Level Of Data Quality Research Articles

Developing a High Velocity Dataset Quality Checking Pipeline

ObjectiveThe volume and frequency of refreshed data within the [organisation] has increased significantly since the beginning of the COVID-19 pandemic. Therefore, a more efficient data quality (DQ) checking process was necessary. Approach Having previously developed an automated DQ checking tool, the focus was on re-engineering the process of DQ task allocation and communication of results. Results 5 analysts were trained in DQ checking. A JIRA workflow tracks the management of data loading. When a dataset is ready for DQ, the Data Manager allocates a ticket to the DQ Lead who then allocates it onto one of the 5 analysts. Via a DQ Slack channel, the analyst is informed and acknowledges receipt of the task. On completion of DQ, the analyst updates the ticket and transfers it to the appropriate workflow stage. Passed DQ tickets are transferred to the Data Manager for data release, whereas failed ones are placed “On Hold”. The DQ Lead triages the issues and liaises with relevant parties for resolution, which may require data amendments. On receipt of amended data, the DQ ticket is transferred back to the queue and the analyst is notified to re-check the data. ConclusionsThe team now complete a high volume of DQ checks efficiently. In 2023, 405 datasets, containing 1716 tables, were quality checked, with the initial DQ taking, on average, 2.6 days. Implications The improved speed of DQ checking ensures projects access the latest available data whilst maintaining the expected DQ levels, integrity and reputation of the TRE.

Improving Clinical Documentation of Rare Neuromuscular Diseases: Development of a Standardised Information Model.

Rare neuromuscular diseases (NMDs) encompass various disorders of the nervous system and skeletal muscles, and present intricate challenges in diagnosis, treatment, and research due to their low prevalence and often diverse multisystemic manifestations. Leveraging collected patient data for secondary use and analysis holds promise for advancing medical understanding in this field. However, a certain level of data quality is a prerequisite for the methods that can be used to analyze data. The heterogeneous nature of NMDs poses a significant obstacle to the creation of standardized documentation, as there are still many challenges to accurate diagnosis and many discrepancies in the diagnostic process between different countries. This paper proposes the development of an information model tailored to NMDs, aiming to augment visibility, address deficiencies in documentation, and facilitate comprehensive analysis and research endeavors. By providing a structured framework, this model seeks to propel advancements in understanding and managing NMD, ultimately benefiting patients and healthcare providers worldwide.

Assessing Completeness of IoT Data: A Novel Probabilistic Approach

AbstractThe Internet of Things (IoT) is one of the driving forces behind Industry 4.0 and has the potential to improve the entire value chain, especially in the context of industrial manufacturing. However, results derived from IoT data are only viable if a high level of data quality is maintained. Thereby, completeness is especially critical, as incomplete data is one of the most common and costly data quality defects in the IoT context. Nevertheless, existing approaches for assessing the completeness of IoT data are limited in their applicability because they assume a known number of real-world entities or that the real-world entities appear in regular patterns. Thus, they cannot handle the uncertainty regarding the number of real-world entities typically present in the IoT context. Against this background, the paper proposes a novel, probability-based metric that addresses these issues and provides interpretable metric values representing the probability that an IoT database is complete. This probability is assessed based on the detection of outliers regarding the deviation between the estimated number of real-world entities and the number of digital entities. The evaluation with IoT data from a German car manufacturer demonstrates that the provided metric values are useful and informative and can discriminate well between complete and incomplete IoT data. The metric has the potential to reduce the cost, time, and effort associated with incomplete IoT data, providing tangible benefits in real-world applications.

Burden and Inattentive Responding in a 12-Month Intensive Longitudinal Study: Interview Study Among Young Adults.

