Examination data quality: An innovation towards quality examination data in Zambia Banji Milumbe Shakubanza * Examinations Council of Zambia
[email protected] Pritchard Haboongo Examinations Council of Zambia
[email protected]
Data quality is an extremely important issue since quality determines the data's usefulness as well as the quality of the decisions made based on the data. Data that are inaccurate untimely or inconsistent with other sources of information lead to incorrect decisions, wasteful expenditures. Inaccurate data in public examinations might subject candidates to unfair treatment in the examination, leading to contention of results, candidates writing under protest, shortfalls in examination question papers or oversupply of examination question papers. Another area that may be affected due to inaccurate data are the collection of examination fees. For a long time the Examinations Council of Zambia (ECZ) has struggled to ensure that quality examination data is kept in the system to enhance decision making. This paper discusses how ECZ is in the process of achieving good data quality through working with the Internal Audit Unit. Challenges and success scored will be discussed in this paper as well as a brief on who is responsible for data quality, the impact of quality problems and the major obstacle to improving data quality.
1.1 INTRODUCTION Education plays a key role in the development of a country and public examinations offer a standard through which the education sector is evaluated. Public examinations must be well administered and consistent with all education policies and strategies that a country puts in place. The information obtained from public examinations should be complete, of high quality and portraying the status of the education sector. The nature of public examinations administration involves handling massive data which must be transformed into useful information for various stakeholders. In the process of getting useful information on examinations conducted, the Examinations Council of Zambia undertakes various tasks of registering candidates, preparing examinations, conducting
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examinations, marking and capturing marks, marks processing and publication of results. All these tasks mentioned involve processing of massive data on databases. The handling of massive data calls for efficient and effective ways of ensuring that the data that has been processed is accurate and dependable and is a reflection of what the candidate submitted and expects to see. In most cases it has been very difficult to ascertain that the candidate data that has been submitted is correctly processed especially in the area of candidate registration and fees collection. A number of omissions have been noted at the time of sitting the examination which cause some candidates sitting the examination without being registered or not having paid the prescribed examination fees due to inaccurate data. With the above problem the ECZ came up with an initiative in conjunction with the internal audit unit and field visits are conducted to ascertain the quality of data submitted for processing. 1.2 BACKGROUND The Examinations Council of Zambia is mandated to conduct examinations at Primary (Grade 7), Junior Secondary (Grade 9), School Certificate and Tertiary levels and had been registering candidates at these levels using the optical mark read (OMR) forms which were distributed to Centres and would be candidates were required to complete their examination entry details by shading in the provided spaces. These forms were in turn submitted to the Examinations Council of Zambia by Schools/Centres where they were scanned and data loaded into the examinations processing system and later generate the provisional registers, entry statistics and pre exam documents. A lot of challenges were observed because there was no facility to use to ensure that the data shaded on the registration form was accurate and error free. The ECZ was very much affected by the data errors which made the operations very difficult. Some of the challenges faced included omission of candidates from the final register, wrongly spelt names, multiple shadings, wrong subjects entered by candidates. This resulted in a lot of complaints by candidates coming through for corrections such as names and subjects which resulted in candidates sitting for a subject that they initially did not register for due to wrong data. The anticipated omission of candidates by the system resulted into ECZ procuring unwanted quantities of examination materials under the Headquarters' stock to mitigate the shortfalls that would be experienced by the Schools/Centres during examinations. Undoubtedly this was a huge cost on the part of the institution as not all these materials would be utilized by the centres. In a bid to improve the quality of data the ECZ introduced a new system of checking the data where by the internal audit section did not only focus on financial matters during their auditing but also
