STRATEGIC FRAMEWORK

To Implement a Telecommunications Business Intelligence Solution in a

Developing Country

D. P. du Plessis and T. McDonald

Department of Computer Science and Informatics

University of the Free State, South Africa

Keywords: Data warehousing, Telecommunications BI, Business Strategy, Business Intelligence Strategy, Developing

Countries.

Abstract: Because no framework existed that considered both the technical and the human resource maturity of data

warehousing and business intelligence in a company, a framework was developed to implement a business

intelligence strategy. The framework consisted of the steps that had to be followed to grow business

intelligence and data warehousing in a telecommunications company in a developing country. These steps

were supported by two modules, the data warehousing lifecycle model and the business intelligence literacy

and cultural maturity model, which formed part of the framework. All the components of the framework are

discussed in detail.

1 INTRODUCTION

South Africa as a developing country has since 1994

gone through a process of privatisation of some of

the state departments. The Department of Post and

Telecommunications was one of those departments.

In order to obtain exclusive rights as the sole

landline provider, for a limited period, government

has set several targets for the new

telecommunications company. One of these targets

was to render new telephone services in the under

serviced areas of the country. A Data Warehousing

(DW) and Business Intelligence (BI) solution was

requested to provide management with the necessary

decision support information to maintain a balance

between acquiring new telephone services, while

maintaining the financial health of the company.

2 STRATEGIC FRAMEWORK

Figure 1 presents the new framework that was

developed to facilitate the alignment of the Business

Intelligence (BI) strategy with the business strategy.

The framework consisted of the steps that were

followed to grow Business Intelligence and Data

warehousing in the company. These steps were

supported by two models which formed part of the

framework, namely the data warehouse lifecycle

model and the BI literacy and cultural maturity

model. Each of the steps will now be discussed.

2.1 Business Intelligence Vision

The vision of the above mentioned

telecommunications company was to drive the BI

Framework and to assist in selecting the appropriate

methodologies, key phases, steps, and roles along

the pathway to deliver decision support information

to the business.

Wu J. (2000) believes that BI is responsible for

the transition of data into wisdom through the

following steps: Data, Information, Analytics,

Knowledge and Wisdom. These steps were adopted

into the above mentioned BI strategy framework and

the implementation is explained by means of the

following steps.

2.2 Data

The existing online transaction processing (OLTP)

systems formed the basis for the first iteration of the

framework. Because the BISIIF is iterative of nature,

after the wisdom step new data needs can be

identified which could result in changes in the OLTP

systems. That means that the wisdom step can high-

512

P. du Plessis D. and McDonald T. (2007).

STRATEGIC FRAMEWORK - To Implement a Telecommunications Business Intelligence Solution in a Developing Country.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - DISI, pages 512-515

DOI: 10.5220/0002378505120515

 SciTePress

Figure 1: Business Intelligence Strategic Iterative and Incremental Framework (BISIIF).

light data needs which can require a change of the

BI strategy that facilitate the process of collecting

the data requires for the OLTP systems.

2.3 Information

As the number of transactions that were processed

and collected by the company’s systems increased, a

wealth of data was collected. While each data

element was a component of a transaction, it could

not provide any meaning by itself. On an individual

basis, a data element such as "product" did not

provide meaning unless they were presented in

conjunction with other data elements. Only

accumulation of data into a meaningful context

provided information. Extract Transform and Load

(ETL) processes were used to provide BI specialists

in the company with the ability to extract data from

the OLTP databases and transform the data into

information.

2.4 Analytic

While combining data and meaning to create

information was extremely useful, separating or

regrouping information extended the value of

information. Applications that have online analytical

processing (OLAP) capabilities provided users with

the ability to analyze information and determine

relationships, patterns, trends and exceptions.

2.5 Knowledge

The next level of elevated understanding was

knowledge. Knowledge is different from data,

information or analytics in that it can be created

from any one of those layers, or it can be created

from existing knowledge using logical inferences. BI

applications that had data mining capabilities

provided users with the ability to identify hidden

trends and unusual patterns within the data. These BI

applications utilized various data mining techniques

which were based on statistics and algorithms to

provide users with the ability to discover knowledge

within their data. Without the use of a data mining

application, identifying hidden trends or unusual

patterns within the data would be extremely time-

consuming.

2.6 Wisdom

Wisdom is the utilization of accumulated

knowledge. By utilizing knowledge, a higher level

of understanding of the data was created. The BI

strategic framework was not a once-off exercise.

There were more then one iteration of the

framework. As the company has worked through the

framework, each interaction increased the wisdom of

the company. As the wisdom increased new

requirements developed and this is how the BI

solution was built in the company.

STRATEGIC FRAMEWORK TO IMPLEMENT A TELECOMMUNICATIONS BUSINESS INTELLIGENCE

SOLUTION IN A DEVELOPING COUNTRY

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Figure 2: The Double Wave Data Warehouse Lifecycle Model.

2.7 Double Wave Data Warehouse

(DWDW)

Du Plessis and McDonald (2007) developed the

Double Wave Data Warehouse (DWDW) lifecycle

model which consisted of two waves for the

development of a data mart. Wave one concentrated

on the rapid implementation of a business critical

information solution. Wave two concentrated on

modelling the ongoing requirement into a permanent

dimensional model. To move from one step to

another in the BI strategic framework, more

development was required in most instances. The

lifecycle model could therefore not be seen as one of

the steps, but it was a parallel process that supported

the company to move from one step to the next.

2.8 BI Literacy and Culture Maturity

Model

Williams and Williams (2004) stated that, the real

success of the data warehouse and business

intelligence of today lies in how well it is utilised by

the business. How “BI literate” the business is, is

important information for businesses because

literacy is closely linked with utilisation of the BI

solution. A BI Literacy and Cultural Maturity Model

was developed to measure the literacy and culture of

the business and facilitate the growth thereof (Du

Plessis and McDonald, 2007).

3 CONCLUSION

This paper discussed some of the challenges faced

when implementing a telecommunications business

intelligence solution in a developing country. The

Business Intelligence Strategic Iterative and

Incremental Framework (BISIIF) was used to

implement the BI strategy in the above mentioned

company. This framework is iterative and

incremental of nature and therefore do not support

once-off, large enterprise data warehouse projects.

Companies in developing countries are normally not

so eager to spend on BI solutions because it is

sometimes seen as a luxury and there are other

information technology solutions that compete with

BI for funding. BI in developing countries therefore

calls for a framework that can deliver smaller

chunks (Data Marts) which can deliver value earlier

to ensure continued funding. This framework is

sensitive for BI literacy and culture.

Wave 1 Wave 2

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REFERENCES

Du Plessis. D. and McDonald. T. (2007). Challenges in

building and maturing of a telecommunications

business intelligence solution in a developing country.

To be published in IRMA 2007 proceedings

Du Plessis. D. and McDonald. T. (2007). The Double

Wave Data Warehouse Lifecycle Model. To be

published in Wessex institute 2007 proceedings

Williams, S. and Williams, N. (2004). The Business Value

of Business Intelligence, Business Intelligence Journal.

Vol. 1, Page 1

Wu J. (2000). The Transition of Data into Wisdom.

Retrieved 28 October 2004 from:

http://www.dmreview.com/article_sub.cfm?articleId=

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SOLUTION IN A DEVELOPING COUNTRY

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