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to come up with insightful data they can use for strategic decision-making (Baier et al., 2012). In the modern business environment, data has evolved into a crucial asset for businesses since businesses use data as a strategic asset that is used regularly to create a competitive advantage and improve customer experiences. Among the most significant forms of data is customer information which is a critical asset used to assess customer behavior and trends and use it for developing new strategies for improving customer experience (Ahmed, 2004). However, data has to be of high quality to be used as a business asset for creating a competitive advantage. Therefore, data governance is a critical element of data collection and analysis since it determines the quality of data while integrity constraints guarantee the reliability of information collected from data sources. Various technologies including Big Data are used by businesses and organizations to allow users to search for specific information from raw data by grouping it based on the preferred criteria marketing departments in organizations could apply for developing targeted marketing strategies (Ahmed, 2004). As technology evolves, new forms of data are being introduced for analysis and classification purposes in marketing organizations and businesses. The introduction of new gadgets such as
Smartphones and new-generation PCs has also introduced new data sources from which organizations can collect, analyze and classify data when developing marketing strategies. Retail businesses are the business category that uses customer data from smart devices and websites to understand how their current and targeted customers perceive their services before using the information to make improvements and increase customer satisfaction (Cerchiello and Guidici, 2012). Analyzing customer data is crucial for businesses since it allows marketing teams to understand customer behavior and trends which makes a considerable difference during the development of new marketing campaigns and strategies. Retailers who use customer data from various sources gain an advantage in the market since they can develop data-informed strategies for attracting and retaining customers in the overly competitive business environment. Based on the information on the benefits of data collection and analysis, the following hypotheses are proposed: The sources of data used as the foundation of data collection and analysis have a considerable impact on the data analysis tools used for analyzing and categorizing data.
910:: volume, variety and velocity. Factor velocity emerged in the 1980s as one of the most important procedures in data analysis tools which was widely used by organizations for market research. The tools used to select core variables from the data that was collected from various sources and analyzed it; if the amount of data used to be too huge for humans to understand via manual observation, factor analysis would be introduced to distinguish between qualitative and quantitative data (Stewart, 1981). Organizations collect data from numerous sources including websites, emails and customer devices before conducting data analysis. Collecting data from numerous sources and analyzing it using different data analysis tools has its advantages, including overcoming the risk of method bias; using data from different sources and analyzing it using multiple analysis methods guarantees businesses and organizations robust and reliable findings they can use in decision making. On the other hand, researchers use modern technologies to analyze and group data collected from respondents in the form of images, audio and video files by applying algorithms and other analysis software Berry et al., 1997). Researchers and marketers can then use the information obtained from the new generation analysis tools and methods for forecasting, decision support and making estimations for decision making. For instance, information from different data sources on demand forecasts can help a retail business determine the amount of stock required in an upcoming season depending on data from previous seasons. The analysis can allow organizations to make data-informed decisions to gain competitive advantage in an era where all businesses and organizations are capitalizing on emerging technologies and business intelligence tools to gain competitive edges. While there are numerous analysis tools in the market, Big Data analytics is the most common and advanced technology that has led to the following hypothesis: Data analytic tools used to analyze data collected from numerous data sources determine the quality and reliability of data analysis.
919:
conducted by PWC indicated that more than two-thirds of retail customers prefer purchasing products and services from businesses that have data protection and privacy plans for protecting customer information. Also, the study indicated that customers trust businesses that can prove they cannot use customer data for any other purposes other than marketing. As technology and the
Internet continue improving, the success of businesses using it as a platform for marketing their products will depend on how effectively they can gain and maintain the trust of customers and users. Therefore, businesses will have to introduce and implement effective data protection and privacy strategies to protect business data and customer privacy. Although developing trust between customers and businesses affects the customers’ purchasing intentions, it also has a considerable impact on long-term purchasing behaviors including how frequently customers purchase which could impact the profitability of a business in the long run. Thus, the above information leads to the following hypothesis: Implementing data security and privacy plans has a positive impact on economic and financial outcomes.
