Impuesto Sobre la Renta

Few articles were published regarding the assessment of road accident data towards an improved quality data as earlier discussed. Generally, some articles discussed the effect of poor road accident data processing on the possibility of reducing the continual occurrence of accidents; while some only discussed the causes of poor quality of road accident data. More so, a few researchers pointed out areas or factors that contribute to poor quality of road accident data, but no suggestions were offered on how to tackle the problems.

Fundamentally, with the intention of achieving a quality data, it is imperative to assess the data sourcing procedures at the local level. Since the degree of data sanitary achieved at this level defines the quality output of the data processed at the provincial level. As a result of this, a practical evaluation of the procedures used in sourcing road accident data is essential. The evaluation process sensitises the quality level of data captured at the local level, through a practical assessment of the data with regard to the output results realised. Before this could be achieved, significantly, a feasible practice of appropriate assessment techniques designed for such case must be carried out. This demonstrates the basic purpose or importance of studying previous assessment approaches implemented by researchers, with reference to the improvement steps or procedures adopted in revitalising the quality level of data collected or processed at the local level.

Most articles or reports studied, performed the evaluation process for data quality improvement in different organisations. However, the practical understanding of the approach applied can

be integrated into the procedures followed in the STD. This action galvanises the evaluation of the data collection procedures and the information/data representation on the data form. Many organisations are obliged to perform radical improvement of data quality as merit towards the appropriate decision-making process. Basically, the quality of data processed has a huge influence on the outcome of the decisions attained, regardless the level of improvement implemented towards the correctness of the data sourced.

According to Karr et al (2005), “data quality problems and actions are driven by decisions based on the data” (Karr et al. 2005). Two other researchers, Fisher & Kingma (2001) declared that the gravity of the decisions make is determined by the level of flawless of the information acquired through an unbiased process. If the expected outcome comes otherwise, then the process is considered corrupt or bias, which means it accommodates unobserved errors that required evaluation process (Fisher & Kingma 2001). This erroneous condition is mostly experienced when an organisation is incapable of revamping their information from an elementary data processing system to an advanced data processing system.

Moreover, a local municipality in the eastern part of South Africa published a report paper on the road accident data management procedure. The report paper encompassed the attributes of assessing the protocols implemented for road accident data collection within the East London area and King Williams Towns, under the jurisdiction of the Buffalo City Municipality [BCM]. From the report, it was depicted that the primary implementation of the assessment process was executed to determine or identify the problems preventing the department in acquiring quality data.

As a result of this, evaluation process implemented leaped from the duplication of road accident data, and detection of incorrect representation of accident locations to non-existence location coordinates (BCM 2005). The department established practicable steps to redress the problems mentioned. The evaluation process executed herein focused directly on the data collection tool and the RTI provided on the data form. The process was extended towards the establishment of GIS dataset, and protocols were restructured to ensure proper capturing of data (BCM 2005).

The first evaluation process begins with data cleansing process, which incorporates the correctional measures or practicable steps, ranging from data completeness checking, duplicated data eradication, location codes modification, and discarding of uncaptured accident reports to the integration of the historical data with the newly processed data (BCM 2005). After the implementation of these predominant steps; hence, procedures are developed and implemented to sustain the regular or periodic assessment of the application of the data form. According to the objectives stated in the report, the main purpose of implementing these protocols is to develop sustainable processes that ensure accurate data collection and

capturing protocols at BCM. This strengthens the possibility of revitalising the process followed by the reporting officers towards data sourcing.

This evaluation process traversed from the monitoring of operations performed within and outside of the municipality, through a close monitoring of the officers’ competence level towards a proper execution of allocated tasks. The possibility of achieving these outcomes was based on three attributes which are:

 Comprehensive understanding of the protocol followed.  Efficiency of the protocol.

 Propose restructuring of the protocol.

