scholarly journals Without Data Quality, There Is No Data Migration

2021 ◽  
Vol 5 (2) ◽  
pp. 24
Author(s):  
Otmane Azeroual ◽  
Meena Jha
Keyword(s):  
Data Quality ◽  
Data Storage ◽  
Business Processes ◽  
Data Migration ◽  
Quality Data ◽  
Migration Strategy ◽  
Data Intensive ◽  
Migration Strategies ◽  
The One ◽  
The Cost

Data migration is required to run data-intensive applications. Legacy data storage systems are not capable of accommodating the changing nature of data. In many companies, data migration projects fail because their importance and complexity are not taken seriously enough. Data migration strategies include storage migration, database migration, application migration, and business process migration. Regardless of which migration strategy a company chooses, there should always be a stronger focus on data cleansing. On the one hand, complete, correct, and clean data not only reduce the cost, complexity, and risk of the changeover, it also means a good basis for quick and strategic company decisions and is therefore an essential basis for today’s dynamic business processes. Data quality is an important issue for companies looking for data migration these days and should not be overlooked. In order to determine the relationship between data quality and data migration, an empirical study with 25 large German and Swiss companies was carried out to find out the importance of data quality in companies for data migration. In this paper, we present our findings regarding how data quality plays an important role in a data migration plans and must not be ignored. Without acceptable data quality, data migration is impossible.

scholarly journals Exceptional Data Quality using Intelligent Matching and Retrieval

AI Magazine ◽  
2010 ◽  
Vol 31 (1) ◽  
pp. 65 ◽  
Author(s):  
Clint R. Bidlack ◽  
Michael P Wellman
Keyword(s):  
Data Quality ◽  
Real Time ◽  
Data Migration ◽  
Quality Data ◽  
Sales Managers ◽  
Web Based ◽  
Sales And Marketing ◽  
Retrieval Algorithms ◽  
Marketing Operations ◽  
User Friendly

Recent advances in enterprise web-based software have created a need for sophisticated yet user-friendly data quality solutions. A new category of data quality solutions are discussed that fill this need using intelligent matching and retrieval algorithms. Solutions are focused on customer and sales data and include real-time inexact search, batch processing, and data migration. Users are empowered to maintain higher quality data resulting in more efficient sales and marketing operations. Sales managers spend more time with customers and less time managing data.

scholarly journals Optimizing Workflow Data Footprint

2007 ◽  
Vol 15 (4) ◽  
pp. 249-268 ◽  
Author(s):  
Gurmeet Singh ◽  
Karan Vahi ◽  
Arun Ramakrishnan ◽  
Gaurang Mehta ◽  
Ewa Deelman ◽  
...  
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Open Science ◽  
Scale Production ◽  
Distributed Resources ◽  
Data Intensive ◽  
Large Scale Production ◽  
Data Files ◽  
The Cost ◽  
Open Science Grid

In this paper we examine the issue of optimizing disk usage and scheduling large-scale scientific workflows onto distributed resources where the workflows are data-intensive, requiring large amounts of data storage, and the resources have limited storage resources. Our approach is two-fold: we minimize the amount of space a workflow requires during execution by removing data files at runtime when they are no longer needed and we demonstrate that workflows may have to be restructured to reduce the overall data footprint of the workflow. We show the results of our data management and workflow restructuring solutions using a Laser Interferometer Gravitational-Wave Observatory (LIGO) application and an astronomy application, Montage, running on a large-scale production grid-the Open Science Grid. We show that although reducing the data footprint of Montage by 48% can be achieved with dynamic data cleanup techniques, LIGO Scientific Collaboration workflows require additional restructuring to achieve a 56% reduction in data space usage. We also examine the cost of the workflow restructuring in terms of the application's runtime.

scholarly journals Lifetime value in business process

2011 ◽  
Vol 59 (2) ◽  
pp. 291-298
Author(s):  
Martin Souček ◽  
Jana Turčínková
Keyword(s):  
Customer Relationship Management ◽  
Business Processes ◽  
Relationship Management ◽  
Customer Relationship ◽  
Value Assessment ◽  
Customer Lifetime ◽  
Lifetime Value ◽  
Qualitative Nature ◽  
The One ◽  
The Cost

