How a Data-Driven Culture Improves Organizational Performance

An Analytic Culture Drives Performance
in Asia Pacific Organizations
Brian McDonough
IDC
June 2015

Analytics In Asia Pacific
 IDC examined two groups of analytic-using
organizations in AP
• Outperformers – A segment that derived more benefits
from analytics than their peers
• Peers – Organizations that still experienced benefits from
analytics but not to the extent of the outperformers
 Benefits included:
• Cost Reduction
• Revenue Increases
• Productivity Improvements
• Innovation Improvements
 N=207, 50% IT, 50% LOB . Singapore, India, Japan
Australia = 25% each
© IDC Visit us at IDC.com and follow us on Twitter: @IDC 2

Analytically-oriented Cultures Better
Address User’s Analytic Needs
5%
33%
9%
31%
0%
10%
20%
30%
40%
50%
60%
Less Involved More Involved Less Reliant More Reliant
1 = Do not meet requirements at all 2 3 4 5 = Fully meet requirements
Managers involvement in
promoting the use of the BI
solution
Managers reliance on analytics
for decision making
 More involvement and reliance on analytics results in twice the level of
benefits derived from an analytics initiative than in other organizations.

Culture Drives More Frequent Use
20%
39%
21%
38%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
Less Involved More Involved Less Reliant More reliant
1. Daily 2. Weekly 3. Monthly 4. On a project by project basis/few times per year 5. Never use it
Managers involvement in
promoting the use of the BI
solution
Managers reliance on analytics
for decision making
 More involvement and reliance on analytics results in twice the level of
regular usage of analytics.

A strong analytics culture (when
managers are more reliant on analytics
for decision making) leads to availability
of ad-hoc data analysis and
visualization technology that meets user
requirements 3.5 times more frequently
than at less analytically oriented
organizations.
3.5x

Delivering The Right Analytics
Increases Benefits
0%
10%
20%
30%
40%
50%
60%
70%
Others Satisfied
Cost Reduction Revenue Increase Productive Innovative
Improvement of 50%+ in each
business outcome
Ad-hoc Analysis

User Needs for Self-service Analytics
are not Being Met
67%
68%
69%
74%
71%
32%
32%
31%
25%
24%
0% 10% 20% 30% 40% 50% 60% 70% 80% 90%100%
Performance (speed of query response or
data loading)
Data visualization
Reporting/Dashboards
Ad-hoc, on-demand data access and
manipulation or multi-dimensional analysis
Access on mobile device(s)
1 -4 = Needs Not Fully Met 5 = Fully meet requirements Do not know
 67-71% of users are not getting the analytics they need

New Analytics Requirements
AnalystsFont line staff ManagersWho?
React faster
Anticipate
sooner
Optimize
managementWhy?
Model &
Predict
Sense &
Respond
Plan &
Analyze
How?
Exploration &
Discovery
Operational
Intelligence
Performance
ManagementWhat?
 Fit the right analytics to the right use case to better meet requirements for
end-user analytic technologies. Begin with descriptive analytics, move to
predictive and optimized.

Transactional
data
Descriptive
data
Performance
data
Attitudinal
data
Data warehouseEvent processing Data repository
Behavioral
data
Exploration &
Discovery
Operational
Intelligence
Performance
Management
Industry and/or process
analytic applications
Information Access and
Analysis Tools
Interactional
data
React faster
Anticipate
sooner
Optimize
management
Model &
Predict
Sense &
Respond
Plan &
Analyze
Business
analysts and
data scientists
Font line staff
and systems
Executives
and managers
Provide access to data and tools
needed for each use case

10
Self Service1
IT Impact
 Centralized IT practices must adapt
Responding to the demand for self service BI/analytics tools will
necessitate a reassessment of current centralized IT practices.
 Involve LOB to better meet analytic needs
Self service BI/analytic demand will come from across user types and
will differ based on personas.
 Work with any groups who brought their own analytics into the
organization.
Bring your own BI/analytics tools (and data) exposes the organization
to addressable risks.

11
Self Service1
Guidance for IT
 Recognize that “bring your own BI/analytics tools” is an
unstoppable movement
Similar to BYOD that will require IT, Line of Business, and Analytics groups to
collaborate closely while recognizing each other’s core competencies.
 Deploy fit-for-purpose tools
Appropriately address different BI/analytics use cases such as performance
management, operational intelligence, and visual discovery and exploration.
 IT should focus on core competencies:
• Data governance
• Security
• Knowledge sharing and training
• Vendor management

12
Skilled Staff Shortage2
Guidance
 Use existing technology skills
Skills will likely exist for deploying and optimizing analytics software and
hardware infrastructure. Accessing data sources.
 Develop competency centers
Mix of cross-functional and domain-specific skills representation (IT and
LOB)
 Use external services
Easier-to-use tools reduce the impact on users but the technology still
must be implemented. IT and the help of external service providers
make delivery of an analytics solution possible.

Questions, Comments
 Brian McDonough
 Research Manager, IDC
 Email: bmcdonough@idc.com
 Twitter: @briantheanalyst

How a Data-Driven Culture Improves Organizational Performance

More Related Content

How a Data-Driven Culture Improves Organizational Performance