928:
collect and analyze data for improved decision-making. Jonsen (2013) explains that organizations using average analytics technologies are 20% more likely to gain higher returns compared to their competitors who have not introduced any analytics capabilities in their operations. Also, IRI reported that the retail industry could experience an increase of more than $ 10 billion each year resulting from the implementation of modern analytics technologies. Therefore, the following hypothesis can be proposed: Economic and financial outcomes can impact how organizations use data analytics tools.
804:
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2004). There are 2 main categories of data analysis tools, data mining tools and data profiling tools. Also, most commercial data analysis tools are used by organizations for extracting, transforming and loading ETL for data warehouses in a manner that ensures no element is left out during the process (Turban et al., 2008). Thus the data analysis tools are used for supporting the 3 Vs in
885:, which refers to the collection and analyses of massive sets of data. While big data is a recent phenomenon, the requirement for data to aid decision-making traces back to the early 1970s with the emergence of decision support systems (DSS). These systems can be considered as the initial iteration of data management for decision support.
896:
Marketers and marketing organizations have been using data collection and analysis to refine their operations for the last few decades. Marketing departments in organizations and marketing companies conduct data collection and analysis by collecting data from different data sources and analyzing them
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Studies indicate that customer transactions account for a 40% increase in the data collected annually, which means that financial data has a considerable impact on business decisions. Therefore, modern organizations are using big data analytics to identify 5 to 10 new data sources that can help them
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Organizations use various data analysis tools for discovering unknown information and insights from huge databases; this allows organizations to discover new patterns that were not known to them or extract buried information before using it to come up with new patterns and relationships (Ahmed,
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While organizations need to use quality data collection and analysis tools to guarantee the quality and reliability of the customer data they collect, they must implement security and privacy strategies to protect the data and customer information from privacy leaks (Van Till, 2013). A study
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technology, those suggesting that data management was more important than business process management used arguments such as "a customer's home address is stored in 75 (or some other large number) places in our computer systems." However, during this period, random access processing was not
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Kumar, Sangeeth; Ramesh, Maneesha
Vinodini (2010). "Lightweight Management framework (LMF) for a Heterogeneous Wireless Network for Landslide Detection". In Meghanathan, Natarajan; Boumerdassi, Selma; Chaki, Nabendu; Nagamalai, Dhinaharan (eds.).
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4.4 Data
Management Center (DMC) The Data Management Center is the data center for all of the deployed cluster networks. Through the DMC, the LMF allows the user to list the services in any cluster member belonging to any cluster
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usage, it became obvious that both management processes were important. If the data was not well defined, the data would be mis-used in applications. If the process wasn't well defined, it was impossible to meet user needs.
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processing and renders interpretation implicit. The distinction between data and derived value is illustrated by the information ladder. However, data has staged a comeback with the popularisation of the term
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Recent Trends in
Networks and Communications: International Conferences, NeCoM 2010, WiMoN 2010, WeST 2010,Chennai, India, July 23-25, 2010. Proceedings
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Mastering Cloud-Native
Microservices Designing and implementing Cloud-Native Microservices for Next-Gen Apps
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competitively fast, so those suggesting "process management" was more important than "data management" used
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The concept of data management arose in the 1980s as technology moved from sequential processing (first
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as a valuable resource, it is the practice of managing an organization's data so it can be analyzed for
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Watson, Hugh J.; Marjanovic, Olivera (2013). "Big Data: The Fourth Data
Management Generation".
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Since it was now possible to store a discrete fact and quickly access it using random access
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1205:. Communications in Computer and Information Science. Vol. 90. Springer. p. 466.
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Several organisations have established data management centers (DMC) for their operations.
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1101:"What Is Data Management? Importance & Challenges | Tableau"
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in a non-technical context. Thus data management has become
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Data warehousing and business intelligence and
Analytics
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1237:"Data Mesh: Delivering data-driven value at scale"
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27:Disciplines related to managing data as a resource
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235:Followings are common data management patterns:
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222:As application software evolved into real-time,
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116:. Unsourced material may be challenged and
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180:Learn how and when to remove this message
243:command query responsibility segregation
426:Data integration and inter-operability
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1180:Business Intelligence Journal; Seattle
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1001:, a domain-oriented data architecture
114:adding citations to reliable sources
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276:Topics in data management include:
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345:Database and storage management
219:time as their primary argument.
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1133:. Prentice Hall International.