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focused on checking the quality of data sent to ECZ and the processed data. The quality of data has a bearing on the funds collected where examination fees were paid as well as the supply of examination materials to the centres. When there is inaccurate data, it means wrong materials are provided as well as wrong quantities of examination materials leading to either under supply or over supply. Over supplying of materials means a lot of wastage in materials as well as expenditure. In addition, the ECZ would stand to lose some revenue if the data is inaccurate as some candidates may end up sitting the examination without paying the prescribed examination fees while undersupply would lead to ECZ repacking examination materials which would be delivered separately at a huge cost to ECZ. Another challenge that ECZ faces with inaccurate data is spending too much time resolving unprocessed marks and correcting candidate’s details arising from registration data errors. This also means that candidates are likely to receive their results later than the rest of the candidates which would create anxieties to both candidates and parents. It is against this background that the ECZ introduced a new system of registering candidates and the monitoring of centres after registration. The new candidate registration system allows centres to enter candidate registration data and are able to print various reports for checking the completeness of data before submitting to ECZ. It also has some inbuilt tools for checking and validating the data entered. The new system saw a reduction in most of these errors although the problem has not been completely eliminated. Procedures of using Electronic Candidate Registration System The schools/centres are provided with the registration software which has in-built controls and validations. After capturing the candidate data the schools/centres are expected to submit their data to the District Education Board Secretary’s (DEBS) offices for consolidation by level (grade). This meant that the data for all schools in one district would be submitted on one CD as opposed to individual centres submitting to ECZ. The CDs from districts are received by the Examination Administration Department at ECZ who are responsible for all examination administration processes. As a quality control measure the following processes are followed:
The province submitting the CDs should also avail and submit hard copies of Provisional Registers that were printed at School/Centre level after capturing the data and are supposed to be validated by all the pupils by signing on the registers.
When receiving the CDs and hard copies, the ECZ officers open the CDs to verify that there is data on the CD and it conforms to the prescribed format.
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After this is done the submitting officer will then complete the receipting register which is signed by both the officer from the Province and ECZ.
After this process is done all the received CDs are then handed over to the IT Department for processing of entries by assigned staff for each examination level. Once the registration process is completed for a province, reports are generated from the main examinations processing system for verification of the processed data against the submitted data from the various provinces. This exercise is undertaken by the examinations administration department. Upon satisfactory verification of all entries, entry statistics are generated that are used in procuring all the required examination materials. Role of Internal Audit in the candidate registration process In view of the challenges that were experienced when the system was manual which resulted into a lot of audit queries the Internal Audit section as a quality control tool were involved in finding solutions to this problem by continually assessing the reliability of the systems by undertaking system audit to ensure the following:
That the Council derives a good return on its investment in the new systems developed.
That there was value for many services from the investments being undertaken.
That there was total adherence to the documented developed systems and procedures before such systems were rolled out.
That all the examination fees collected were correctly accounted for.
1.3 PROBLEM STATEMENT Despite the introduction of the new systems for candidate registration and marks entry which have in-built data validations, the Examinations Council of Zambia has continued to experience problems during results processing all related to data errors. This study aim at looking at how the information technology department of the Examinations Council of Zambia has collaborated with the Internal Audit section to change their business processes as a result of serious challenges encountered in the manual processes of processing examinations related data. 1.4 SIGNIFICANCE OF THE STUDY The complaints from stakeholders have continued despite efforts put in place to check the examination data before it is processed to ensure data quality. The ECZ changed systems and procedures for capturing both registration data and marks in a bid to improve examinations data quality. The significance of the study was to find out the source of data errors and how they can be resolved. 4