Intensive longitudinal data (ILD) collection methods have gained popularity in social and behavioral research as a tool to better understand behavior and experiences over time with reduced recall bias. Engaging participants in these studies over multiple months and ensuring high data quality are crucial but challenging due to the potential burden of repeated measurements. It is suspected that participants may engage in inattentive responding (IR) behavior to combat burden, but the processes underlying this behavior are unclear as previous studies have focused on the barriers to compliance rather than the barriers to providing high-quality data. This study aims to broaden researchers' knowledge about IR during ILD studies using qualitative analysis and uncover the underlying IR processes to aid future hypothesis generation. We explored the process of IR by conducting semistructured qualitative exit interviews with 31 young adult participants (aged 18-29 years) who completed a 12-month ILD health behavior study with daily evening smartphone-based ecological momentary assessment (EMA) surveys and 4-day waves of hourly EMA surveys. The interviews assessed participants' motivations, the impact of time-varying contexts, changes in motivation and response patterns over time, and perceptions of attention check questions (ACQs) to understand participants' response patterns and potential factors leading to IR. Thematic analysis revealed 5 overarching themes on factors that influence participant engagement: (1) friends and family also had to tolerate the frequent surveys, (2) participants tried to respond to surveys quickly, (3) the repetitive nature of surveys led to neutral responses, (4) ACQs within the surveys helped to combat overly consistent response patterns, and (5) different motivations for answering the surveys may have led to different levels of data quality. This study aimed to examine participants' perceptions of the quality of data provided in an ILD study to contribute to the field's understanding of engagement. These findings provide insights into the complex process of IR and participant engagement in ILD studies with EMA. The study identified 5 factors influencing IR that could guide future research to improve EMA survey design. The identified themes offer practical implications for researchers and study designers, including the importance of considering social context, the consideration of dynamic motivations, and the potential benefit of including ACQs as a technique to reduce IR and leveraging the intrinsic motivators of participants. By incorporating these insights, researchers might maximize the scientific value of their multimonth ILD studies through better data collection protocols. RR2-10.2196/36666.

In Vivo Demonstration of a Real-Time Temporal SNR Acoustic Output Adjustment Method.

This work proposes a novel method of temporal signal-to-noise ratio (SNR)-guided adaptive acoustic output adjustment and demonstrates this approach during in vivo fetal imaging. Acoustic output adjustment is currently the responsibility of sonographers, but ultrasound safety studies show recommended as low as reasonably achievable (ALARA) practices are inconsistently followed. This study explores an automated ALARA method that adjusts the mechanical index (MI) output, targeting imaging conditions matching the temporal noise perception threshold. A 28-dB threshold SNR is used as the target SNR, following prior work showing relevant noise quantities are imperceptible once this image data quality level is reached. After implementing adaptive output adjustment on a clinical system, the average MI required to achieve 28-dB SNR in an 11-volunteer fetal abdomen imaging test ranged from 0.17 to 0.26. The higher MI levels were required when imaging at higher frequencies. During tests with 20-s MI adjustment imaging periods, the degree of motion impacted the adaptive performance. For stationary imaging views, target SNR levels were maintained in 90% of SNR evaluations. When scanning between targets the imaging conditions were more variable, but the target SNR was still maintained in 71% of the evaluations. Given the relatively low MI recommended when performing MI adjustment and the successful adjustment of MI in response to changing imaging conditions, these results encourage adoption of adaptive acoustic output approaches guided by temporal SNR.

Motor test data quality evaluation model based on critic-fluctuant weight assignment method

Abstract The article addresses the limitations of traditional weight analysis methods in handling ground ignition experiment data for solid rocket motors, particularly the anti-interference ability to noise factors. To address this issue, the specialized Critic-Fluctuant weight analysis method is proposed. This method calculates objective weight values based on fluctuation values and integrates noise standard scores into data quality assessment, enabling researchers to effectively and intuitively identify different levels of data quality. After comparing and analyzing the objective weights and data quality scores of 100 ground ignition test data sets, it is verified that Critic-Fluctuant has higher adaptability and accuracy than traditional CRITIC methods in ground ignition test scenarios. The article provides new insights and tools for researchers to deal with similar data characteristics.

Mind the gap: Facilitating early design stage building life cycle assessment through a co-production approach

Despite the transition towards a circular economy (CE) being a significant element in achieving the decarbonisation of the built environment, a clear and common pathway to applying CE principles to building design is still lacking in both industry practice and academia. The integration of Building Information Modelling (BIM) and Life Cycle Assessment (LCA) has the potential to enable the identification of feasible pathways to increase circularity. This study aims to investigate its practical use to facilitate the application of circular economy principles at the early stage of building design via using LCA-based BIM plugins. Within this aim, the paper centres on a co-production approach and presents a gap analysis by identifying gaps in knowledge and implementation as well as addressing the pressing needs of the current practice in the UK. A series of semi-structured interviews with expert practitioners in the field were conducted for data collection, contributing to the following phases of the co-production by allowing for an in-depth investigation and reflection of the practice. The findings have revealed that: (a) there is still an insufficient level of contextual awareness and readiness in the implementation of CE principles in the built environment and LCA understanding and (b) the adoption of BIM at the early stages of building design is still limited in the current practice; BIM models with sufficient level of data details and quality to enable circularity assessment are rarely produced. Thus, the paper highlights the need for enabling mechanisms, including the introduction of legislative instruments, the involvement and commitment of the industry and key stakeholders, the support for training and skills improvement, and the establishment of effective communication and implementation process management frameworks. Future research agenda points to the need of formulation of a BIM protocol to enable integration of BIM and LCA to promote CE principles in the building design process.

