Presentatio June 2012

22
© 2012 Forrester Research, Inc. Reproduction Prohibited 1 © 2009 Forrester Research, Inc. Reproduction Prohibited Agile Business Intelligence (BI) and Data Virtualization Boris Evelson, Vice President, Principal Analyst

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Transcript of Presentatio June 2012

Page 1: Presentatio June 2012

© 2012 Forrester Research, Inc. Reproduction Prohibited 1 © 2009 Forrester Research, Inc. Reproduction Prohibited

Agile Business Intelligence (BI) and Data Virtualization

Boris Evelson, Vice President, Principal Analyst

Page 2: Presentatio June 2012

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Earlier generation BI and DW just don’t cut it anymore

Best practices Next-gen

technologies

Criticality

Scalability

Complexity

Low penetration

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Traditional BI is complex

Source: October 20, 2010, “The Forrester Wave™: Enterprise Business Intelligence Platforms, Q4 2010” Forrester report

Data Information

80% 20%

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The Four V’s Determine When Big Data Should Be Considered

Source: September 2011 “Expand Your Digital Horizon With Big Data”

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Traditional BI presents IT/business alignment challenges

Flexibility and agility Operational risk

management

Business IT

Reacting Planning

Interaction Requirements-gathering

Business requirements Standards

Analysis and Discovery Analysis

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Base: 1092 packaged-application decision-makers at firms with over 19 employees.

*Base: 782 custom software development decision-makers at firms with over 19 employees.

(Percentages do not total 100 because of rounding)

Source: Forrsights Software Survey, Q4 2011.

What are your firm's plans to adopt business intelligence software?

2%

3%

33%

12%

17%

13%

20%

3%

27%

16%

26%

18%

11%

Don't know

Decreasing / scaling back

Expanding / upgrading implementation

Developed/implemented, not expanding

Planning to develop/implement

Interested but no plans

Not interested

Packaged applications Custom development*

60%

28%

BI and analytics are hot!

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Organizations that use BI show increased levels of maturity, but it’s still a long road ahead

†Base: 173 technology professionals familiar with their respective organization’s BI efforts

Overall maturity 2.75

Governance and ownership 3.25

Organization 2.81

Processes 2.65

Data and technology 2.82

Measurement and adjustment 2.34

Keeping up with the latest trends 2.07

†Source: Q4 2010 Global BI Maturity Online Survey

BI Maturity is evaluated on a scale of 1 to 5 (low to high)

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Anecdotal evidence suggests that BI and analytics penetration levels in an average enterprise are still very low

Source: Boris Evelson, “Poll: What % of your company workers use traditional BI apps?” Boris Evelson’s Blog For

Business Process Professionals, April 24, 2011 (http://blogs.forrester.com/node/3974/results)

Percent of workers using enterprise BI applications

Pe

rce

nt

of

org

an

iza

tio

n

45%

19%

13%

23%

0%

10%

20%

30%

40%

50%

<6% 7%-10% 11%-19% >20%

“What percent of your company workers use traditional BI apps?”

In 64% of enterprises <10% of workers use BI

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Agile software development methodology by itself does not work well for BI

Agile relies on quick prototypes. Traditional BI technology

(RDBMS) does not lend itself to prototyping.

Agile is code-centric.

BI is data-centric.

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Forrester expands the definition of Agile – “democratized” - BI

Forrester defines Agile BI as:

An approach that combines

processes, methodologies,

organizational structure, tools, and

technologies that enable strategic,

tactical, and operational decision-

makers to be more flexible and

more responsive to the fast pace

of business and regulatory

requirement changes.

Source: March 31, 2011, “Trends 2011 And Beyond: Business Intelligence” Forrester report

Agile BI

3. Next-gen technology

2. Organizational and process best

practices

1. Software development methodology

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Agile development methodology

Liaisons, business analysts Face-to-face business

participation

Processes Interactions

Specifications Prototypes (virtualization)

Plan Reacting to change

Tra

ditio

na

l

Agile

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Start with organizational best practices

Insist on business

ownership and

governance. Emphasize

organization and

cultural change

management. Decouple data

preparation and data

usage. Treat front- and

back-office BI

requirements and

users differently. Establish hub-and-

spoke organizational

model.

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Follow up with process best practices

Use a combination

of top-down and

bottom-up

approaches. Use Agile

development

methodology. Enable BI self-

service for end

users.

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Base it all on four key categories of next-gen BI technologies

Source: March 31, 2011, “Trends 2011 And Beyond: Business Intelligence” Forrester report

Auto information discovery

Contextual BI

Integrated full lifecycle

BI on BI

Data sources

Data and content

Disk and streaming

Historical and predictive

Complex data structures

Metadata

Within processes

Within Information Workspace

Self service

SaaS

Mobile

Offline

Exploration and discovery

Adaptive data models

Big data

Unlimited dimensionality –

Advanced Data Visualization

* Where data virtualization plays a role

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A single data model cannot support all decision types – need a mixed environment

Source: July 2009, “Fit Your Data Architecture To Your Analytical Needs” Forrester report

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Data Latency And Complexity Capabilities Of Various Analytical Data Architectures

Source: July 2009, “Fit Your Data Architecture To Your Analytical Needs” Forrester report

Data

virtualization

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Key Analytical Data Architecture Decision Drivers

Source: July 2009, “Fit Your Data Architecture To Your Analytical Needs” Forrester report

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Key Analytical Data Architecture Decision Drivers (Cont.)

Source: July 2009, “Fit Your Data Architecture To Your Analytical Needs” Forrester report

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Key Analytical Data Architecture Decision Drivers (Cont.)

Source: July 2009, “Fit Your Data Architecture To Your Analytical Needs” Forrester report

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When and why Forrester recommends data virtualization

All enterprises have

multiple BI platforms

Push as much BI data access

functionality (queries, joins,

metrics, aggregates) into a single

data virtualization layer

Single EDW is not

practical, agile or flexible

New data sources may

come (very often) and go

Put data virtualization front and

center of provisioning (and

deprovisioning) data sources

Complement EDW with data

virtualization for flexibility and

agility

BI apps do not have an

exclusive on data access

Standardize data access for BI

and for OLTP apps. Minimize

data replication

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Choosing data virtualization technology: January 2012 “The Forrester Wave™: Data Virtualization, Q1 2012”

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Thank you

Boris Evelson

+1 617.613.6297

[email protected]

http://blogs.forrester.com/boris_evelson

Twitter: @bevelson

www.forrester.com