TOWARDS A FRAMEWORK FOR PROJECT MANAGEMENT INTELLIGENCE (PMInt) Robert Hans PhD Student at University of South Africa Supervised by Prof E Mnkandla

TOWARDS A FRAMEWORK FOR PROJECT

MANAGEMENT INTELLIGENCE (PMInt)

Robert Hans – PhD Student at University of South Africa

Supervised by Prof E Mnkandla

AGENDA

• Background

• Purpose of the study

• Foundation of Project Management

Intelligence (PMInt) Framework

• Business Intelligence (BI) Architecture

• PmInt Architecture Adapted from BI’s

• Elements of the PMInt Architecture

• PMInt in action – A scenario on turnover of

project members

• Benefits of the proposed PMInt Framework

BACKGROUND

• Hans and Mnkandla (2013) argue that just like business

managers depend on BI to improve business performance,

ICT project managers should have PMInt tools similar

which will enable them to deal with

continuously changing

and complex software project environment which is similar

to a business environment.

• PMInt is

‘The art and science of creating knowledge from

available project information through the systematic

process which involves collection, analysis, communication

and management, which will enable better project decisions

to meet project requirements.’ (Hans and Mnkandla,

PURPOSE OF THE STUDY

• The concept of project management

intelligence is a new one and therefore

there is

a need

for development of sound frameworks

and models to support PMInt.

• To close this gap therefore this study is

proposing a project management intelligence

FOUNDATION OF PMInt FRAMEWORK

• A study by Hans and Mnkandla (2013) argues

that project intelligence (PMInt) tools should be

modelled on business intelligence (BI) tools.

• Based on this study then PMInt framework is

should be founded on BI framework.

• Firstly, the core of BI is the

gathering

,

analysis

and

distribution

of information. Secondly, the

objective of BI is to

support the strategic

decision-making process

(Martin, Laksmi and

FOUNDATION OF PMInt FRAMEWORK (CONT…)

• According to Martin, Laksmi and Venkatesan

(2013) a BI system consists of:

• decision support capabilities,

• query and reporting,

• online analytical processing (OLAP),

• statistical analysis,

• knowledge management capabilities,

• forecasting and

BI ARCHITECTURE

Operational

data sources

External data

sources

Extract Transform

Load

_Data

warehouse

OLAP

Data mining

Reporting

Front-end

applications

0 20 40 60 80 100 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr East West North

Figure 1 – Typical Business Intelligence Architecture. Adapted from

Chaudhuri, Dayal and

Narasayya (2011)

PMInt ARCHITECTURE ADAPTED FROM BI’S

Operational data sources External

data

sources Extract Transform Load

Data

warehouse

OLAP Data mining

Front-end

applications

0 20 40 60 80 100 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr East West North A d a p tin g B I A rc h it e c tu re fo r P M In t A rc h it e c tu re

Figure 2 – Project Management Intelligence (PMInt) Architecture.

Internal project data sources Organization’s social media (e.g. facebook, blog, etc.) Extract

Transform Load

Project

data

warehouse

OLAP Data mining Reporting Analytics

Front-end

applications

0 20 40 60 80 100 1st Qtr 2nd Qtr 3rd Qtr 4th Qtr East West North External project data sources

ELEMENTS OF THE PMInt ARCHITECTURE

PMInt data sources

• Internal data sources

: Project data sources might not be as

many as those found in BI because projects might not

necessarily need data from all data sources of the business.

– For example, projects that an organization might be running may

not need data from the sales department database.

• External data sources

: PMInt tools also need data from key

external project stakeholders

, such as suppliers and

sponsors.

• Online social media

are critical data sources as it might

want to establish opinions/feelings of some of its project

stakeholders with regard to certain project aspects.

ELEMENTS OF THE PMInt ARCHITECTURE (CONT…)

Extract, Transform and Load (ETL)

• The

extraction

,

transformation

and

loading

processes

of data from different data sources ensure that the data

stored in the data warehouse is credible for accurate

and quality analysis as well as reporting.

• Data manipulation

: project managers need online

analytical processing, data mining, project progress

and performance reporting and text analysis tools to

gain insight on various project aspects.

– Analytical information or trends provided by the earned value

management (EVM) technique is limited to triple constraint data of a

project and does not provide any other useful information on other

aspects (e.g. assessing project member’s behavior) of a project that

might be of interest to a project manager.

PMInt IN ACTION – A SCENARIO ON TURNOVER OF

PROJECT MEMBERS

Figure 3 – A scenario on project members’ turnover

Project’s social media

(Blogs and Facebook)

Exit interview

database

E

T

L

Data mart

E

T

L

D

a

ta

M

in

in

g

Behavioral Prediction Model

Decision making process on

project HR matters

In

flu

e

n

ce

s

THE BENEFITS OF THE PMInt FRAMEWORK

Benefits are in three-fold:

• It

provides guidelines

for the development of

project management intelligence tools.

• The framework

advances the theories for practice

in the project management discipline.

• It also

highlights some of the intelligent tools

which project managers need in order to improve

their decision-making process and deliver

CONCLUSION

• Many of the components of the BI architecture

presented above were adapted to the PMInt

architecture with minor modifications on some of

them.

• This ‘natural’ mapping and adaptation should not

be a surprise given that ICT projects are business

constructs and their environments are also similar

as discussed by Hans and Mnkandla (2013).

REFERENCES

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