Development and preliminary validation of a platform for systematic, unbiased pre-screening of non-small cell lung cancer (NSCLC) across three community oncology practices.

e23230 Background: With a rapidly increasing number of candidate drugs and clinical trials in oncology, efficiency in clinical trial execution has become a primary focus area for innovation. Currently, fewer than 5% of oncology patients enroll in clinical trials, and data suggests a lack of diversity in clinical trial patient populations compared to the broader patient demographic. To address these issues, we developed a systematic unbiased pre-screening platform for all patients. This approach holds the promise of hastening clinical trials by enhancing the identification of eligible patients and concurrently promoting diversity among those invited to participate in these trials. Methods: A framework for use of EMR ingested data and standardized abstraction of select 160 variables was developed for patients with NSCLC. An algorithm based on 7 key eligibility criteria was developed to generate an automated assessment of eligibility for a presumed trial. Abstracted data was compared to a medical oncologist’s assessment (gold standard) and the automated tool was validated by comparing eligibility assessment before each visit with that of a medical oncologist. Results: Three community oncology practices in the US participated. Source data-verified values for histology, ECOG PS, treatment history, line of therapy, and current metastatic status were available for > 90% of patients in real-time. Tumor NGS testing rates for actionable alterations exceeded 75%. From Oct 23, 2023 to Jan 14, 2024, 786 NSCLC pts had MD visits. Preliminary validation of the automated tool in 79 patients revealed concordant classification in 76/79 pts (96.2%). Sensitivity, specificity, PPV and NPV were > 90%. Concordance for all variables was 98% between the abstractor and medical oncologist. Conclusions: A platform for systematic, unbiased pre-screening of patients for clinical trials in NSCLC has been developed to support and facilitate oncology sites without imposing financial or resource burdens or affecting routine clinical workflow. Preliminary validation supports high performance. Updated validation data will be presented in the meeting. The high levels of data completeness and quality, including NGS testing rates that are substantially higher than what has previously been reported in community centers, underscore their suitability as clinical trial sites.

Crowdsourced Fact-checking: Does It Actually Work?

There is an important ongoing effort aimed to tackle misinformation and to perform reliable fact-checking by employing human assessors at scale, with a crowdsourcing-based approach. Previous studies on the feasibility of employing crowdsourcing for the task of misinformation detection have provided inconsistent results: some of them seem to confirm the effectiveness of crowdsourcing for assessing the truthfulness of statements and claims, whereas others fail to reach an effectiveness level higher than automatic machine learning approaches, which are still unsatisfactory. In this paper, we aim at addressing such inconsistency and understand if truthfulness assessment can indeed be crowdsourced effectively. To do so, we build on top of previous studies; we select some of those reporting low effectiveness levels, we highlight their potential limitations, and we then reproduce their work attempting to improve their setup to address those limitations. We employ various approaches, data quality levels, and agreement measures to assess the reliability of crowd workers when assessing the truthfulness of (mis)information. Furthermore, we explore different worker features and compare the results obtained with different crowds. According to our findings, crowdsourcing can be used as an effective methodology to tackle misinformation at scale. When compared to previous studies, our results indicate that a significantly higher agreement between crowd workers and experts can be obtained by using a different, higher-quality, crowdsourcing platform and by improving the design of the crowdsourcing task. Also, we find differences concerning task and worker features and how workers provide truthfulness assessments.