These three attributes predict the assurance of achieving the objectives of the investigation. Another researcher, Batini et al (2009) provided layouts regarding the steps to follow in analysing and comparing data quality. The steps encompass the approach of developing a platform suitable for dimensional assessment of data quality. Unlike the approach used by the BCM (2005), where the investigation was carried out by the personnel of the department. In this context, the investigation team was exempted from studying the process adopted in the department, since personnel involved in the investigation have the knowledge of the process. Batini et al (2009) emphatically recommended that the most reliable way of resolving the poor quality process cultivated by an organisation is by being involved in the process. Obviously, this promotes fundamental knowledge about the procedural system adopted by the organisation. The information obtained by the researcher during the process will be sensitised towards the prospect of identifying the cause of the problem or perhaps the areas that are vulnerable. Moreover, Batini et al (2009) enumerated the steps involved in evaluating the problems affecting the quality of data in an organisation as:

 Organise the phases and steps that constitute a suitable research methodology platform.

 Develop strategies and techniques appropriate for the assessment and improvement of data quality level.

 Define the right dimensions and metrics that suit data quality assessment procedure.  Identification of the cost types that maybe encountered in the course of performing the

assessment procedure, such as cost associated with poor quality data, which is defined as the process cost caused by the data errors and the opportunity cost due to loss and/or missed revenues; another type is cost of assessment and improvement activities, which is also referred to as direct cost (Batini et al. 2009).

 Understand the nature of data considered in the research methodology; which defines the data type whether as primary data, which comprises unstructured and semi- structured data points, or secondary data which consist of structured data points.

 Understand the information transaction tools implemented for data processing, data modification and data management system in the research methodology (Batini et al. 2009).

 Perform close observation on the authorised personnel involved in the process developed for data updating.

 Evaluate the process followed in the data processing system.

 Evaluate the operations or services [tasks] performed in ensuring regular production of the data.

The attributes outlined above serve as paradigm for the methodology developed for the investigation to be performed in the STD. In the same article, Batini et al (2009) offered no illustrations on the procedure to be followed regarding the evaluation of organisations [authorities] involved, process followed, and operations or services performed as part of the research methodology, but rather explicated the significance and application of data quality dimensions in the data assessment process.

However, more details were offered concerning the first six attributes. The first attribute is

‘phases and steps’, which constitutes the approach developed in acquiring background

information about the organisation protocols and services, suitable dimensional measurements for assessing the quality level of data, and process improvement in selecting appropriate steps, strategies and techniques towards the possibility of achieving data quality goal (Batini et al. 2009). In essence, the first attribute galvanises the decision of channelling focus directly on the assessment procedures suitable for evaluating the problems rendering data quality inadequate. More so, this aspect measures the quality of data collected and the protocols followed through relevant dimensions (Batini et al. 2009).

The second attribute comprises the ‘strategies and techniques’. Under the second attribute, two types of strategies were recommended to guide the evaluation process, depending on the applicability of the strategies towards the process practiced in the organisation. The two types of strategies recommended were ‘data-driven’ and ‘process-driven’ strategies. Actually, strategies can be incorporated into the methods considered in the phases and steps. However, the data-driven strategy is described as ‘an improvement process implemented for cultivating

and improving quality of data processed through a direct modification of the data value (Batini

et al. 2009),’ while process-driven strategy is described as ‘a considerable improvement

strategy implemented for the redesigning of the processes that produce data’ (Batini et al.

2009).

The third attribute is recommended as ‘dimension and metrics’, which basically transpired the quality level of data, subject to sequential implementation of quality assessment processes [refer to subsection 2.5.4 above]. The quality dimensions are implemented to evaluate the level

of purity of the data processed. This particular attribute is significant during information [data] outsourcing and information [data] transaction processes6_{. In addition, these dimensions are} implemented according to their order of relevance to the research performed. This aspect is perceived as a key interest to many researchers in this field, as the process of uncovering the extent of poor quality data. The list of acceptable quality dimensions is provided in the table below according to the description provided by Batini et al (2009).