2.0 LITERATURE REVIEW Data quality According to Wikipedia, Data are of high quality "if they are fit for their intended uses in operations, decision making and planning" (J. M. Juran). Alternatively, the data are deemed of high quality if they correctly represent the real-world construct to which they refer. Furthermore, apart from these definitions, as data volume increases, the question of internal consistency within data becomes paramount, regardless of fitness for use for any external purpose, e.g. a person's age and birth date may conflict within different parts of a database. The first views can often be in disagreement, even about the same set of data used for the same purpose. (Data quality, 2014) Lucey T 2005, alludes good information to be timely, appropriate for its use, complete. When the information that is generated by various organisations lack these qualities, we see the various challenges facing in the operations. (Lucey, 2005) Data quality is an extremely important issue since quality determines the data’s usefulness as well as the quality of the decisions made based on the data (Creese and Veystsel, 2003). According to Brauer (2001), data quality is the cornerstone of effective business intelligence. (Turban, Mclean, & Wetherbe, 2004) This is true in public examinations because good quality examinations data helps in decision making as to which candidates would proceed to the next level in their education such as high school, college and universities. This ensures that the candidates admitted at each level have the minimum requirements / qualifications to enter into that level. If the data is of poor quality, it means that even the decisions made would be poor. Laudon and Laudon 2007, further states that before a new database is in place, organisations need to identify and correct their faulty data and establish better routines for editing data once their database is in operation. Analysis of data quality often begins with a data quality audit, which is a structured survey of the accuracy and level of completeness of the data in an information system. Data quality audits can be performed by surveying entire data files, surveying sample files from data files, or surveying end users for their perceptions of data quality. (Laudon & Laudon, 2007) This is why the Audit section of Examinations Council of Zambia has undertaken such data audits to identify and correct the data quality problems. Impact of quality problems The impact of data quality problems on the organisations can be very detrimental to the business. Data are frequently found to be inaccurate, incomplete, or ambiguous particularly in large, centralised databases. The economical and social damage from poor quality data has actually been 5
calculated to have cost organisations billions of dollars. According to Laudon 2007 at British Telecommunications (BT) Group, poor quality data led to poor product inventory data and customer billing errors. These problems were hindering BT’s interactions with suppliers and customers. The staff at BT were spending too much time and effort correcting data. Every time wrong data was detected, corrective actions had to be taken and this consumed organisation energies. This negatively impacted on their business because they had to halt sending out bills or in some cases recalling the wrong bills sent. During this period less revenue was coming into the firm and also the firm was spending a lot of time and money to correct the wrongs, hence loss of business. All this happened due to the inaccurate data that was available at the company. Another impact of the poor quality data is on the decision making process. Appropriate decision making becomes a problem because of the poor quality of the data which lead to bad decision making for the business. The quality of decisions depends on the quality of data or information that is available in the firm. Bad data quality leads to misinformed or under-informed decisions. Laudon and Laudon, 2007 states that data that are inaccurate, untimely, or inconsistent with other sources of information lead to incorrect decisions, product recalls, and financial losses. Inaccurate data in criminal justice and national security databases might even subject you to unnecessary surveillance or detention. If a database is properly designed and enterprise-wide data standards established, duplicate or inconsistent data elements should be minimal. Cause of Data quality problems The causes of these data quality problems are various. Some of the causes may be Management, organisational and technological factors. Organisations tend to spend too much time and effort trying to correct the errors instead of addressing the cause of the problem. Without the cause of data quality problems being identified, the errors keep on recurring because the method used to resolve would be that of ‘fire fighting’. One can also assume that the data errors are also caused by lack of structure within the organization at data collection points. (Papaids, 2008) Laudon and Laudon states that “Most data quality problems, however, such as misspelled names, transposed numbers, or incorrect or missing codes, stem from errors during data input. The incidence of such errors is increasing as companies move their business to the Web and allow customers and suppliers to enter data into their web sites that directly update internal systems.” (Laudon & Laudon, Management Information Systems: Managing the Digital Firm, 2007) In the current registration system used by ECZ, the Centres are left to input their own candidates in the registration system and later submit the registration data to ECZ. 6
