This document describes a predictive model for organizational commitment using big data. It models the organization as a network of commitment agents and uses a Markov chain to represent transitions between states. Statistical techniques like design of experiments and probability distributions are used. Live data is captured and leading indicators from the model can help ensure higher commitment levels across the organization. There is potential to enhance it to evaluate dynamic business scenarios and power a real-time management dashboard.

1. Using Big Data to Predict
Organizational Commitment
Rajiv B. Deo
B.Tech. M.Tech. C.I.S.A.

2. © Rajiv B Deo 20152
References
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http://crypto.mat.sbg.ac.at/~ste/diss/node1.html, May 1998
 M.C. Paulk et al., eds., The Capability Maturity Model: Guidelines
forImproving the Software Process, Addison Wesley Longman,
Reading, Mass., 1995
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1979
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Operations Research, Holden-Day Inc 1973
 W.Feller, Introduction to Probability Theory & It’s applications, Vol
1 & 2, Wiley, 1971

3. © Rajiv B Deo 20153
 The success of any business depends heavily on
meeting the customer requirements in terms of
quality, cost and functionality on or before the
agreed dead line.
 This is achieved by ensuring a very high level of
commitment among all the sub groups activities
 “commitment” is nothing but keeping the
promises made with each interface represented
by a sub group
Organizational Commitment - I

4. © Rajiv B Deo 20154
Organizational Commitment - II
 Quality model based on Capability Maturity
concept developed by SEI is built on the
management commitment and involvement at
each stage for meeting goals of every KPA
 Thus, there is a need to continuously monitor and
predict organization commitment level in every
organization aiming at “Optimizing” level of SEI
CMM. Over a period this level needs to improve.
 In this presentation, we shall take a brief look at a
quantitative model conceptualized and developed
by the author to predict level of organization wide
commitment.

5. © Rajiv B Deo 20155
Organization commitment model
Request for
commitment is
made by the
service user agent
Scope of the request
is frozen & Risk +
Impact analysis is
done by the service
provider agent
Commitment given to the
user agent - the date of
expected fulfillment
Commitments
Commitments tracked to
closure
Organization commitment model described here is best represented by a network
diagram (PERT chart) where each arm in the critical path is represented by a two
party commitment transaction involving a user agent and a service agent. The
actual process between user and service agents is described below:-

6. © Rajiv B Deo 20156
Commitment Agents & theirInterfaces
Senior Management
Business
Development
Software
Delivery
Infra-structure Quality & Software
Engineering
SQ Audit performance
Team Performance
- Schedule
- Costs
Duration of team meetings
# Proposals
# Orders
# Customers
# New Customers
Audit Performance
SLA performance
- Installation
- Problem solving
- H/W S/W Purchase
# Processes introduced / modified /
improved
- Process Compliance Index
Customers
Project Management Training Management Resource
Management
Human Resource
Management

7. © Rajiv B Deo 20157
NetworkDiagramforpredicting Organizational
Commitment Level
C
U
S
T
O
M
E
R
Business
Development
Group
Software
Delivery Group
Project Management
Group
Quality Group
Human Resources Group
Resource
Management Group
Training Group Infra-structure
Group

8. © Rajiv B Deo 20158
Statistical Techniques - 1
 Design of experiments technique is used to
identify unique independent factors which
influence the predictability of the commitment
transaction.
 Delivery, Quality, and Cost of each project
depends on commitment from -
 Senior Management
 Quality Management
 Project Management
 Training Management
 Resource Management
 Help Desk & Infrastructure Management
 Hardware & Technology Procurement Management
 Human Resource Management
 Business Development

9. © Rajiv B Deo 20159
Statistical Techniques - 2
 The predictive model for organizational commitment level is
dependent on the current state and is completely
independent of the previous states of the system.
 The Organizational Commitment Model as seen by the
customer is represented as a first order, finite state Markov
chain consisting of two channels viz.
 Main channel
Marketing - Pre-sales – Project Management –
Implementation
 Supporting channel
Resources, Training, HR, Quality, Senior
Management, Infrastructure

10. © Rajiv B Deo 201510
Statistical Techniques - 3
Transition probability from stage I to stage J
is worked out using
pij(s) = P(X(t+s) = j | X(t) = i)
where,
X is the Markov property derived from the
performance of respective commitment
agents on the critical path of the
organizational network diagram.

