using big data to predict organizational commitment

Using Big Data to Predict Organizational Commitment

Rajiv B. DeoB.Tech. M.Tech. C.I.S.A.

© Rajiv B Deo 20152

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http://crypto.mat.sbg.ac.at/~ste/diss/node1.html


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


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.


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:-


Commitment Agents & their Interfaces

Senior Management

Business Development

Software Delivery

Infra-structure

Quality & Software Engineering

SQ Audit performanceTeam Performance- Schedule- CostsDuration of team meetings

# Proposals# Orders# Customers# New Customers

Audit PerformanceSLA 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


Network Diagram for predicting Organizational Commitment Level

CUSTOMER

Business DevelopmentGroup

Software Delivery Group

Project Management Group

Quality Group

Human Resources Group

Resource Management Group

Training Group

Infra-structure Group


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


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 channelResources, Training, HR, Quality, Senior

Management, Infrastructure


Statistical Techniques - 3Transition 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.


Statistical Techniques - 4Organization 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

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

Commitment Agents Table part IAgent

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

Commitment Agents Table part IIAgent

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


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


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.


SummaryThe 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


Scope for further work 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.

using big data to predict organizational commitment

Data & Analytics