The paper focuses on lifetime value assessment and its implementation and application in business processes. The lifetime value is closely connected to customer relationship management. The paper presents results of three consecutive researches devoted to issues of customer relationship management. The first two from 2008 and 2010 were conducted as quantitative ones; the one from 2009 had qualitative nature. The respondents were representatives of particular companies. The means for data collection was provided by ReLa system. We will focus on individual attributes of lifetime value of a customer, and relate them to approaches of authors mentioned in introduction. Based on the qualitative research data, the paper focuses on individual customer lifetime value parameters. These parameters include: the cost to the customer relationship acquisition and maintenance, profit generated from a particular customer, customer awareness value, the level of preparedness to adopt new products, the value of references and customer loyalty level. For each of these parameters, the paper provides specific recommendations. Moreover, it is possible to learn about the nature of these parameter assessments in the Czech environment.

Gaming for Respondents: A Test of the Impact of Gamification on Completion Rates

2017 ◽  
Vol 59 (1) ◽  
pp. 117-138 ◽  
Author(s):  
Susanna Warnock ◽  
J. Sumner Gantz
Keyword(s):  
Marketing Research ◽  
Customer Segmentation ◽  
Decision Makers ◽  
Customer Relationship ◽  
Quality Data ◽  
Business Analytics ◽  
Quantitative Studies ◽  
The One ◽  
The Cost ◽  
The Impact

This paper examines the challenge for marketing research companies in overcoming consumers' reluctance to participate in surveys in order to provide decision makers with quality data. On the one hand, clients are increasingly demanding more data; these clients are interested in employing and refining many of the trends in business analytics - Big Data, data-driven customer relationship management, more sophisticated customer segmentation or simply monitoring customer satisfaction. In order to do this, the clients are demanding more data, more often. On the other hand, marketing research companies are finding it increasingly difficult to get respondents to participate in quantitative studies. The cost of reaching respondents, getting them to begin a survey and, more importantly, complete the survey with thoughtful and honest answers, is decreasing the profit margin for many marketing research companies. How can marketing research companies deal with this difficult dilemma?

scholarly journals Challenges in data analyses and data storage for metabarcoding

2021 ◽  
Vol 4 ◽  
Author(s):  
Diego Fontaneto ◽  
Alain Franc
Keyword(s):  
Data Storage ◽  
Working Group ◽  
Clustering Algorithms ◽  
The Other ◽  
Cost Action ◽  
Use Of Data ◽  
Data Analyses ◽  
Current Scenario ◽  
The One ◽  
The Cost

The working group of the DNAquanet COST Action dealing with data analyses and data storage for the use of metabarcoding approaches in biodiversity assessment, namely Working Group 4, had two main goals. On the one hand the comparison of the available analytical pipelines, while keeping track of new advances in bioinformatics, and on the other hand to deal with potential issues in data storage and sharing in the era of big data. Such activities were carried out through discussions at meetings of the COST Action, organisations of workshops, online surveys, and meta-analyses.The main achievements of the first line of activity, comparing pipelines, will be summarised in the first of the talks of the session, dealing with differences in clustering algorithms to obtain clusters of sequences that are then used for subsequent analyses and inference. The other talks in the session will introduce different pipelines and approaches that are currently developed to improve the way biological monitoring can be performed.The main achievement of the second line of activity, on data storage and sharing, will be summarised in the first flash talk of the session, dealing with the current scenario and potential pitfalls related to sharing the raw data from massive sequencing.The other flash talks in the session will provide examples on the applications of different approaches to analyse biodiversity using DNA sequence data.We are confident that the pluralism in approaches and applications that will be presented in the session will provide supporting discussions and interactions for a convergence towards the optimisation of the pipelines and the best use of data from metabarcoding.

scholarly journals From Prediction of the Improvement of the Quality towards an Equitable Sharing of the Cost of the Improvement between Business Processes

2021 ◽  
Vol 9 (5) ◽  
pp. 268-274
Author(s):  
Jaouad Maqboul ◽  
Bouchaib Bounabat
Keyword(s):  
Deep Learning ◽  
Data Quality ◽  
Quality Assessment ◽  
Business Process ◽  
Shapley Value ◽  
Business Processes ◽  
Cost Benefit ◽  
Impact Factors ◽  
The Cost ◽  
The Impact