Technological causes of quality problems may be attributed to data being stored in different systems which are not integrated. This could be due to a non-standardised way of collecting data for input into the system. Also the available technology may not handle the resolution of errors, hence the need to change both the hardware and software to enable efficient and effective error handling. The goal of every firm should be to stop building enterprise data liabilities and start building enterprise data assets. That is why, many organisations are investing in ensuring data quality using different methods. Resolving data quality problems. Organisations can use a variety of ways in resolving the data quality problems. One way to resolve data quality problems would be by appointing staff in key areas of the firm to deal with quality problems and also develop procedures for detecting and resolving errors. The management of public examinations need to realise the importance of quality data and decide to take data quality more seriously than before. One way would be to appoint a project manager in charge of data quality programmes in the institution. They should thus dedicate resources to tackle this problem and come up with better quality data. The organisations should develop a data quality methodology that incorporates best practices from inside and outside the company. Inside they should ensure that all data entered was checked for quality whereas outside the company ensure that all implications of wrong data were addressed. This should be done to ensure that there was consistency in the way data was received which would then be easier to enter into the system. Technology can also be deployed to clean up data and track all inconsistencies in the data. With the use of a database, data tools such as data profiling and cleansing should be employed in order to identify or remove erroneous data. Profiling ensures that you do not lose your customers even when there are changes in some of their data held in the database such as addresses. Data cleaning According to Rahm and Hong, data cleaning, also called data cleansing or scrubbing, deals with detecting and removing errors and inconsistencies from data in order to improve the quality of data. Data quality problems are present in single data collections, such as files and databases, e.g., due to misspellings during data entry, missing information or other invalid data. When multiple data sources need to be integrated, like in data warehouses, federated database systems or global webbased information systems, the need for data cleaning increases significantly. This is because the sources often contain redundant data in different representations. In order to provide access to
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accurate and consistent data, consolidation of different data representations and elimination of duplicate information become necessary. (Rahm & Hong) Data profiling, also called data discovery or data auditing, is specifically about discovering the data available in an organization and the characteristics of that data. Data profiling is a critical diagnostic phase that arms the firm with information about the quality of their data. This information is essential in helping determine not only what data is available in the organization, but how valid and usable that data is. (Corporation) By undertaking data cleansing and data profiling institutions are able to use the information about its candidates to improve its services and ensure that the data held is of high quality. User perception on data quality When we look at the statement that, “the biggest obstacle to improving data quality is that business managers view data quality as a technical problem”. It is true that there have been misunderstandings that technology is the cause of data quality problems when in actual fact it is the result of user actions. Jason C Whitehead, president of Trituns Innovation LLC said ‘Data is the life blood of technology systems’. The data is what actually gives life to the systems. Without data to fuel the systems the technology is of no value. Data is ubiquitous in organisations and therefore ensuring that data is consistently of high quality is very cardinal. In resolving data quality issues managers emphasise on acquiring new technology (software and hardware) to address the problems. All this shows that managers believe that the cause of data quality problems is nothing else but the technology. Whitehead further says that one of the biggest barriers to correcting data quality problems is the widespread misunderstanding of the nature and cause of problems. He also agrees that managers conceptualise data quality as a system or technical problem when in actual fact poor quality data is a behavioural problem. Data is entered, monitored, utilised and updated by users. Thus users have the ability and responsibility to ensure the quality of the data that they input into the computer systems. More often than not data errors are blamed on the technology. It is important to help managers and users realise that the quality of data input in the system will also determine the quality of the output. The notion ‘Garbage in Garbage out’ has not been clearly understood by the managers and users.
