Adaptation of Information Quality and Assurance Management Paradigm in a Strategic Public Sector Organization

Information quality despite being a critical area in organizations lacks in comprehensive methodologies for its adaptation and improvement even after years of active research and practice. Therefore adaptation of information quality while ensuring information assurance has become a major concern for strategic organizations to upgrade their IT (Information Technology) Assets. The issue exists specifically in organizations with integrated access to distributed data . In this article, a new model is proposed that is derived after addressing issues in already existing information quality and information assurance adaptation models, showing the methodology and its advantages for strategic organizations. However, this model has to be adapted to the requirements and needs of an organization; since it is a reference model. The steps and procedures are identified in quality and assurance adaptation model to bring abstract standard into practice.


INTRODUCTION
I n today's era, information data produced and stored by organizations is far greater than ever before.
Subsequently, excess of information along with insufficient management of information quality has resulted many organizations into leading to an uncertain situation in information quality and assurance. In addition, evaluation, development and management of data has proved to be very difficult to perform. Information quality management in organization is a mission success essential factor. Information quality management certifies availability of correct data at the right time to the right individual helps make an effective decision making process. An organization that has long term effects on the stature and well-being of society can be termed as data architectures, organization's data resides in its own data centres as well as distributed across remote locations or at public and private clouds. Hybrid cloud architectures provides considerable major economic advantages, yet their implementation demands a more mature and experienced approach to information assurance; this will include integrated monitoring and management capabilities as well as information security management at the level of each individual digital asset. Models for adaptation and implementation of information quality and assurance exist but all pose certain limitations in application to different sets of organizations. This paper will provide a comprehensive information quality and assurance adaptation and implementation model for strategic organizations.
The purpose of this study is to analyze issues in existing information quality models and propose a model that addresses all the current issues while ensuring security in any government organization with strategic objectives.
The rest of this paper is organized as follows. In Section 2, we provide a brief literature review, followed by considerations, issues involved in formulating an information quality and assurance strategy in Section 3.
Section 4 proposes a Data quality and assurance model that addresses the issues outlined in section 3. Section 5 presents the implementation results of proposed model in two public sector strategic organizations and Section 6 discusses the findings. Section 7 concludes the paper by summarizing the work done and provides research direction for further work.

RESEARCH OBJECTIVES
The notion of information quality adaptation model includes different aspects organizational operations and quality information classifications. The fact remains is that there does not exist a model of information quality adaptation that can address the existing issues in existing models and ensuring information security perspectives.
In this paper an attempt is being made to propose an information quality adaptation model designed to focus on strategic organizations of all types. The model will ensure resolving the existing problems in existing major industry adopted models with an added feature of address assuring information security at each and every individual level. There is no consensus about terminology and methods of quality because quality is determined from different viewpoints and aspects [3]. According to Ehlers [4] quality is a multi-front concept with many interpretations. A definition of quality proposed by Juran [5] that is used commonly is "fitness for purpose". Similar to "Strategy", Quality is also dependent on goals and objectives of an organization.

LITERATURE REVIEW
The concept of TQM (Total Quality Management) has been implemented in certain sectors for generic purposes, such as, higher education management [6], information systems management [7] and software development [8].
On the other hand, for certain precise purposes many other models have been proposed, for example, for information quality metrics [9] and for education and training performance [10].
The main idea behind maintaining strategic information quality as discussed by Coleman [11] is that such an organization is strictly required to identify that in what manner its data uses and data itself will change over time.
As data of an organization affects all of its facets and at the same time with passage of time uses of data change over time, therefore, maintaining data with high-quality demands a total and wholesome emphasis on producing good data. Issues associated with information quality can cause harm to an organization in form of inadequate information assurance measures that may range from losing user's confidence and customer's mistrust to loss of mission and even human life. This leaves that, in a strategic organization information quality management along with information assurance are among key mandatory requirements for its mission success.
The information quality and assurance adaptation model is mainly an issue that roots to continuous management supervision and monitoring to measure and analyze data through assessments, verification and validation. Experts in quality control methodology [12] always recommend addressing the "root cause". Anticipated information quality metrics including accuracy, completeness, timeliness, consistency, conformity and record duplication and anticipated information assurance metrics including, confidentiality, integrity, authentication, non-repudiation of data should be considered while addressing the root causes of information quality and assurance.

ISSUES IN EXISTING INFORMATION QUALITY AND ASSURANCE MODELS
The quality of information has transformed into a major apprehension for strategic organizations and a major area of research in Management Information Systems. In organizations, the demand for and awareness of high quality information has increased growth of data warehouses and access to data from different sources for administrators and higher management. In Data Quality model presented by Johns [13], more focus has been made on health care data. The health care industry data models are technically strategic in nature as such data do not require change over a longer period of time. Therefore, health data models cannot be equally applied to other organizations with strategic objectives. Changes and modifications in health care information systems do not require changes in the data that is classified to be quality data. Insufficient or incomplete knowledge and understanding of TQM, lack of commitment by management, incompetent teamwork and incapability to modify or alter organizational culture are main steps to be addressed and overcome for successful quality management [14]. Another model -Quality adaptation model by Pawlowski [3] proposes that four main steps are necessary to successfully adapt and implement an system. On the other hand, information assurance model frameworks [16][17] lacks addressing the information quality concerns in their models by only focusing the information assurance goals and objectives.
Based on the analysis discussed regarding existing information quality models, it can be concluded that the system clearly lacks availability of a comprehensive model that can be used to implement information quality in a public sector organization with strategic goals.
Further to the issues discussed in the previous paragraph, some additional information quality and assurance areas of concern are lack of continuous monitoring of the whole adaptation process and addressing the root causes of information quality and assurance issues.