11. © Rajiv B Deo 201511
Statistical Techniques - 4
Organization Commitment level is
predicted with a certain level of
confidence from a stochastic process
consisting of
collection of OCi{i = 1,2, …. n}
where in,
each OCi has a specific probability
distribution function.

12. 12
Degrees of Freedom– Commitment Agents
SM QM PM TM RM IT HP HR BD
∑ 7 7 7 6 6 5 4 6 2
SM 5 0 1 1 0 0 0 1 1 1
QM 5 1 0 1 1 1 0 0 1 0
PM 7 1 1 0 1 1 1 1 1 0
TM 5 1 1 1 0 1 1 0 0 0
RM 7 1 1 1 1 0 1 1 1 0
IT 6 0 1 1 1 1 0 1 1 0
HP 4 1 0 1 0 1 1 0 0 0
HR 7 1 1 1 1 1 1 0 0 1
BD 4 1 1 0 1 0 0 0 1 0

13. 13
Commitment Agents Table part I
Agent Description Mechanism Interfaces Degrees of
Freedom
Weight age
(Wagent)
SM Senior
Management
Senior
Management
decision making
and reviews
Customers, Software
Delivery, Quality,
Infrastructure, Business
Development, Human
Resources
12 0.2857
QM Quality
Management
Audit
Performance
Software Delivery,
Hardware & Technology
procurement, Help Desk
Support, Business
Development,
Project Management,
Resource Management,
Training Management,
Human Resource
Management
12 0.0714
PM Project
Management
Project Planning,
review, and
Tracking
Software Delivery,
Resource Management,
Training Management
14 0.1429
TM Training
Management
Inter Group
Coordination
Project Management,
Resource Management,
Infrastructure, Quality,
Human Resource
Management
11 0.0714

14. 14
Commitment Agents Table part II
Agent Description Mechanism Interfaces Degrees of
Freedom
Weight age
(Wagent)
RM Resource
Management
Inter Group
Coordination
Human Resources,
Project Management
13 0.0714
IT Help Desk &
Infrastructure
Management
SLA Performance Project Management 11 0.0714
HP Hardware &
Technology
Procurement
Management
SLA Performance Project Management 8 0.0714
HR Human Resource
Management
SLA Performance Software Delivery,
Quality, Infrastructure
13 0.1429
BD Business
Development
Business Targets Software Delivery 6 0.0714

15. © Rajiv B Deo 201515
Using Prediction Model in practice - I
 To find out what would be the organizational
commitment level in the month of August, you
would look at the predicted value of OC8 of 9
service providers mentioned in the network
diagram.
 Organization commitment level for August 2016
would be
1. OC8 = WSM*OC8SM + WQM*OC8QM + WPM*OC8PM
2. OC8 = OC8 + WTM*OC8TM + WRM* OC8RM
3. OC8 = OC8 + WITOC8IT + WRD* OC8BD
4. OC8 = OC8 + WHP*OC8HP + WHR* OC8HR

16. © Rajiv B Deo 201516
Using Prediction Model in practice - II
 After the organization commitment level for a
month is predicted using the stochastic process
model, we test the hypothesis that the
organizational commitment would be at the
predicted value with 95% level of confidence
using Chi square test. If the test fails we repeat
the exercise for a lower level of confidence till the
test succeeds.
 The predicted service provider component’s level
from the model is used by the concerned service
providers to give realistic commitments, there by
ensuring better predictability and greater
customer satisfaction.

17. © Rajiv B Deo 201517
Summary
The predictive model defined here,
was implemented using Hadoop with
R and beta tested at many global
organizations from 2008 to 2014
Live raw data captured from Unicenter,
Remedy, SAP modules.
The leading indicators from the model
have ensured higher levels of
organizational commitment

18. © Rajiv B Deo 201518
Scope forfurtherwork
 The predictive data analytical model can evaluate
dynamic business scenarios including
organization re-structuring as the degrees of
freedom between the service agents change
resulting in a different levels of organization
commitment.
 A real time Management Dashboard driven by
business simulation of different organizational
strategies can boost the organization wide
commitment level as seen by the customer.