Table 4: Quality dimension elements

Quality dimensions Descriptions

Accuracy

Extent to which data are correct, reliable and approved (Wang & Strong 1996; Batini et al. 2009); data are adequately accurate provided that, such data collected are practicable to the real-world systems (Batini et al. 2009).

Completeness

Capacity of information system to represent every meaningful state of a real-world system (Lee et al. 2002; Batini et al. 2009; Wang & Strong 2013). Completeness is categorically recognised as the extent to which a data coverage covers all significant features as anticipated.

Consistency

“Violation of semantic rules define over a set of data items” (Batini et al. 2009). This is often experienced during data entering or data coding process. It is also applicable to the evaluation of the data collected with the use of data form, in form of unstructured and semi-structured data format.

Time-related dimensions

Currency

Currency is defined by Redman (1996) in an article published, as the extent to which data is up-to-date or complete as cited by Batini et al (2009). This is defined as the time or period required in the entering process of data collated into the dataset.

Volatility Volatility is simply defined by Jarke et al (2001) as “the time or period, for which information is valid in the real-world (Batini et al. 2009)”

Timeliness

Timeliness is described as the extent to which implementation processes are performed accordingly as regards data processing. Wang and Wand (1996) referred to Timeliness as “the extent to which the age of data is appropriate for the task at hand” (Batini et al. 2009).

The fourth attribute recommended is ‘costs’. However, Batini et al (2009) described costs as “a relevant perspective considered in methodologies, due to the effects of low quality data on resource consuming activities” (Batini et al. 2009). From another viewpoint, costs can be defined in terms of the data collection as ‘evaluation process implemented to quantify the

energy, time and resources exhausted in the course of acquiring and improving quality of data or quality system for data processing’ (Batini et al. 2009). In conjunction with the last statement,

Batini et al (2009) further emphasised that costs of low quality data is measured as the outcome of quality evaluation and improvement actions performed on data collated.

Due to this, management will be able to quantify the effect of poor quality data on the progress of the operations performed within the organisation, and as well, on the services rendered to the public [users]. For instance; cost of preventing regular occurrence of RTA on the South African roads is estimated to R300,000,000,000 [billion] from R210,000,000,000 [billion] annually (SANRAL 2008; SANRAL 2012), as cited in the subsection 2.1.3 above. English (1999) classified cost of poor quality into process and opportunity costs (Batini et al. 2009). Process costs are defined as ‘the cost associated with the recapitulating and reassessing of

whole data process due to errors discovered’ (Batini et al. 2009); while opportunity costs are

referred to as ‘the costs due to lost and missed revenues’ (Batini et al. 2009).

The fifth attribute is identified as the ‘nature of data’. This defines the type of data considering for data analysis. In this case, three types of data are considered, which are unstructured data, semi-structured data and structured data. The sixth attribute is considered as the information

transaction tools. This is valuable in measuring the capacity of the tools involved in the data

collection, data modification, and data management procedures.

The National Institute of Statistical Science [NISS] published a technical report illustrating the steps required in assessing the data quality warehoused in the database through statistical observations (Karr et al. 2005). The institute accentuated on the need to identify organisational issues as the first step to be itemised in the improvement checklist (Karr et al. 2005), which is similar to the recommendations offered by Batini et al (2009) regarding the appropriate methodologies towards the assessment of quality of the data produced. Moreover, many organisations possess the obligation to identify the organisational issues frustrating the process for producing quality data, but they are incapacitate from addressing such issues due to inadequate resources distribution (Karr et al. 2005).

