how a data-driven culture improves organizational performance
TRANSCRIPT
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
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Analytically-oriented Cultures Better
Address User’s Analytic Needs
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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
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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.
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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
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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
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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 ordata loading)
Data visualization
Reporting/Dashboards
Ad-hoc, on-demand data access andmanipulation 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
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AnalystsFont line staff ManagersWho?
React fasterAnticipate
sooner
Optimize
managementWhy?
Model &
Predict
Sense &
Respond
Plan &
AnalyzeHow?
Exploration &
Discovery
Operational
IntelligencePerformance
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.
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Transactional
data
Descriptive
data
Performance
data
Attitudinal
data
Data warehouseEvent processing Data repository
Behavioral
data
Exploration &
Discovery
Operational
IntelligencePerformance
Management
Industry and/or process
analytic applicationsInformation Access and
Analysis Tools
Interactional
data
React fasterAnticipate
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 adaptResponding to the demand for self service BI/analytics tools will necessitate a reassessment of current centralized IT practices.
Involve LOB to better meet analytic needsSelf 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.
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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 toolsAppropriately 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
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Skilled Staff Shortage 2
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: [email protected]
Twitter: @briantheanalyst
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