Comparative analysis of translation platforms: Accuracy, fluency, and unambiguity from a technological perspective

This study aims to evaluate and compare the performance of leading translation platforms (Youdao, Google, Baidu, and ChatGPT) for translating news texts. The evaluation focuses on three key dimensions: accuracy, fluency, and unambiguity. Each platform was tested by translating a Chinese news article, and their outputs were analyzed for their handling of specialized terminology, linguistic fluency, and cultural adaptability. The findings reveal significant performance differences, reflecting varying levels of technological maturity, data quality, and cultural adaptability among the platforms. ChatGPT emerged as the top performer due to its high accuracy and fluency, while Youdao, Baidu, and Google exhibited varying strengths and limitations in handling complex news texts. Ultimately, the results highlight the importance of continuous technological innovation and theory-practice integration in improving translation quality, particularly for accurate and effective global news dissemination.

Does it Pay to Send Multiple Pre-Paid Incentives? Evidence from a Randomized Experiment

To encourage survey participation and improve sample representativeness, the Survey of Consumer Finances (SCF) offers an unconditional pre-paid monetary incentive and separate post-paid incentive upon survey completion. We conducted a pre-registered between-subject randomized control experiment within the 2022 SCF, with at least 1,200 households per experimental group, to examine whether changing the pre-paid incentive structure affects survey outcomes. We assess the effects of: (1) altering the total dollar value of the pre-paid incentive (“incentive effect”), (2) giving two identical pre-paid incentives holding the total dollar value fixed (“reminder effect”), and (3) offering multiple pre-paid incentives of different amounts holding the total dollar value fixed (“slope effect”) on survey response rates, interviewer burden, and data quality. Our evidence indicates that a single $15 pre-paid incentive increases response rates and maintains similar levels of interviewer burden and data quality, relative to a single $5 pre-paid incentive. Splitting the $15 into two pre-paid incentives of different amounts increases interviewer burden though lengthening time in the field without improving response rates, reducing the number of contact attempts needed for a response, or improving data quality, regardless of whether the first pre-paid is larger or smaller than the second.

National Data Management: A Modern Data Stewardship Approach for the Swiss Administration Enabled by the New Platform “I14Y Interoperability”

To create the basis for the implementation of the Once-Only-Principle in Switzerland, the Swiss Federal Council launched a program called “National Data Management” in 2019. The Once-Only-Principle aims to reduce the administrative burden on citizens and companies. Instead of having to contact them repetitively for the same information, the responsible authorities should strive to exchange the required data. To enable this, we need to actively work on the data landscape of the Swiss administration. The concept of data stewardship is known to be an efficient tool to promote and ensure a consistent high level of data quality. Nowadays public administrations of many countries strive to embrace the idea of data stewardship. This paper describes the approach we have developed for the Swiss administration.

Data collection and saturation in exploring the public sector accounting in Bangladesh

The study aims to explore the data collection procedures and analysis of public sector accounting (PSA) practices in Bangladesh. This study adopts a qualitative methodology, employing interviews with government accountants and regulatory personnel. To investigate current public sector accounting practices and issues in Bangladesh, a significant focus is placed on government accountants due to their practical expertise and alignment with the study's purpose. Out of the 95 respondents from entities such as the CGA (50), CAG (20), Ministry of Finance (05), iBAS++ project (10), and academic community (10), a total of 22 interviews were meticulously chosen to meet the study's goals, ensuring the highest level of data quality. Data collection tools encompass semi-structured in-depth interviews featuring targeted questions, supplemented by participant observation and document analysis. In conjunction with interviews, the researcher employs direct observations to document participants' behaviors and activities and those of the studied institutions and companies. Before inputting data into computer software, the researcher conducted data analyses and coding interpretation. NVivo software was employed for data classification, clustering, and memo-writing facilitation. Supporting data from document reviews and observations are also taken into account. When the researcher listens to the recordings and notices similar responses and comments from respondents across different locations, consistently revealing similar data, the researcher focuses on refining thematic arrangements within the data categories. The data saturation occurred in different phases, encompassing information, observations, and documentation. For instance, in the case of public sector accounting practices, saturation was observed with the 13th participant concerning regulatory-related questions and procedures. Regarding problems and issues of PSA, saturation was reached with the 19th individual, and ultimately, overall saturation concerning expert experiences was attained with the 22nd respondent. Furthermore, the researcher engaged in discussions with numerous additional respondents to gain insights from these three distinct backgrounds.

