Presentation I recently delivered at Tucana's 2014 People Analytics Conference in London on April 10, 2014

1. Analytics Driving Action
Jonathan Sidhu
Executive Program Manager
Business Analytics Transformation
@jmsidhu
Mark Tristam Lawrence
Learning Intelligence Leader
Global Business Services
@mtlawrence
#PADDHR

3. Agenda
 IBM – Who we are and what we do
 Workforce Analytics - Background
 Analytics in Action
 Getting Started

4. IBM - a virtual social community
 72% of us outside Americas
 64% workforce in Services business
 55% workforce has less than 5 years
service
 36% of employees work remotely
 12% from acquisitions & outsourcing deals
 1% on global assignments

5. Agenda
 IBM – Who we are and what we do
 Workforce Analytics - Background
 Analytics in Action
 Getting Started

6. Source 1: 2012 IBM CEO study: Q24 ―What do you see as the key sources of sustained economic value in your organization?‖
Source 2: SHRM Human Capital Benchmarking Database, 2011
Products / services innovation
Human capital
Customer relationships
Brand(s)
Business model innovation
Technology
71%
66%
52%
43%
33%
30%
Human capital is the leading cited source of economic value...
...but, CEOs face significant workforce challenges.
The average turnover in the
U.S. is 15% per fiscal year.2
Total costs of replacement can reach
200% of an employee‘s annual salary.2
Key sources of sustained economic value1
CEO Study

7. HR Value
StrategicSmarter WorkforceOperational
Smarter HR Operations
Succession
Management
Learning &
DevelopmentCompensation
Management
Performance
Review
Performance
Planning
Talent
Acquisition
Workforce
Planning
Absence
Management
Payroll
Benefits
Time &
Attendance
Scheduling
& Staffing
Analytics are critical to HR operations and workforce
effectiveness

8. We redefined IBM‘s HR Strategy
Thought
Leadership Develop deep expertise in business, HR,
execution methods and systems thinking
to enable IBM‘s ability to produce value for
clients, IBMers, investors and communities
Analytics
Use technologies to capture, analyze and
integrate data that will yield rich insights
and advance the science of predicting,
shaping and adapting to business trends
Collaboration
Use contemporary technologies and
techniques within the HR community to
enable seamless global communication to
develop, test, refine and implement the
best ideas

9. Time and Resources
ValueandImpact
Based on : Competing on Analytics, Davenport and Harris
Data Management
 Consolidation of data
 Data quality and accuracy
Basic Reporting
 Standard reporting that is reasonably automated
 ‗Slice and dice‘ data based on standard variables
Benchmarking
 Key Performance Indicators (KPIs)
 Performance Measured against Best Practices
Analysis
 Multi-dimensional analysis to better
understand business challenges
Advanced Analytics
 Segmentation, Predictive
Modeling & Optimization
Efficiency
Effectiveness
Business Impact
Business Analytics Model

10. Enterprise-wideReporting
PredictiveAnalytics
InternalSurveys
ExternalAnalytics
Objectives
• Integrate BI into HR as an ‗everyday‘ tool
• Develop deeper analytic and predictive modelling
skills
• Develop key enterprise-wide reports and
scorecards
• Provide better insights from analytics to inform
strategy
• Grow analytic skills in emerging countries
Purpose: To embed a culture of analytics
within the HR organization
SocialAnalytics
IBM Workforce Analytics - a clear purpose…

11. Agenda
 IBM – Who we are and what we do
 Workforce Analytics - Background
 Analytics in Action
 Getting Started

12. Can attrition be predicted?
 Questions
 Can we highlight individuals who
have the highest propensity to leave
an organization?
 Can increased compensation reduce
the rate of unwanted attrition?
 Method
 Analysis of 5 years longitudinal data
 Compensation & attrition analysis
based on historical data
 Visualization – use of heat maps
Geography
Brand
Other
Type of
Hire
Education
Type
Segment and sub segment of population

13. Can we see attrition risks & reduce unwanted attrition?
Retention Case Selection
Action Optimization—Identify retention
cases and targeted actions to retain
them
Attrition
Which employees are most likely
to leave? What kind of actions,
programs and investments will
reduce attrition in the most
effective way?
How likely is each person to leave, and why?
Attrition Hot Spots
Identify high-attrition clusters
Derive attrition ―rules‖
Estimate FUTURE attrition
Understand response to incentives
How well-connected are those
employees most likely to leave?
What actions will yield the
best outcomes?
Benefit($k)
Cumulative Net Benefit is maximized at $9M…
... yielding an attrition reduction of 2.7 pts

14. Our results?
Attrition can be predicted
We can highlight those who have the highest propensity to leave
the organization
Proactive retention efforts can reduce rates of unwanted attrition

15. Can we measure Employment Risk?
 Problem Statement
 Can collective employment risk be quantified?
 Can a ‗risk index‘ be built?
 Methodology
 Statistical analysis of 5 year longitudinal internal and external data
 Tens of metrics analyzed for stability to find a suitable set of dependent
metrics
 Index stabilized over time
 Visualization – use of heat maps, indices and maps
 Built into BI tools