Continuous Monitoring:
Includes measuring, analyzing, and then improving a system at each individual phase while adhering to the principals of TQM which is also required to effectively confront performance issues.
In US DoD [15] research, it is mentioned that root causes behind problems in information quality will always be triggered by one of the following areas.

System Problem
Problems in information quality mainly strengthen due to limitations in system design and aided by improper modifications in documentation with partial or no training of users or caused by the systems that lacks scalability.

Policy and Procedure Problem
Another reason of information quality problems can be either contradicting guidance in present policies and SOPs (Standard of Procedure), failure to follow the present policies and SOPs or deficient in suitable guidance.

Data Design Problem
There exists a possibility that the data errors are allowed into the system by database itself due to batch loads, improper user privilege specifications and use of incomplete data constraints.
In addition to above mentioned root causes of information quality, another area of grave concern in modern era for public sector strategic organizations is Information Assurance in all project plans that involve architecture modification.

Lack of Information Assurance Capabilities
Existing models of information quality do not address security concerns and typically Information Assurance concerns are ignored to achieve better performance while implementing.

PROPOSED MODEL -HYBRID INFORMATION AND QUALITY ASSURANCE
The proposed information quality assurance model is Application to strategic organization with broader goals and objectives including both information quality and information assurance.
Root causes of data quality problems are addressed.
Monitoring process throughout for achievement of desired goals and achievement of assurance objectives.
Adaptation in a strategic organization.
The proposed stages of adaptation of data quality are outlined in Fig. 1. It begins with formulation of a data strategy by creating system awareness, and requirements identification, its implementation followed by verification and validation to ensure meeting objectives. The model also encompasses evaluation of achieved output. All the phases proposed in this model will include addressing primary root causes of data quality issues outline in previous section as well as the process of continuous monitoring will be executed in parallel.
The activities involved in these phases are further elaborated and explained as follows.

Data Strategy
As first step in the HIQA model process, establishing the right secure environment for information quality management is supposed to be among the most challenging steps in the process. Creating information quality and assurance environment will include involvement of both information system administrators and functional users. Fig. 2 depicts Data Strategy broadly.

Awareness
The

Requirements Identification
The success factors for quality should be defined by building confidence and consensus among all stakeholders. All requirements should be defined in this phase; this includes requirements that may be classified as quality data and identifying the information assurance measures to be implemented in the system keeping in view any system changes and modifications and objectives and goals of organization.

Plans Formulation
Implementation plans shall be formulated from the viewpoint of project management and focused on following:

Implementation
Usually small actors are involved in making the concept successful in the initial stages of implementing an adaptation process. This increases importance of formulation of a comprehensive implementation strategy with robust security measures outlining activities and actions of quality system. According to Thiagarajan and Zairi [18] involvement of all the stakeholders in the quality adaptation process is of vital importance. Also it is pertinent to mention that, familiarity with information and quality assurance system by all stakeholders does not necessarily require all staff members in order to maintain the secrecy of classified information functions. After completion of the implementation phase (Fig. 3), a comprehensive verification and validation test will be conducted to quantify and qualify the outcomes.

Verification and Validation
In verification phase, the process is assessed for correctness of data, its completeness and conformance to defined procedures or predetermined requirements. Documenting after ensuring that output data is what it is supposed to be is the primary aim of verification. It verifies the correctness of data output with focus on achievement of preset security goals.
The validation phase is primarily aimed to determine the analytical quality of a specific data set. These validation measurements focus on the main Quality Assurance plans and objectives and to determine whether the associated security measures are implemented correctly. An individual(s) who is not directly or indirectly part of the activity being performed should primarily be performing the validation phase. Broader objectives of this phase are mentioned in Fig. 4.

Evaluation
The last step of HIQA, evaluation and assessment of any improvements/progress is carried out that is achieved during the whole project. These improvements will be examined with respect to: (1) modifying or current methodology to adapt data quality management and/or (2) find whether or not the project has led to any achievements and accomplishments of its goals and objectives (3) Information Assurance parameters set at the start of process are met or not. The considerations of this step are mentioned in Fig. 5.
A quality system demands high requirement of continuous evaluation, updating, and enhancements in conjunction to the novel developments in a strategic organization. The information quality will be evaluated to find whether or not the quality adaptation system has guided to overall improvements in organization's performance. The evaluation phase also includes assessment of quality metrics and any improvement actions required at a different point of time as for example in system design modification.
The outcome of this phase is an evaluation strategy, improvement concepts, and, most important, a broad discourse on quality.
The pyramid shown in Fig. 6 shows complete proposed model that includes continuous monitoring and addressing main root causes of issues in data quality models at each individual process phase. This also remains a distinguishing characteristic of proposed model.

