MIPRO 2005 Presentation Arian

8/6/2019 MIPRO 2005 Presentation Arian

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AgendaAgenda

Where are we today?Where are we today?

7 mortal sins7 mortal sinsQuo Vadis BI?Quo Vadis BI?


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Where are we today?


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Data

BI = going from data to decisionBI = going from data to decision

Data isnData isnt information!t information!

Data + structures =Data + structures =

informationinformation

Information isnInformation isntt

knowledge!knowledge!

Information + analytics =Information + analytics =

knowledgeknowledge

Knowledge isnKnowledge isnt decision!t decision!

Knowledge + human =Knowledge + human =decisiondecision

Structure Analytics

Human

Decis

ion


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Last 30 yearsLast 30 years

Economy

BI applicatons

1970 20051990 2000

IBM Microsoft

EIS, MIS

Teradata, Hyperion,

Seagate, Oracle...

RG, DW, OLAP MS OLAP

BSCIndicators

BSCMIS

applications

Golden age of report generators Dashboard

applications

1980

Costs of base technology

(including OLAP)

Total Quality Management


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Complete IT systemComplete IT system

Data WarehouseData Warehouse

Data Marts

Production applications(ERP, MRP...)

OLAP

Analytical applications

(Olap clients, Data Mining...)

Specialized BI applications(Planning & Budgeting, BSC, BPM, CRM, HR...)

Report generators(Crystal, Business Objects, MS Reporting...)

BI

Reporting

Analytical

& reporting

system


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7 mortal sins


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7 mortal sins (57 mortal sins (5--7)7)

"Darwin was right: BI projects need to evolve"Darwin was right: BI projects need to evolve Business demands projects to be short and simple, and to have anBusiness demands projects to be short and simple, and to have an

immediate return, but this could easily lead to the wrong scopinimmediate return, but this could easily lead to the wrong scoping of projects.g of projects.Just as using the same templates time after time mean plans rareJust as using the same templates time after time mean plans rarely evolve,ly evolve,building in the same limitations in to a new system is one of thbuilding in the same limitations in to a new system is one of the greateste greatestinhibitors to success. BI needs to evolve but BI projects shouldinhibitors to success. BI needs to evolve but BI projects should notnot -- theytheyshould start and stop and not evolve.should start and stop and not evolve.

"We can outsource the whole thing"We can outsource the whole thing Enterprises must define their BI key competencies and capabilitiEnterprises must define their BI key competencies and capabilities in orderes in order

to determine what to into determine what to in-- or outsource.or outsource.

"Just give me a dashboard!"Just give me a dashboard! Companies must have a solid and stable BI infrastructure in placCompanies must have a solid and stable BI infrastructure in place first. Theye first. They

should then create a networked approach where these new technoloshould then create a networked approach where these new technologies aregies areable to communicate with other BI technologies inside and outsidable to communicate with other BI technologies inside and outside thee the

organisation, as well as with other technologies such as businesorganisation, as well as with other technologies such as business processs processmanagement and application integration. A management dashboard smanagement and application integration. A management dashboard shouldhouldbe seen as the finishing touch.be seen as the finishing touch.


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Quo Vadis BI?


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Rearrange the BI pyramidRearrange the BI pyramid

5%

15%

80%

Analyses

Dynamic

reporting

Static

eporting

Today (2005)

15%

60%

25%

Analyses

Dynamic

reporting

Static

reporting

Tommorow (2015)


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Business is multidimensional, but whatBusiness is multidimensional, but what

about BI applications?about BI applications?

Fact:Fact: Human perception is multidimensionalHuman perception is multidimensional

Typical business dimensionsTypical business dimensionsTime, Organizational structure, Hierarchies of products,Time, Organizational structure, Hierarchies of products,

Hierarchies of customers (partners),Hierarchies of customers (partners), GeographicGeographical distribution...al distribution...

Conclusion:Conclusion: OLAP data perfectly match humanOLAP data perfectly match humanperceptionperception

Problem:Problem: BI applications generally do not useBI applications generally do not use

OLAP data as much as they shouldOLAP data as much as they should

Solution:Solution: Raise the abstraction level for mostRaise the abstraction level for most BIBI

applicationsapplications toto use OLAP datause OLAP data as a primary sourceas a primary source


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Measurments should be comparableMeasurments should be comparable

Fact:Fact: Human perception is multidimensionalHuman perception is multidimensional

Typical business dimensionsTypical business dimensionsTime, Organizational structure, Hierarchies of products,Time, Organizational structure, Hierarchies of products,Hierarchies of customers (partners),Hierarchies of customers (partners), GeographicGeographical distribution...al distribution...

Conclusion:Conclusion: OLAP data perfectly match humanOLAP data perfectly match humanperceptionperception

Problem:Problem: BI applications generally do not useBI applications generally do not use

OLAP data as much as they shouldOLAP data as much as they should

Solution:Solution: Raise the abstraction level for mostRaise the abstraction level for most BIBI

applicationsapplications toto use OLAP datause OLAP data as a primary sourceas a primary source


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Next 10 years (SMB)Next 10 years (SMB)

BI spendings

2005 201520122007 2010

DW

+

OLAP clients

DW + OLAP clientsDW + OLAP clients

Business analyses

Performance applicationsCBEM and the birth of

networked BI systems

Increase of 50x!

100%

Business analyses

Performance applications

Benchmarking

5,000%

1,500%

2,500%

4,000%

Documents

MIPRO 2005 Presentation Arian