Shared Leadership and Employee Satisfaction in China: A Multiple Correspondence Analysis of Gender and Education Aspects of Participative Leadership Models

The relevance of researching on modern management approaches is today´s dynamic labor market’s demand for flexible leadership models, such as shared leadership, to attract talents in companies by meeting employee´s requirements of more participation in management tasks. This paper aims for the investigation of gender and education level related influences on attitudes toward shared leadership and employee satisfaction in a Chinese cultural context for a better understanding of possible limitations of shared leadership. Shared leadership is mainly explored with a focus on its origins in North America, the study of shared leadership in the Chinese culture is underrepresented. This quantitative research is conducted through an online survey, distributed by WeChat, involving 103 Chinese employees, with 39 women and 64 men, 34 years old on average, in a manufacturing work environment to approve the industry relevance. The convenience sampling ensures a high level of trust and data quality of the respondents regarding sensitive information about employee satisfaction and attitude toward shared leadership. Only existing, validated questionnaires were used. The survey regarding employee satisfaction is based on Spector (1985). The survey regarding attitude toward shared leadership is based on Small (2007). The results are evaluated by their means, standard deviations, correlation analysis and multiple correspondence analysis. Statistical analysis is performed by XLSTAT. The first hypothesis (H1), suggesting a clustering effect by gender and education level on shared leadership’s scope, is not significantly confirmed by the correlation analysis. The multiple correspondence analysis raises suspicions about men exhibiting higher employee satisfaction and a more negative attitude toward shared leadership than women. Individuals with a bachelor’s degree tend to display ambivalent attitudes toward shared leadership. The second hypothesis (H2), proposing a positive correlation between attitude toward shared leadership and employee satisfaction, is not confirmed by the correlation analysis. No significant correlation is found among the variables for the entire sample of Chinese respondents. The third hypothesis (H3), proposing an ambivalent attitude of Chinese employees toward shared leadership, is confirmed by the calculated mean score within the survey by Small (2007). There are potential reasons for this ambivalence, e.g. suggesting a conflict between traditional and modern values in China. This paper recommends a deeper understanding of ambivalent employee satisfaction at work to enhance the effectivity and efficiency of planned leadership changes. Tailored leadership training should be designed to align with the preferences of these specific target groups. Future research directions could focus on strategies for persuading team members to embrace a shared leadership approach. Additionally, exploring the reasons behind negative attitudes toward shared leadership paves the way for further research, such as examining potential connections with hierarchical power structures.

Challenges of Health Data Use in Multidisciplinary Chronic Disease Care: Perspective from Heart Failure Care.

The healthcare sector generates approximately 30% of all the world's data volume, mostly for record keeping, compliance and regulatory requirements, and patient care. Healthcare data often exist in silos or on different systems and platforms due to decentralised storage and data protection laws, limiting accessibility for health service research. Thus, both the lack of access to data and more importantly the inability to control data quality and explore post-trial (phase IV) data or data with translational relevance have an impact on optimising care and research of congestive heart failure (CHF). We highlight that for some diseases, such as CHF, generating non-traditional data has significant importance, but is hindered by the logistics of accessing chronic disease data from separate health silos and by various levels of data quality. Modern multidisciplinary healthcare management of cardiovascular diseases-especially when spanning across community hubs to tertiary healthcare centres-increases the complexities involved between data privacy and access to data for healthcare and health service research. We call for an increased ability to leverage health data across systems, devices, and countries.

GEOSCOPE Network: 40 Yr of Global Broadband Seismic Data

Abstract The GEOSCOPE observatory (Institut de physique du globe de Paris [IPGP] and École et Observatoire des Sciences de la Terre de Strasbourg, 1982) provides more than four decades of high-quality continuous broadband data to the scientific community. Started in 1982 with only two stations, the network has grown over the years thanks to numerous international partnerships. At present, 34 stations operate in 18 countries across all continents and on islands throughout the oceans, filling important gaps in global Earth coverage. Most of the first installed stations are still running today, allowing for long-term observations, and new sites are being prospected to further improve global coverage. Over the years, GEOSCOPE has contributed to defining today’s global seismology standards (data format, data quality level, instrumentation requirements), being the French contribution to the international effort for global seismic observations. The stations are instrumented with the best quality seismometers (from the very first STS-1 in the early 80s to the last STS-6A and Trillium T360 today) and digitizers (Q330HR and Centaur) to record with high fidelity the ground motions generated by all types of seismic sources. Real-time data are sent to the tsunami warning centers and both validated and real-time data are available at the IPGP, Epos-France and Earthscope data centers. The quality of GEOSCOPE data and metadata is ensured by daily and yearly validation that enables issue detection and mitigation. GEOSCOPE, in collaboration with the other global networks, has played and continues to play a crucial role in the study of Earth’s structure and global dynamics and the characterization of all types of seismic sources.