16. Predictive Analytics – Headlights for Employment Risk
Quality of Life Index
Cultural Values
(e.g. Hofstede)
Span of control
Employee Complaints
Inflation & Unemployment
Surveys
Headcount & Voluntary Attrition
Employment
Regulation
Tenure, Band, Rating
Political Instability
Country Level Volatility
Data Is Simulated

17. Our results?
Employment risk can be quantified
Usable risk indices can be built to:
 Inform & coach
 Assist leaders in managing overall
enterprise risk posture
 Provide insight to HR leaders

18. Smarter Learning Analytics
 Problem Statement
 Education performance is judged on volumes, not impact or value
 Learning organisations struggle to align with rapidly changing business models
 Methodology
 Back to basics – why do we provide learning?
 Formulation of new metrics, dependencies and relationships
 Identification, acquisition and modelling of new datasets
 Creation of new reports with optimal visualisation
 Standardisation of formatting, branding, platform and access
 Democratisation of data!

19. Smarter Learning Analytics – Evaluation
Learner Reaction (Level 1) and
Skill Usage (Level 3)
 Resource: Free up dedicated
FTE and increase self-service
 Speed: Reduction of time
from training to reporting
 Impact: Increase in available
analytics; more powerful
communication

20. Smarter Learning Analytics – ROI
Return on Investment (Level 4)
 Automate: Reduce emails from
learning organisation and enable
learners to see value
 Standardise: Common
methodology to provide scalable
solution
 Impact: Draw direct line of sight
between learning and revenue

21. Smarter Learning Analytics – Alignment
Business Impact (Level 5*)
 Delivery Excellence:
 Identify and reduce factors attributed to learning or skills in troubled projects
 Practitioner Utilisation:
 Challenge perception that short-term time away from the client harms longer-
term practitioner utilisation targets
 Grow Talent:
 Demonstrate the impact of learning in promotion, progression, attrition and
retention

22. Smarter Learning Analytics - Findings
Can we demonstrate the value of Learning?
Can we align metrics with current business decisions?
In practice:
 Which learning activities do we prioritise; and which need revision?
 Where are we effectively addressing skill gaps; and where aren‘t we?
 Why should we invest in learning; and should we increase investment?

23. Can we leverage Social ‗Big Data‘ productively?
 Problem Statement
 Can we see what employees are saying on social media about our
company?
 Can we get ‗real time‘ feedback on our business from our own people?
 Methodology
 Consumer insight tool redesigned for employee insight
 HRIS combined with social media
 Highly visual design
 ‗Opt in‘ consent

24. Analyzing ‗big data‘ created by social interactions
→ Determine unexpected
affinities across multiple
analytic dimensions
→ Discover related topics
above and beyond our
initial search
→ Glean employee
sentiment across
company & by segment
→ Perform trend analysis of
sentiment over time

25. The Key Advantages of Social Analytics
 Hear ‗pulse‘ of the organization
 Augment surveys with better understanding
of what employees think
 Tailor services & programs
 Ability to act in nearer real-time
 Leverage existing social media footprint
 Understand what competitors‘ employees
are saying about their employers

26. Our results?
We can see (and use) what employees are saying on social media
about our company
We can get ‗real time‘ feedback on our business from our own people
that provides insight and allows executives to take action

27. Agenda
 IBM – Who we are and what we do
 Workforce Analytics - Background
 Analytics in Action
 Getting Started

28. Analytics: The New Path to Value: Links to the full study, a 22 minute video and presentation highlighting the key findings (https://ibm.biz/newpathvalue)
IBM Institute for Business Value +
 Surveyed 3,000 executives, managers
and analysts plus extensive interviews
 Respondents represent more than 30
industries in 108 countries
 Interviews with IBM and MIT thought
leaders
 Analysis by IBM and MIT teams

29. Organizational obstacles, not data or financial
concerns, are holding back analytics adoption
Ability to get the data
Lack of management bandwidth due to competing
priorities
Lack of skills internally in the line of business
Lack of understanding how to use analytics to improve
the business
Culture does not encourage sharing information
Ownership of the data is unclear or governance is
ineffective
Lack of executive sponsorship
Concerns with the data
Perceived costs outweigh the projected benefits
No case for change
38%
34%
28%
24%
23%
23%
22%
21%
21%
15%
Primary obstacles to widespread analytics adoption
Organizational
Data
Financial
Source: Analytics: The New Path to Value; https://ibm.biz/newpathvalue

30. Lesson Learned
• Cultural shift from data extraction to business analytics
• Stakeholders need to set clear priorities for data and analytics
• Need a broad range of skills
• Simplicity and elegance outweigh ―bells and whistles‖
• Take some risks with new tools – open people‘s minds to the
opportunity
• If you do what you‘ve always done, you‘ll get what you‘ve always got

31. Questions?

32. Thank you!
@jmsidhu
Please leave feedback:
http://j.mp/sidhufeedback
@mtlawrence

33. Additional IBM Institute for Business Value White Papers
a) Business Analytics & Optimization for the
Intelligent Enterprise (2009)
b) Breaking away with Business Analytics &
Optimization (2009)
c) Analytics: The new path to value (2010)
d) Analytics: The widening divide (2011)
e) Analytics: The real world use of big data (2012)
f) Analytics: A blueprint for value (2013)