Exploring the Impact of Data Quality on Decision-Making Processes in Information Intensive Organizations

This study investigates the influence of data quality on decision-making processes within organizations that heavily rely on information for their operations. With the increasing digitalization and proliferation of data in today's business landscape, the quality of data has emerged as a critical factor in ensuring accurate and effective decision-making. Through a comprehensive review of existing literature and an empirical analysis, this research aims to shed light on the relationship between data quality and decision-making outcomes. The study employs a mixed-methods approach, utilizing both qualitative inter-Keywords


INTRODUCTION
In today's rapidly evolving business landscape, organizations across diverse sectors are undergoing a seismic shift driven by technological advancements, digitalization, and the unprecedented availability of data.This era of interconnectedness has ushered in the rise of information-intensive organizations that harness data's power not only to drive strategies but also to navigate the complexities of the modern market.Amid this transformation, the quality of data has emerged as a linchpin in decision-making processes [1].This paper embarks on a journey to explore the intricate relationship between data quality and decision-making within these organizations, aiming to unveil the multifaceted impact that data quality wields on their strategic outcomes.

Data-Driven Transformation and the Significance of Quality
The dynamics of decision-making have experienced a paradigm shift, transitioning from intuitive approaches to data-driven methodologies [2].This shift is propelled by the convergence of advanced technologies like big data analytics and machine learning, enabling organizations to extract actionable insights from vast datasets in real time [3].In parallel, information-intensive organizations have emerged across various industries, thriving on their ability to collect, aggregate, and interpret massive volumes of data.However, the potency of data-driven decision-making hinges on the quality of the data itself.Flawed data can lead to skewed analyses, misleading conclusions, and suboptimal decisions.As a result, data quality emerges as a critical factor that influences the accuracy and effectiveness of decision-making processes [4].

Investigating the Nexus of Data Quality and Decision-Making
This paper's central objective is to unravel the intricate relationship between data quality and decisionmaking processes within information-intensive organizations.By bridging theory and practice, the study aims to shed light on how variations in data quality impact the decision-making continuum [5].Moreover, the research recognizes that the connection between data quality and decision-making is bidirectional wellstructured decision-making processes can foster improved data governance and subsequently enhance data quality [6][7].Through a comprehensive review of existing literature and empirical analysis, this study seeks to provide valuable insights into the diverse dimensions of data quality's influence on decision-making outcomes.It also aims to identify challenges faced by organizations in maintaining high data quality standards and propose practical strategies for enhancing data quality within the decision-making landscape.

LITERATURE REVIEW
In the contemporary data-driven landscape, data quality has emerged as a central tenet that significantly shapes decision-making processes within information-intensive organizations [8].This section undertakes a thorough examination of the existing body of literature surrounding data quality and its intricate interplay with decision-making outcomes.By delving into various dimensions of data quality, exploring theoretical underpinnings, and investigating empirical findings, this review aims to provide a comprehensive understanding of the multifaceted relationship between data quality and effective decision-making [9][10].

Dimensions of Data Quality
Data quality encompasses a multidimensional construct that reflects the accuracy, reliability, completeness, consistency, timeliness, and relevance of data [11] [12].Accuracy denotes the correctness of data values in representing real-world phenomena, while reliability pertains to the consistency of data over time and its ability to yield similar results upon replication.Completeness underscores the presence of all required data elements, and consistency ensures that data across various sources or instances align harmoniously.Timeliness, often critical in dynamic decision-making contexts, signifies the relevance of data in relation to the timing of decisions [13] [14].Furthermore, the relevance of data to the decision at hand is a pivotal dimension, as irrelevant or outdated data can lead to misinformed choices.This multidimensional nature of data quality underscores its fundamental role in cultivating an environment where decision-makers can rely on data to drive informed actions [15].

The Theoretical Foundation
The literature review not only dissects the dimensions of data quality but also unveils the theoretical frameworks that underpin its significance in effective decision-making.The Resource-Based View (RBV) posits that organizations can gain a sustainable competitive advantage by leveraging unique resources.In this context, high-quality data serves as a strategic asset that empowers organizations to make informed decisions, adapt to changing environments, and anticipate market trends [16] [17].Building on this, the Information Processing Theory emphasizes the role of data in reducing uncertainty and improving problem-solving processes.Quality data acts as a cognitive resource that enhances decision-makers' abilities to process information, enabling them to reach more accurate conclusions in complex scenarios [18].The Fit-Viability Model extends these perspectives, arguing that the fit between an organization's resources and its external environment drives its viability [19].High-quality data enhances this fit by ensuring that organizations possess the right information to navigate external challenges and opportunities effectively [20].

Emperical Insights
In this research explores further empirical studies that provide credence to the theoretical foundation by providing concrete evidence regarding the impact of data quality on decision making.These studies span a variety of industries, each highlighting the impact of data quality on decision-making outcomes.For example, in healthcare settings, accurate and timely patient data influence clinical decisions, treatment plans and patient outcomes [21].In the financial sector, precise and reliable market data drives investment decisions that impact financial performance [22] [23].Additionally, studies in supply chain management underscore the role of accurate inventory and demand data in optimizing logistics decisions.These empirical insights collectively underscore that data quality is not just a theoretical construct but a real asset that significantly influences organizational performance through the quality of decisions taken [24][25].