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Yes technology (hardware and software) would assist in enhancing quality but the key here are the users to ensure data quality. Responsible user behaviour is important in ensuring that they deliver the desired data quality. In addition organisations should adopt a comprehensive data quality program built on principles of prevention, detection, correction and accountability. Organisations seeking to implement such a program should seek assistance from individuals with expertise in creating an environment that drives desired behaviour. Business value of data quality According to Aberdeen Group research, Data quality-related problems cost companies millions of dollars annually because of lost revenue opportunities, failure to meet regulatory compliance or failure to address customer issues in a timely manner. Poor data quality is often cited as a reason for failure of critical information-intensive projects. By implementing a data quality program, organizations can:
Deliver high-quality data for a range of enterprise initiatives including business intelligence, applications consolidation and retirement, and master data management
Reduce time and cost to implement CRM, data governance, and other strategic IT initiatives and maximize the return on investments
Construct consolidated customer and household views, enabling more effective cross-selling, up-selling, and customer retention
Help improve customer service and identify a company's most profitable customers
Provide business intelligence on individuals and organizations for research, fraud detection, and planning
Reduce the time required for data cleansing. (Aberdeen Group research)
This view would help in public examinations to reduce on candidate queries, produce high quality results and reduce the time it takes to process and then release the results to the candidates. 3.0 METHODOLOGY The field visits to the examination centres in eight (8) out of the ten (10) provinces of Zambia was undertaken by Internal Audit section. The exercise involved obtaining the Provisional Registers from the Schools/Centres visited and compare them to the ECZ data generated after processing and the payment schedules that are generated by schools when the candidates are paying the
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Examination fees (for grades where fees payments are still applicable). Examination centre visits were undertaken to carry out the exercise effectively. The three levels of school examinations (Primary, Junior Secondary and Senior Secondary) were targeted because that was where massive data was generated. That would give a good indication on the extent of data quality problems in public examinations. 4.0 FINDINGS Data audits by the internal audit section of the examinations Council of Zambia revealed the following:Examination fees Shortages During the audit cash shortages were found in a number of centres. It was during this process that some of the centres deposited the examination fees. The table below shows the number of centres in each province that did not account for all the examination fees collected. Copperbelt province recorded the highest number of centres with fees shortages. Table 1: Examination fees shortages Province
Number of centres
Southern
20
Eastern
20
Copperbelt
27
North Western
10
Central
19
Western
16
Lusaka
14
Muchinga
04
Disparities in number of Candidates registered between ECZ and School Records Disparities were noted in the number of candidates registered by the Examinations Council of Zambia and the candidates registered by Centres. The table below indicates the Centres where such disparities were noted. These disparities were detected after comparing the provisional registers maintained by the Centres and that of ECZ generated from the examinations processing system. Central province had the highest number of centres with difference from what was generated at ECZ and the data that was kept in centres. 10
Table 2: Centres with differences in the number of candidates registered. Province
Number of centres
Southern
08
Eastern
13
Copperbelt
24
North Western
13
Central
46
Western
09
Lusaka
33
Muchinga
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Non Registration of Candidates that have paid Examination fees. It was noted that some schools/centres did not register some candidates despite such candidates having paid examination fees in full as required. The table below shows the schools/centres where such observations were noted. Copperbelt again recorded the highest number of centres that did not register candidates who paid examination fees. This is the same province that had the highest number of centres with examination fees shortages. Table3: Number of Centres with Candidates not registered but paid examination fees. Province
Number of centres
Southern
17
Eastern
13
Copperbelt
51
North Western
08
Central
25
Western
12
Lusaka
0
Muchinga
07
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Registration of Candidates without Proof of Having Paid Examination Fees. There were schools/centres that were found to have registered candidates without proof of having paid the prescribed examination fees. Southern Province had more centres registering candidates without proof of payment of examination fees. Table4: Centres with registered candidates without proof of payment of examination fees Province
Number of centres
Southern
14
Eastern
09
Copperbelt
13
North Western
04
Central
07
Western
06
Lusaka
10
Muchinga
02
Non Issuance of Receipts to Candidates It was noted that some schools/centres were collecting and depositing examination fees into ECZ bank account without issuing receipts to candidates as required. According to the financial regulations on examination fees, Centres are required to issue receipts to candidates as they pay the examination fees. Examination Centres captured under wrong districts It was noted during audit that some grade seven (7) examination centres were appearing under a wrong district as shown in Table 5 below. Central province recorded the highest number of centres that were captured under wrong districts. Table 5: Number of centres in wrong districts Province
Number of centres
Southern
06
Eastern
0
Copperbelt
06 12
North Western
02
Central
16
Western
02
Lusaka
10
Muchinga
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Non Adherence to the Prescribed Procedures on Accounting for Examination Fee. It was noted that some DEBS offices and schools/centres did not follow ECZ prescribed procedures and system on accounting for collected examination fees. Departures were noted at various DEBS offices in that the money was not immediately deposited into the ECZ account. Non Adherence to the Prescribed Procedures on registering candidates for examinations. It was observed that schools/centres registered and ECZ admitted candidates that did not meet the registration requirements in that either the candidates were repeaters or had invalid examination numbers. This was contrary to the regulation that candidates sitting the examination as internal candidates at School Certificate level should not repeat. Failure by centres to avail documents used in the registration process It was noted during audit that some Centres could not avail all financial documents used in the accounting for examination fees. This made it difficult to verify the data because there were no source documents from the centre for making comparisons. 5.0 Success since the exercise begun This quality control intervention measure by the ECZ has yielded positive results as follows:
Candidates that may have paid the prescribed scale of fees but not registered are then included on the register before the examination.