ANALYSIS OF HIQA MODEL
The methodology used to analyze efficiency of model is Action Research, in which links practice and theory to solve problems that are more of a practical nature [19] [20]. Kemmis and Mctaggart [21] developed one of the most widely used approaches to action research.
Each action research cycle consists of the following steps ( Fig. 7): Plan: Formulate a plan of action aiming to increase efficiency of current practices.
Act: The members responsible for implementing the plan act mutually to execute the plan.
Observe: Through evaluation of outcomes, the actions are observed to collect evidence. Each cycle may guide to perfection in the initial logic (M1), this will lead to more refined ideas in a row. M2, M3 etc.

Justification for Research Method Selection
At this stage of research, Action research is chosen to be a suitable research method for following main reasons: It is a field based method, which allows evaluation of metrics "in an organizational context using real practitioners", as recommended in [22].
It is a qualitative method, and is therefore suitable for exploratory research [22].
Cost of the implementation of this model is supposed to be high as at every stage it contains continuous monitoring, but keeping in view the nature and objectives of strategic organization, it cannot be ignored.

Organizational Context
Action research was conducted in two public sector strategic organizations where the model was completely implemented. Main stakeholders identified in the action research included indirect users who were project modeling analysts that are accountable for data model development.

FINDINGS
Instead of reporting the results of each action research phase, the results are summarized as the cycles were conducted in a cumulative approach.

Number of Phases
In both government organizations, there were only a limited number of people qualified or available to conduct reviews, and the need to calculate the value of each phase added significantly to their workload. A basic philosophical difference is highlighted at this point, differentiating practice from research: Research tends to strive for completeness and closure -in this case, to measure all possible aspects of quality.
Practice tends to focus on what is necessary to get the job done -To measure only those aspects that are most important for improving qualityan "80/20" approach. This reflects a focus on utility.
The need for completeness in measuring information model quality must be balanced with need for the measurement approach to be practical and useable.

Usage of Phases
The reviewers while implementing model expressed their concern over how exactly phases of model should be interpreted. However a productive outcome of study was to explain how the stages could be used. Mainly, following possible uses of each phase was identified: To identify the most efficient option among alternate models available (model choice) To enhance the model quality. (product quality) To carryout comparison of different projects and to improve the process of data modeling over time (process quality).

Use of Phases is a Cost-Benefit
Quality adaptation and adoption is always costly and is deployed if the perceived benefits outweigh the efforts required to deploy them [23]. However, in a public sector

Validation of Phase
In a public sector strategic organization, it is not affordable to take chances for greater prosperity of public associated with the organization and for this reason cost of availing quality is ignored keeping in view the outcome. It was commonly expressed by reviewers that, following phases of model is a generic process to achieve information quality and assurance but to ensure its validation for a longer period of time to ensure its strategic nature it is highly suitable to conduct the following two operations in each phase.
Addressing the root causes of Information

Quality and Information Assurance
Continuous monitoring for achievement of desired goals and objectives of organization.
The proposed model can equally be applied in organizations that include public sector organizations such as, health department, education commissions, armed forces of a country and poverty alleviation departments.
As discussed earlier, implementation of information quality and assurance adaptation process and achievement of information quality in strategic organization requires ignoring cost of implementation, thus making the achievement of objectives more feasible. The results achieved would not have been possible otherwise by implementing the process in a laboratory.

CONCLUSIONS
In this paper, suitability of current quality and assurance standards in practice and their use in different organizations were discussed. To implement a comprehensive information quality and assurance system in a strategic organization, major phases involved are: formulating a data strategy, its implementation, verification and validation of the process and evaluation of the overall output. All phases of the model will be performed with a variety of actors to build awareness and consensus. To facilitate this process and to develop a quality system for an organization, continuous monitoring of objectives at each stage with focus on information assurance objectives and addressing root issues associated should be addressed. For future, it is anticipated that to support this process and to integrate quality into a broad range of strategic organizations, a range of tools will be available.
An unexpected finding was that qualitative information, in the form of textual descriptions of quality issues, was perceived to be the most valuable output of the review process. Cost of the implementation of this model is supposed to be high as at every stage it contains continuous monitoring, but keeping in view the nature and objectives of strategic organization, it cannot be ignored.
It is much more difficult to measure quality of a logical specification than a finished product (e.g. a working system). The conclusion from this research for data modeling practice is that a combination of "hard" and "soft" information provides the best solution to the problem of evaluating the quality of information models.
Since the proposed model is a process based model laboratory analysis of high-level process based models are not feasible to provide accurate information, development of tools and methods for empirical analysis are the area that requires active research to develop tools for empirical analysis of such models.