The Role of Data Governance
A noteworthy aspect of the literature review is the role of data governance in maintaining data quality.Effective data governance involves the establishment of policies, procedures, and responsibilities for ensuring data accuracy, integrity, and security [26] [27].By implementing data governance frameworks, organizations can enhance data quality by enforcing standards, conducting regular audits, and promoting a culture of data stewardship [28].This connection between data governance and data quality elucidates that ensuring data quality is not solely a technological challenge but a holistic endeavor encompassing people, processes, and technology [29] [30].

3.
RESEARCH METHODS In order to thoroughly investigate the effect of data quality on the decision-making process, this study used a mixed methods approach, strategically combining qualitative and quantitative research methods with 105 people.This methodology is designed to capture different understandings of the complex relationship between data quality and decision making in information-intensive organizations.

Qualitative Interviews
The research commenced with in-depth qualitative interviews conducted with key professionals from diverse information-intensive organizations.Through open-ended questions and interactive discussions, these interviews delved into the challenges and benefits associated with data quality in the decision-making context.This qualitative phase aimed to unearth insights that might be obscured in purely quantitative analyses, offering a comprehensive exploration of the human aspects and contextual nuances involved.

Qualitative Interviews
Building upon the qualitative insights, a quantitative survey was administered to a larger and more diverse sample of participants from similar organizations.The survey was thoughtfully designed to gauge respondents' perceptions of data quality's impact on their decision-making outcomes.By quantifying these perceptions, the survey provided a broader perspective, allowing for the identification of overarching trends and correlations within a statistically significant framework.How significant is the impact of data quality on cost management in decision-making within your organization?6 How significant is the impact of data quality on customer satisfaction in decision-making within your organization?7 How significant is the impact of data quality on risk assessment in decision-making within your organization?8 How significant is the impact of data quality on innovation initiatives in decision-making within your organization?9 How significant is the impact of data quality on competitive strategy in decision-making within your organization?10 How effective are data management practices within your organization in maintaining data quality?

4.
RESULT AND DISCUSSION Through the research methods employed, we have uncovered several significant findings that provide deep insights into the relationship between data quality and decision-making processes within information-intensive organizations.These findings hold great potential to inform improvements in data management and decisionmaking in the future.From 8 qualitative questions, various types of responses have been compiled into a table for further analysis.This table helps identify patterns and trends in the responses provided by respondents.Qualitative data can provide deep insights into the views, opinions, or perceptions of respondents regarding a specific topic or issue.Table-based analysis makes it easier to summarize key findings and gain a better understanding of the various perspectives at hand.It can serve as a foundation for better decision-making, strategic planning, or improvements in a project or initiative.It's important to understand and appreciate the diversity of responses that emerge so that decision-making can be supported by rich and diverse data.We once sent an email campaign to the wrong segment due to a data mix-up, resulting in a poor response.Data quality is critical for marketing ROI.From the above graph, it can be clearly seen that Decision-Making Processes hold significant importance within the organization.The total percentage of 76.19%, comprising "Very Important" (33.33%) and "Important" (42.86%) responses, indicates that the majority of respondents believe that data quality significantly influences decision-making.These results affirm the importance of prioritizing and maintaining data quality in the organizational decision-making context.In a competitive and rapidly changing business environment, decisions supported by accurate and reliable data are crucial for achieving objectives, managing costs, enhancing customer satisfaction, and formulating competitive strategies.Therefore, organizations need to continually improve their data management practices to ensure optimal data quality in decision-making.

5.
CONCLUSION In today's dynamic business landscape characterized by technological advancements and the proliferation of data, organizations are undergoing transformative changes.This paper embarked on a journey to explore the profound impact of data quality on decision-making processes within information-intensive organizations.Through a thorough literature review, it became evident that data quality is not just a theoretical concept; it is a pivotal factor that significantly shapes the accuracy and effectiveness of decisions.The transition from intuitive decision-making to data-driven methodologies has been driven by technological advancements like big data analytics and machine learning.However, the success of data-driven decision-making depends fundamentally on the quality of the data being used.Flawed data can lead to skewed analyses, erroneous conclusions, and suboptimal decisions.
Our investigation aimed to unravel the intricate relationship between data quality and decision-making.By combining qualitative interviews and quantitative surveys, we obtained a comprehensive view of this relationship.The qualitative phase allowed us to explore the challenges and benefits of data quality in-depth, while the quantitative survey provided quantitative validation and broader insights.Our findings affirmed the crucial role of data quality in decision-making.The majority of respondents indicated that data quality is of utmost importance in influencing their decision-making processes.This underscores the significance of maintaining high data quality standards within organizations.
Furthermore, our research highlighted that the relationship between data quality and decision-making is bidirectional.Well-structured decision-making processes can foster improved data governance, subsequently enhancing data quality.This insight emphasizes the need for a holistic approach that encompasses people, processes, and technology to ensure data quality.

Table 1 .
Qualitative Interviews open-ended questions and interactive discussions 6 Can you share an example of how data quality has influenced a recent supply chain decision?7 How does data quality impact marketing decision-making in your organization?8 Can you provide an example of how data quality has influenced a recent marketing decision?E-ISSN: 2622-6804 P-ISSN: 2622-6812

Table 3 .
Responses from Qualitative Questions

Table 4 .
The Value of the Response Given by the Respondent