1030:Hierarchical storage management
923:Financial and economic outcomes
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581:Library and information science
367:Hierarchical storage management
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914:Data security and data privacy
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1025:Enterprise content management
856:is increasingly replaced by
362:Business continuity planning
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989:Data Management Association
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876:. This trend obscures the
449:Document management system
406:Reference and master data
357:Database management system
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1040:Machine-readable document
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954:Information architecture
708:Interdisciplinary fields
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18:Research data management
959:Enterprise architecture
352:Database administration
1035:Information repository
870:information management
652:Information management
640:Collections management
554:Data quality assurance
445:Documents and content
415:Master data management
267:Static content hosting
1005:Computer data storage
974:Controlled vocabulary
728:Documentation science
716:Communication studies
469:Business intelligence
440:Data interoperability
1478:Protection (privacy)
1262:at Wikimedia Commons
1015:Digital preservation
994:Data management plan
874:knowledge management
660:Knowledge management
110:improve this section
66:related to handling
1060:Identity management
1020:Document management
901:Data analysis tools
740:Information science
636:Archives management
575:Part of a series on
517:Metadata publishing
502:Metadata management
1050:System integration
1045:Performance report
1010:Data proliferation
969:Information system
964:Information design
664:Library management
512:Metadata discovery
459:Records management
454:Content management
52:The data lifecycle
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1558:Wrangling/munging
1408:Format management
1258:Media related to
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325:Data architecture
256:Materialized view
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16:(Redirected from
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1468:Preservation
1458:Philanthropy
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1322:Augmentation
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1216:. Retrieved
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1108:. Retrieved
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1080:CRM software
1075:ERP software
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892:Data sources
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857:
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732:Epistemology
724:Data science
647:
644:Preservation
529:Data quality
395:Data privacy
390:Data erasure
313:Data subject
308:Data steward
297:Data trustee
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108:Please help
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1528:Stewardship
1418:Integration
1367:Degradation
1352:Compression
1332:Archaeology
1317:Acquisition
859:information
852:, the term
797:WikiProject
686:Information
656:cataloguing
622:Information
478:data mining
385:Data access
318:Data ethics
304:or guardian
252:Index table
239:Cache-aside
224:interactive
64:disciplines
1548:Validation
1483:Publishing
1473:Processing
1443:Management
1357:Corruption
1347:Collection
1218:2016-06-16
1110:2023-12-04
1087:References
1070:Data theft
848:In modern
373:subsetting
287:Data asset
170:April 2020
140:newspapers
1553:Warehouse
1518:Scrubbing
1498:Retention
1493:Reduction
1448:Migration
1423:Integrity
1391:Transform
1342:Cleansing
1186:(3): 4–8.
1127:(2004) .
999:Data mesh
944:FAIR data
939:Open data
865:knowledge
698:Knowledge
694:Artefacts
690:Documents
618:Libraries
614:Histories
498:Metadata
487:data mart
330:Dataflows
264:Valet key
97:does not
1573:Category
1523:Security
1513:Scraping
1488:Recovery
1362:Curation
1327:Analysis
1157:. 2023.
932:See also
908:Big Data
883:big data
878:raw data
862:or even
809:Category
762:Archival
758:Academic
682:Metadata
674:Curation
604:Glossary
507:Metadata
260:Sharding
231:Patterns
1533:Storage
1508:Science
1503:Quality
1433:Lineage
1428:Library
1403:Farming
1386:Extract
1372:Editing
786:Special
774:Private
599:Outline
205:storage
196:, then
154:scholar
118:removed
103:sources
78:Concept
1453:Mining
1413:Fusion
1270:Curlie
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782:School
778:Public
770:Health
341:design
272:Topics
245:(CQRS)
156:
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766:Legal
754:Areas
632:Focus
568:Usage
371:Data
200:) to
161:JSTOR
147:books
1543:Type
1438:Loss
1396:Load
1306:Data
1207:ISBN
1159:ISBN
1135:ISBN
854:data
678:Data
658:) -
646:) -
485:and
476:and
339:and
212:disk
133:news
101:any
99:cite
68:data
1381:ELT
1377:ETL
1337:Big
1268:at
872:or
435:ELT
431:ETL
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