An effective imputation scheme for handling missing values in the heterogeneous dataset

<span>A high level of data quality has always been a concern for many applications based on machine learning, including clinical decision support systems, weather forecasting, traffic predictions, and many others. A very limited amount of work is devoted to exploiting the missing values for effective imputation and better prediction. This paper introduces a unique approach to predicting and imputing missing data fields in the multivariate dataset such as numerical, categorical, and unstructured. The proposed imputation method is a multi-model scheme based on the joint approach of natural language processing (NLP) encoders, machine learning-driven feature extractors, and a sequential regression imputation technique to predict missing values. <br /> The proposed system is robust and scalable without requiring extensive engineering. The validation of the model is done on the benchmarked clinical dataset of heart disease obtained from UCI. The results show that the proposed methods achieve better imputation accuracy and require significantly less time than other missing data imputation methods.</span>

Event Log Data Quality Issues and Solutions

Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly impacts the reliability, validity, and usefulness of the derived process insights. The literature offers a taxonomy of preprocessing techniques and papers reporting on solutions for data quality issues in particular scenarios without exploring the relationship between the data quality issues and solutions. This research aims to discover how process mining researchers and practitioners solve certain data quality issues in practice and investigates the nature of the relationship between data quality issues and preprocessing techniques. Therefore, a study was undertaken among prominent process mining researchers and practitioners, gathering information regarding the perceived importance and frequency of data quality issues and solutions and the participants’ recommendations on preprocessing technique selection. The results reveal the most important and frequent data quality issues and preprocessing techniques and the gap between their perceived frequency and importance. Consequently, an overview of how researchers and practitioners solve data quality issues is presented, allowing the development of recommendations.

A Method to Classify Data Quality for Decision Making Under Uncertainty

Every decision-making process is subject to a certain degree of uncertainty. In sectors where the outcomes of the operations planned are uncertain and difficult to control such as in forestry, data describing the available resources can have a large impact on productivity. When planning activities, it is often assumed that such data are accurate, which causes a need for more replanning efforts. Data verification is kept to a minimum even though using erroneous information increases the level of uncertainty. In this context, it is relevant to develop a process to evaluate whether the data used for planning decisions are appropriate, so as to ensure the decision validity and provide information for better understanding and actions. However, the level of data quality alone can sometimes be difficult to interpret and needs to be put into perspective. This article proposes an extension to most data quality assessment techniques by comparing data to past quality levels. A classification method is proposed to evaluate the level of data quality in order to support decision making. Such classification provides insights into the level of uncertainty associated with the data. The method developed is then exploited using a theoretical case based on the literature and a practical case based on the forest sector. An example of how classified data quality can improve decisions in a transportation problem is finally shown.

Implementation of Stochastic Modelling in Enhanced Cadastral Databased for Multi-Classes Datasets

Stochastic Modelling (SM) was a crucial component of least squares adjustment (LSA), particularly when processing data from geodetic networks. The projected variances which generate using SM execute an important part in defining both the accurateness of the computed parameter vectors and the impact of the adjustment outcomes. As positional precision becomes the primary objective, there is still potential for improvement because there are multiple sources of datasets with varying levels of data quality. Concerning the assertion that the National Digital Cadastral Database (NDCDB) is accurate, its development involved the use of historical datasets that were obtained from a number of different measurement classes, specifically the first, second, and third classes. In this study, researchers evaluated whether or not it is possible to employ stochastic modelling to maintain the position correctness of historical data that encompasses a wide range of data quality classes. In order to accomplish this, an approach known as an Least Squares Variance Component Estimator (LS-VCE) was utilised to generate reliable estimates of variances. Two (2) certified plans (CPs) that is CP93887 and CP33758 was selected as measurements for the first and second classes CP, respectively. The experiment showed that the variance that has been estimated by LS-VCE could produce realistic adjustment results, as shown by an analysis of the corrected results obtained by allocating the variance into different data classes. In light of these findings, the investigations showed and demonstrated conclusively that separate variance is necessary for each data classes with the aim of preserving positional accuracy. In conclusion, it is crucial to incorporate a realistic variance component inside a coordinated cadastral database in order to fulfil the objective of ensuring the accurateness of survey data for future time periods.