Candidates that may not have paid the examination fees and correct scale of fees but are registered are then asked to pay the required fees.
Candidates that are illegally included on the registers are excluded from the registers.
Duplicated candidates are immediately deleted.
The ECZ had been able to collect all the expected revenue from the Centres.
Where centres were found to be in wrong districts, they are immediately corrected to ensure that examination materials are delivered to the correct district.
All in all errors and omissions are identified and corrected timely resulting in the institution having the correct data. Previously candidates who were not registered were sitting for the examinations using the Under Protest window - which have proved to be very costly for ECZ in terms of both time and financial resources incurred in trying to investigate and formalize the registration after the 13
marking exercise. Under-protest means a registered candidate sitting an examination in a subject that they were not entered for. 6.0 Who is responsible for data quality? This seems to be a difficult question to answer due to the user perception that data quality is a technological issue. From the findings above, it is very clear that data quality problems are as a result of the user, because as mentioned by Whitehead the users are the ones who enter the data and have a responsibility of ensuring that correct examination data is entered. 7.0 Impact of data problems on public examination administration The problem of data quality on the administration of examinations by the ECZ has been felt in the following ways: Loss of Council revenue due to misappropriation of examination fees. Candidates not sitting for the examination despite having fulfilled all the registration requirements. In addition, Candidates sitting the examination as under protest. Loss of confidence by the stakeholders. Loss of income due to non-payment of examination fees. Understatement of income due to fraudulent accounting Failure to correctly account for collected examination fees by districts. Wasteful expenditure in redirecting examination materials to the right districts where centres appear in wrong districts. In the process, too much handling of examination materials which can lead to examination leakages. Possible inaccuracies in the accounting of examination fees. Wasteful expenditure arising from printing excess examination materials where you have double registration of centres and candidates. 8.0 Obstacles to improving data quality Non-compliance with the stipulated guidelines and procedures on Administration and Management of Examinations in Zambia by the officers entrusted with the responsibility of accounting for examination fees as well as registering candidates. Delays by centres to follow up on data disparities once the statements of entry have been received by the centre. Lack of quality control measures in centres when verifying and processing candidate entries. Inadequate pools of skilled persons at user levels, the use and rapid obsolescence of the ICTs due to continuous technological innovations and development. 9.0 Recommendations It is recommended that the Schools/Centres that were found with examination fees shortages be reminded to deposit examination fees as required. Management through Finance and Accounts 14
Department should write to the schools/centres that were found to have misappropriated ECZ funds urging them to pay back the misappropriated funds. It is further recommended that the affected DEBS Offices who do not separate the examination sessions when receipting examination fees should be written to through the Provincial Education Offices reminding them on the need to account for examination fees according to the level and session as required. Where candidates paid the examination fees, the affected districts should be reminded of the need to ensure that all paid up candidates are dully registered for examinations. Further the centres should be encouraged to ensure that there is coordination between accounting officers and guidance teachers in schools to avoid omission of candidates at registration stage. Centres should be reminded on the need to issue receipts to all candidates that have paid examination fees before depositing such collected examination fees as required. It is recommended that the centres be reminded on the need to register only candidates that have paid examination fees as stipulated in the guidelines on Administration and Management of Examinations in Zambia. As it has been observed that there were differences in the number of candidates registered between the ECZ records and those of schools/Centres, it is important that the affected districts reconcile these differences with ECZ to avoid