiki)
    With predefined grouping (dimensions levels hierarchies) of real numeric variables (mweasures) is having the option of pre calaculation of some basic simple stochastic statistics. Aggregations like: N (count), Mean/Sum, sum of squares.
    With the grouping an explosion of the needed storage can occur. With the same implemetnation the performance of retreiving and drill down is able getting tremenously improved.


  • modern
    NoSql
    The DWH and SQL is getting out of the hype. Instead NoSQL and optional direct access to all kind of data.
    modern
    In database
    In-database_processing (wiki) Today, many large databases, such as those used for credit card fraud detection and investment bank risk management, use this technology because it provides significant performance improvements over traditional methods

    Technical details SAS in database (documented at SAS opertional life chapter)


    modern
    Modern BI
    BI modern

    Thinking about modern BI is also changing.

    modern
    Big Data
    BI modern rosebt
    Thinking about modern BI is also changing the way of storing data. Buzzing -- Big data big-data-vendor-landscape (rosebt 2012/6)



    BI generic    Business struggle BI    Business Analysts    top bottom

    Business Struggling BI - Analytics



    Business view - IT strategy

    BI battles

    The battle:

    IT:
    internal standards
    support staffing
    historical grown knowledge
    hypes buzzing

    Business:
    efficient work
    compliancy
    market position
    Profits
    clock

    Business view

    Struggling arround with IT as not knowing to translate their MIS requirements to BI Solutions.

    dual

    IT Staff - Business

    The way of communication: The goal of BI, Analytics, was your own business continuity and optimization.
    2012/10/11/dont-fire-your-analyst (forbes)

    agree

    Business - real customers

    The goal Business is having customers and get some profits. A well running business having al lot of both.
    building great customer experiences (beyondphilosophy blogs)





    complex

    IT Technical view:

    Data integration
    Common blocking by requiring to copy the data to own maintained systems (file transfers)
    Duplicating the business data. Not knowing anymore te real information
    Security officers
    Common blocking issue by the approach: when not understood it is not allowed.
    DBMS – DBA’s
    Blocking the free way (DDL / DML / MDL ) of data creation by analysts
    Just knowing the SQL approach
    DTAP
    Commonly not in place or even blocking by conflicting technologies
    Scheduling, Publishing Channels & More
    Olap - dashboards
    3 reasons to hate BI dashboards (zdnet aug 2012)




    Business data - value strategy

    Big data CEO view

    clock

    Business information papers


    lef CSC:
    There was a good presentation about Data_rEvolution: LEF_2011Data_rEvolution.pdf The changes on the market is very nice presented.
    papers The "shared nothing architecture" to be understood by the seti@home example. Not all data - information processing will be suitable for this approach. Even better where the questions you should have asked.

    dual

    Big data a blog

    like:

    dual

    links references




    papers

    classic ETL as history

    emcien
    Common sense is staying important. Emcien is focussing on manufacturing lines. Organizations around the world are unknowingly under-utilizing their most important asset: data. Most organizations store and manage this data in departmental silos, so garnering a complete view requires teams of analysts pouring over spreadsheets containing data that is already old. To gain a holistic, real-time view, the silo walls must come down. Herein lies the problem – and opportunity – of Big Data.
    3-reasons-why-data-mining-is-dead



    BI generic    BI Tools     Business struggle BI    Business Analysts    top bottom

    Business Analysts , Data scientist

    why bi The management language, touching Bi is at:
    Six sigma , abc


    Role Jobs - IT strategy

    brains

    Data Scientist

    The Nature of Big Data and the Skills of Data Scientists (smartdatacollective.com dec 2012 Ling Zhang)
    The job title Data Scientist was invented by DJ Patil and Jeff Hammerbacher when they tried to name people in their data team who work on big data and they did not want to limit people’s functions because of improper job title like business analyst or research scientist Building Data Science Teams...(see the book)
    Data Scientist at work






    brains

    Older functions Data Scientist

    The Information analys, The Business analyst are some names of the same kind of work in some area.
    The statistical analyst, actuary are other parts
    brains

    TCO, Total Cost Ownership

    Managing the Total Cost of Ownership of Business Intelligence (SAP WP, Dr W. Applebaum 2010 For many companies, total cost of ownership is out of control. And the problem is growing, fueled by ever-increasing demands from the user community, massive new sources for data, new capabilities, shadow IT landscapes, and the cost of keeping people abreast of all the changes.


    brains

    dwh Mid sized Business






    Institutes, Consultancy , Expertise


    link name
    iiba International Institute of Business Analysis
    idg International Data Group (IDG)
     
    bigdataq ( jan 2013)
     
    big-data-buzzwords-from-a-to-z (CRN nov 2012)
     
    wiseclouds webcat exploring the database forest (Ziff david jan 2013 )
    link name (dutch)
    businesssoftware ictinformatiecentrum.nl
    datamanagement-en-leiderschap (cio.nl) opinie onderdeel van idg
    marqit.nl   marqit.com expertise consultancy
    business analytics A hidden SAS site (dutch)
     
    iiba NL 28-022011 surprising related is RVS (NN ING). I am insider.
    Eureka! Of de vrijheid om ontdekkingen te doen (business-analytics.biz)
     



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    © 2012 J.A.Karman (21 apr 2012)