Importance of Data Quality for Analytics

Jugulum, Rajesh

doi:10.1007/978-3-319-21332-3_2

Cited by 22 publications

(26 citation statements)

References 3 publications

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“…Where 0 is the attraction when there is no distance between the fireflies i.e ( = 0) and ∈ [0, ∞) is the light absorption coefficient. The distance between two fireflies i and j at positions and x j is the Cartesian distance calculated by equation (2).…”

Section: Firefly Algorithm -Adaptive Search Proceduresmentioning

confidence: 99%

“…Missing data is a general weakness that can influence the consequences of the prediction system to be ineffective [1][2][3]. Ignoring the missing data has an impact on the results of the analysis [4][5][6][7][8][9], learning outcomes, predictive results [10] and potentially weakens the validity of the results and conclusions [8,9] and leads to estimation of biased parameters [7,[11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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Class Center-Based Firefly Algorithm for Handling Missing Data

Nugroho

Utama

Surendro

2020

Preprint

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Estimating missing data in a dataset is a significant advance during the data cleaning stage. Improper data handling can make inaccurate results when conducting data analysis. Most of the research about missing data estimation is irrespective of the correlation between attributes. However, an adaptive search procedure helps find the estimates of the missing data when correlations between attributes are considered in the process. Firefly Algorithm (FA) implements an adaptive search procedure in the imputation of the missing data by finding the estimated value that is closest to the value in other data known. Therefore, this study proposes a class center-based adaptive approach model for missing data by considering the attribute correlation in the imputation process (C3-FA). Based on the experiment, the general result find that the class center-based firefly algorithm is an efficient technique for getting the actual value in handling the missing data. This can be seen on the value of Pearson correlation coefficient (r) that close to 1 and the root mean squared error (RMSE) value is generally closer to 0. In addition, the proposed method can maintain the true distribution of data values. This is indicated by the Kolmogorov–Smirnov test that value of DKS for most of the attributes in the dataset is generally closer to 0. Also, the results of the accuracy evaluation using three classifiers, showed that the proposed method produces good accuracy.

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Section: Firefly Algorithm -Adaptive Search Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Class Center-Based Firefly Algorithm for Handling Missing Data

Nugroho

Utama

Surendro

2020

Preprint

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“…The business analysis based on bad data will result in business losses. The data quality also influences the level of users satisfaction and stakeholders [4]. Maintaining high data quality level is essential for the organization, whether it is to improve the productivity of its employees or to give better services to the customers.…”

Section: Introductionmentioning

confidence: 99%

Data Quality Management Maturity Measurement of Government-Owned Property Transaction in BMKG

Wibisono

Hidayanto

Nugroho

2018

CommIT (Communication and Information Technology) Journal

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Government-Owned Property (GOP) management, including the bookkeeping of GOP transaction, is part of GOP Officer responsibility to ensure the quality of transaction data. This responsibility also applies to GOP Officer in Indonesian Agency for Meteorological, Climatological and Geophysics ‘Badan Meteorologi, Klimatologi, dan Geofisika’ (BMKG). GOP data as the source for the Central Government Financial Report is expected to be well-maintained. It must be presented as accurate as possible, although there are still inaccurate data presented in the latest BMKG GOP Report. This qualitative research using document study and some interview sessions aims to measure how well the Data Quality Management (DQM) maturity of GOP transaction in BMKG using Loshin’s Data Quality Maturity model. Thus, the result of maturity assessment is analyzed to recommend and implement DQM activities from the Data Management Body of Knowledge (DMBOK). The purpose is to improve GOP DQM. The research shows that the level of DQM maturity is at a repeatable level to defined level. Moreover, 52 maturity characteristics need to be followed through with DQM activities.

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“…Types of model analysis are statistics, predictions, or data mining models that empirically come from data using statistical methods that are generally accepted. The work to produce functional models requires several analytical strategies, namely, data quality analysis, descriptive, diagnostic, predictive, and prescriptive [23], [24].…”

Section: Modeling Analysismentioning

confidence: 99%

“…The diagnostic analysis includes understanding the impact of input factors and operational policies. In this section, two types of approaches are carried out in diagnostic, namely log activity analysis on social networks and e-learning [23].…”

Section: Modeling Analysismentioning

confidence: 99%

Student Performance Based on Activity Log on Social Network and e-Learning

Agusriandi¹,

Sitanggang²,

Wijaya³

2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Learning activities in social networks and e-learning platforms bring massive activity log in the database, making it challenging to measure students’ performance. Data mining technique and social network analysis provide some benefits in the field of education in discovering knowledge from hidden information of student’s activities on e-learning and social network environment. This study aims to identify dominant students on social network group based on centrality values and to analyze log data from the activities on e-learning using process mining technique. Centrality value was measured by analyzing data quality or data pre-processing, creating the network, measuring the network, and highlighting degrees and layouts. The process mining technique included data pre-processing, discovering process, and conformance checking. This study found that dominant students were identified from a high hub score and authority. This study also found a free-rider student. The presence of dominant students and free-riders made the collaboration of social network group are weak. This study also found that student performance on e-learning has been discovered where the student’s activity, namely, the course module viewed and course viewed, were more frequent than other activities. On the other hand, an optimum fitness value was obtained, i.e., 0.94 on all the processes of e-learning. This study provides insights that can be used to improve student collaboration and to enhance online learning activities.

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Importance of Data Quality for Analytics

Cited by 22 publications

References 3 publications

Class Center-Based Firefly Algorithm for Handling Missing Data

Class Center-Based Firefly Algorithm for Handling Missing Data

Data Quality Management Maturity Measurement of Government-Owned Property Transaction in BMKG

Student Performance Based on Activity Log on Social Network and e-Learning

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