candidates being omitted from sitting the examinations. In addition ECZ should devise systems that would help schools or centres verify the final records of registered candidates way before the examinations are conducted. The problem of centres appearing in wrong districts should be quickly addressed to avoid incurring costs on non-existent or duplicated centres. In addition, the entire system of registration of centres should be revisited in order to realign centres to correct districts. A deliberate policy on how the candidate data was going to be checked should be put in place. To resolve the data quality problems, Management needs to re-examine its data quality efforts to ensure that it complied with the provisions of the rules and regulations in examinations administration. Implementing both commercial and home-grown systems for data profiling and matching tools to examine and correct data sent to the examinations processing system. A combination of the two would work well in that where the commercial software failed to meet the needs of the firm, the in-house software would take care of those short comings. Also in terms of software support, it becomes easier with a home grown product than the commercial product. 10.0
CONCLUSION
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From the findings above, it can be concluded that data quality problems are as a result of errors committed during data entry and it is the responsibility of the users to ensure that the data entered is of high quality. Further it is important to note that improving data quality is a long-term task, and many of the measures are organizational in nature. However, data quality should be an issue from the start of any implementation process, and there are some things that can be addressed at once. The recent innovations by ECZ have become central to the process of business development in the institution. Information technology has offered new ways of exchanging information, and transacting business, changed the nature of the financial and other service sectors and provided efficient means of using the human and institutional capabilities by stakeholders. The world is rapidly moving towards knowledge-based economic structures and information societies, which comprise networks of individuals, firms and countries that are linked electronically and in interdependent relationships. In summary, the collaboration between Internal Audit and IT Department has helped strengthen the data capturing and processing procedures at ECZ by ensuring that issues of candidate queries arising from the registration, sitting of examinations, processing and certification due to poor data quality have drastically reduced which has improved the image of the institution to its stakeholders and also reduced the overall cost of doing business.
References: Data quality. (2014, July 18). Retrieved July 30, 2014, from Wikipedia, the free encyclopedia: http://en.wikipedia.org/wiki/Data_quality An Introduction to Data Mining. (n.d.). Retrieved July 21, 2011, from thearling.com: http://www.thearling.com/text/dmwhite/dmwhite.htm Baumgarten, A. (2007). Data Cleansing for Data Warehousing. Retrieved July 21, 2011, from Data Warehousing Review: http://www.dwreview.com/Articles/Data_Cleansing.html Corporation, D. (n.d.). Data Profiling. Retrieved July 4, 2012, from DataFlux Corporation: http://infoimpact.com/articles/Data%20Profiling%20White%20Paper1003-final.pdf Improving data quality. (n.d.). Retrieved from https://www.dhis2.org/doc/snapshot/en/implementer/html/ch13s03.html Laudon, K. C., & Laudon, J. P. (2007). Essentials of Business Information Systems (7th ed.). Upper Saddle River, New Jersey, USA: Pearson Education Inc. Laudon, K. C., & Laudon, J. P. (2007). Management Information Systems: Managing the Digital Firm (11th ed.). Upper Saddle River, New Jersey, USA: Pearson Education, Inc. 16
Lucey, T. (2005). Management Information Systems (9th ed.). London: BookPower. Papaids. (2008, October). Case Study: What Can Be Done About Data Quality? Retrieved December 17, 2012, from StudyMode: http://www.studymode.com/essays/Case-Study-Can-Done-About-Data167976.html Rahm, E., & Hong, H. D. (n.d.). Data Cleaning: Problems and Current Approaches. Retrieved July 21, 2011, from http://wwwiti.cs.uni-magdeburg.de/iti_db/lehre/dw/paper/data_cleaning.pdf Scalable. (n.d.). Retrieved September 17, 2012, from Webopedia.com: http://www.webopedia.com/TERM/S/scalable.html Thomas, R. (2003). Building qualitative and quantitative research methods in theses and dissertations. Thousand Oak: Corwin press inc. Turban, E., Mclean, E., & Wetherbe, J. (2004). Information Technology for Management (4th ed.). New Delhi, India: John Wiley & Sons.
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