Furthermore, Karr et al (2005) explained the fundamental steps to follow in addressing issues relating to data quality. The report structured these steps as part of the data quality dimensions and assessments. These steps can be considered as mechanisms for data quality improvement. Contrary to the methods discussed by Batini et al (2009), Karr et al (2005) focused directly on the data that are already refined or structured. In addition, the mechanisms are considerably implemented to determine the problems associating with data recording and analysis procedures as discussed in subsection 2.2.4 above. In this regard, the process cut off the assessment of the primary source of the data processed or inputted into the database. The mechanisms consist four essential steps identified as:

 Preliminary screening for data quality –This step was implemented to ascertain the quality effect of the primary assessment procedure adopted for the assessment of the data collected or gathered before being stored in the database. Karr et al (2005) emphatically specified that this particular step is mainly applicable to organisations that

are incapably weak towards quality data production. Moreover, preliminary screening for data quality entails some correctional protocols initiated in reforming the quality of data collected. These correctional protocols are structured into four relevant processes, which are considered as discarding of data, inadequate data resources, data quality

improvement and data improvement not required (Karr et al. 2005). These

aforementioned protocols play important roles in ensuring the need for an improved implementation. The first of the correctional protocols, which is discarding of data, is implemented to ascertain the practicality of the data collated; followed by the

inadequate data resources, which is implemented to check whether resources or

means of improving data quality are ascertainable or obtainable; and thereafter, data

quality improvement is implemented to justify the prudent implementation of the

available resources in terms of personnel, money, time and initiatives; and the final protocol, which is data improvement not required, is implemented to quantify the level of improvement required, whether is low, high or no improvement required at all (Karr et al. 2005). This part of the mechanisms is made feasible through decision making process.

 Exploratory analyses for data quality assessment –this step is applied through appropriate analysis platform developed, wherein a vast overview or background of the quality of the data processed can be observed or acquired through statistical observations. The exploration of the data through descriptive statistics will showcase the practicality of the data acquired. Other important observations could be obtained by understanding the structure of the data [in terms of attributes, missing and incomplete data points, data grouping etc.], characteristics of variables or features, connections between variables and relational characteristics [by checking for incorrect coding in the

database during the creation of relational entities between variables] (Karr et al. 2005).

 Identification of anomalous data elements –this aspect streamlines the irregularities that could be identified during data mining process without accessing the related database. The process can also be performed through critical observations by characterising the data collected. Beside the first approach, according to Karr et al (2005) anomalies can also be detected through the use of robust statistical approach. This approach directs attention on irregularities such as identification of duplicate

records, identification of omitted information and identification of regular misrepresentation of data (Karr et al. 2005).

 Selected methods for data quality improvement –this step determines the right approach towards the data quality improvement. Karr et al (2005) affirmed the relevance or significance of this particular step in identifying any sort of inconsistencies that maybe existing in the database system. Obviously, this is applicable where

inconsistencies are existing among the data points and data definitions (Karr et al. 2005).

The application of these mechanisms is based on the practical understanding of the technical issues frustrating the quality of data stored in the database. From previous observations, it was depicted that human factors are extremely huge contributor of errors committed during data processing (O’ Day 1993; Vogel & Bester 2004; Karr et al. 2005; Vogel & Bester 2005). According to a statement declared by Karr et al (2005), “data quality concerns are problems of large-scale machines and human generation of data, the assemblage of large data sets, data anomalies and organisational influences on data characteristics such as accuracy, timeliness and costs” (Karr et al. 2005).

Technically, errors are periodically, if not frequently committed at the preliminary level of data sourcing [data collection]. The desire to discard or rectify these errors guarantees minimal rate in errors committed during transfer of data from recording stage to analysis stage. More so, human factors should be prioritised as the major concern in the process of restructuring the degree of quality of the data assembled in any organisations. To buttress this statement further, Karr et al (2005) stressed that human factors are often the difficulty part in the quality data achievement because they are the controller of the data collection equipment. In order to complement the efforts of procuring quality data, Fisher and Kingma (2001) advised the data management to consider information [data] collected as a critical product, not simply as derivative of the process.

A group of research institutes published a book on how to design, improve and implement data