In this work we have developed a quality approach for the quality assessment of data related to the business process for quality projects, this approach uses the cost of the implementation of quality combined with the impact of quality broken down into the benefit and efficiency of data, shapley value helps us choose the business processes that will collaborate to reduce the cost of improvement, Deep learning helps us calculate the quality values for any dimension based on history of previous improvements. To reach our goal, we used the cost-benefit approach (ACB) and the cost-effective approach (ACE) to extract the impact and cost factors then using a multi-optimization algorithm. -objective we will minimize the cost and maximize the impact for each business process and the deep learning introduced will complement our approach to learn from the previous improvements after validation of the processes which will be chosen as well as the values calculated after improvement. The importance of this research lies in the use of impact factors and the cost of the quality evaluation which represent the basis of any improvement, our approach uses generic multi-objective optimization algorithms which will help choose the minimum value of each business process before the improvement, adding a layer of predicting and estimating the quality value of the data generated by the business process before the improvement even, while the value of shapley has aim to minimize the cost of quality projects during fission and merger of companies and even within a company composed of several services and departments to have the lowest possible total cost to help companies manage the portfolios of quality.. Keywords: Artificial neural network, data quality assessment, data quality improvement, deep learning, prediction of improvement in data completeness shapley value.

scholarly journals Analysis on Data Migration Strategies in Heterogeneous Databases

2021 ◽  
pp. 210-214
Author(s):  
Ms. Latha S S ◽  
Pavan Kumar S
Keyword(s):  
Data Quality ◽  
Data Migration ◽  
Heterogeneous Databases ◽  
Target System ◽  
Application Systems ◽  
Different Types ◽  
Migration Strategies ◽  
Data Requirements

Data required for a new application are frequently come from other existing application systems. If data required for the new application are available from existing systems and the volume of data is large, the necessary data should be migrated from the existing systems (source systems) to the new application (target system) instead of recreating those data for the target system. The Transformation of data is generally a necessary step in data migration because the data requirements and the architecture of the target system are different from that of the source systems. This paper surveys the data migration techniques which focus on improving the data quality between different types of databases.

Designing Information Product (IP) Maps On the Process of Data Processing and Academic Information

2017 ◽  
Vol 4 (1) ◽  
pp. 25-31 ◽  
Author(s):  
Diana Effendi
Keyword(s):  
Data Quality ◽  
Data Management ◽  
Information Management ◽  
Information Quality ◽  
Quality Data ◽  
Management Approach ◽  
Quality Of Data ◽  
Information Product ◽  
Academic Activities

Information Product Approach (IP Approach) is an information management approach. It can be used to manage product information and data quality analysis. IP-Map can be used by organizations to facilitate the management of knowledge in collecting, storing, maintaining, and using the data in an organized. The  process of data management of academic activities in X University has not yet used the IP approach. X University has not given attention to the management of information quality of its. During this time X University just concern to system applications used to support the automation of data management in the process of academic activities. IP-Map that made in this paper can be used as a basis for analyzing the quality of data and information. By the IP-MAP, X University is expected to know which parts of the process that need improvement in the quality of data and information management.   Index term: IP Approach, IP-Map, information quality, data quality. REFERENCES[1] H. Zhu, S. Madnick, Y. Lee, and R. Wang, “Data and Information Quality Research: Its Evolution and Future,” Working Paper, MIT, USA, 2012.[2] Lee, Yang W; at al, Journey To Data Quality, MIT Press: Cambridge, 2006.[3] L. Al-Hakim, Information Quality Management: Theory and Applications. Idea Group Inc (IGI), 2007.[4] “Access : A semiotic information quality framework: development and comparative analysis : Journal ofInformation Technology.” [Online]. Available: http://www.palgravejournals.com/jit/journal/v20/n2/full/2000038a.html. [Accessed: 18-Sep-2015].[5] Effendi, Diana, Pengukuran Dan Perbaikan Kualitas Data Dan Informasi Di Perguruan Tinggi MenggunakanCALDEA Dan EVAMECAL (Studi Kasus X University), Proceeding Seminar Nasional RESASTEK, 2012, pp.TIG.1-TI-G.6.

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