Impact Of Planning On Successful Project Implementation: A Case Of GCB Bank Data Scrub Project.
Introduction
In a
knowledge economy, data is gold, literally. This is because data is everything
in the information age. In fact, the bottom line is in the data (Eckerson,
2002). This assertion is re-echoed in the words of the Director of Education
and Research at the Data Warehousing Institute, “Data and information are now
as vital to an organization’s well-being and future success as oxygen is to
humans. And without a fresh supply of clean unpolluted data, companies will
struggle to survive”(Eckerson, 2002).
Numerous
organizations these days, including banks, use data, be it that of their
customers, competitors or of their industry to support their decision making
processes and to strategize. Well, in such instances, the quality of decisions
arrived at will be a function of the quality of data employed to aid the
decision making (Vannan, 2001). Vannan (2001) holds
that “dirty” data or poor-quality data has negative impacts on how business is
even carried out.
The
Bank of Ghana issued guidelines in October 2001, mandating all banks to carry
out the “Know Your Customer” policy (KYC policy), which is a principal
component of Customer Due Diligence (CDD) (Australian government, 2008). The
purpose of the document was to assist banks to recognize the importance of
ensuring that they actually know the customers they deal with each day.
The
KYC policy refers to “documentation which sets out a business approach to
ensuring that it can effectively identify, verify and monitor its customers and
the financial transactions in which they engage, relative to the risks of money
laundering and terrorism financing” (Australian Government, 2008).
Following
the 9/11 attacks on the Unites States, enforcement of KYC policies in all
jurisdictions became even more apparent. Data collected subsequent to the
attacks suggests that the whole plot was expected to have been carried out with
US$ 400,000 – 500,000 withdrawn using the US’ own formal banking channels (Balasubramanian, 2009). The big question
here is how well did the banks ‘involved’ in the plot know their customers?
Rationale
Rationale
Currently,
on the corporate landscape, change is the only constant thing. Competition, a
precursor of change, continues to pick up speed all across the globe (De Meyer,
Loch and Pich, 2001). This rapid tempo of competition has in a lot of ways been
an offshoot of globalization and technological breakthroughs, especially in the
area of ICT. To keep up with the competition, companies and organizations have
been compelled to constantly innovate through projects, thereby requiring
careful planning.
To
make matters worse, there is also demand by better informed customers for goods
and services
which
are best suited to their needs, leading to an increased need for good quality
data.
Data scrubbing is time consuming and
expensive and thus it is important that careful planning be carried out prior
to it to mitigate excessive time consumption. But then, what was the impact of
planning on the data scrubbing project, that is, if it was actually or properly
done? This question is what this case study research primarily seeks to answer
using GCB Data Scrub project as a case study.
Main
Objective
To evaluate the impact of planning on Data
Scrubbing project in GCB Bank.
Abstract
The
Bank of Ghana issued guidelines in October 2001, mandating all banks to carry
out the “Know Your Customer” policy (KYC policy), which is a principal
component of Customer Due Diligence (CDD). This was to make sure that the banks
actually know the customers they deal with each day. GCB therefore launched a
data scrub project to clean up their customer database, thus requiring that
planning be done. Data scrubbing is time consuming and expensive. This study
therefore sought to investigate the impact of planning on the GCB data scrub
project. The study was essentially descriptive in nature and employed the case
study research design. Primary data was collected by questionnaire
administration. Analysis of responses to the statement, “proper and thorough planning was done by management for the data scrub”
revealed that only 31.2 % of them answered in the affirmative, implying that a
majority of respondents (68.8 %) were in disagreement. About 87 % said the
project overran its budget; all the respondents (100 %) were of the view that
the project was not completed within scheduled time; and, 46.7 % of them said
the quality of deliverables met specifications. There were no significant
differences (p > 0.05) among the responses given by the respondents for each
of the implementation success measure, implying that there was strong agreement
among the responses given by the respondents. Regression analysis revealed that
the planning of the data scrub project significantly accounted for 23.1 % (p
< 0.05) of the variance in the success measure, cost. This means that the
planning done by GCB was able to significantly reduce budget overruns by a
factor of 23.1 %. It was recommended that the collection of customer updated
information be decoupled and handled separately because of high risk. It was strongly suggested also that a five-member project team be assigned to each
particular region concurrently. This would help the project benefit from
division of labour and save transportation cost involved in travelling from region
to region, not to mention a drastic reduction of project completion time from 2
years to a possible 7 months
Key
Findings
The steps in the planning process were: 1.
Determination of missing information in and sorting of customer mandate forms;
2. Collection of updated information from customers; and, 3. Input of new information into the bank’s
database through a staging area.
Some of the identified risks in the planning
process were: inaccurate recording of missing information, illness, lack of
communication, slow computers, power outages, improper filing of records by
bank branch partly due to inadequate archival space, unwillingness of customers
to give out their information, lack of training in how to communicate with
customers to willingly give their information, slow internet connection,
limited access into the bank’s system and type of employment status given to
project team members, confused state and unfavorable travelling arrangements.
Regression analysis revealed that the planning
of the data scrub project significantly accounted for 23.1 % (p < 0.05) of the variance in the
success measure, cost. This means that the planning done by GCB was able to
reduce budget overruns by a factor of 23.1 %. The analysis further showed that
a very meager variance of 0.44 % in the time success measure and 0.22 % in the
quality success measure were insignificantly accounted (p > 0.05) for by the planning process.
This
means that the planning process for the data scrub project had virtually no
effect on the scheduling time for the project and the quality of deliverables,
suggesting that planning may have not been properly done.
The
above data also seem to suggest that during the planning process, management
tended to be most sensitive to ensuring that there were no budget overruns
(cost) than having the project completed within the scheduled time (Time) and
the quality of deliverables meeting specification (Quality). It is in a way not
surprising considering that the data scrub project took 2 years to complete and
required additional hands.
The following
recommendations were given:
- Customer
information updates should be done at any time a customer has a change in
status to prevent pile ups, thus reducing the cumbersome nature of the
data scrubbing process.
- Data
collected from customers should be accurate and checked for accuracy.
- There
should be more supervision and proper planning to get best results.
- Permanent
staff should also be involved to make the process smooth. Evaluation of work done at every stage
is very necessary.
- More
computers should be provided to make the project faster and smooth.
Researcher: A.B.
Some
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