2016
DOI: 10.1016/j.ejor.2016.01.052
|View full text |Cite
|
Sign up to set email alerts
|

Prioritising data items for business analytics: Framework and application to human resources

Abstract: The popularity of business intelligence (BI) systems to support business analytics has tremendously increased in the last decade. The determination of data items that should be stored in the BI system is vital to ensure the success of an organisation's business analytic strategy. Expanding conventional BI systems often leads to high costs of internally generating, cleansing and maintaining new data items whilst the additional data storage costs are in many cases of minor concernwhat is a conceptual difference … Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

3
65
0
7

Year Published

2017
2017
2024
2024

Publication Types

Select...
5
3
1

Relationship

0
9

Authors

Journals

citations
Cited by 84 publications
(75 citation statements)
references
References 94 publications
3
65
0
7
Order By: Relevance
“…Indeed, compared to other functions like finance and marketing, HR is lacking proper analytical tools and metrics for the decision-making process and is thus left behind in producing strategic value (Lawler et al, 2004). A data-driven approach to HRM is perceived as a possible way to address this problem, enabling decisions based on evidence instead of intuition or personal experience (Lawler et al, 2004;Rasmussen -Ulrich, 2015;Pape, 2016). However, when examining the literature more closely it has to be noted that concepts surrounding the notion of evidence-based decision-making in HRM are still not fully established.…”
Section: Background Of Hr Analyticsmentioning
confidence: 99%
See 1 more Smart Citation
“…Indeed, compared to other functions like finance and marketing, HR is lacking proper analytical tools and metrics for the decision-making process and is thus left behind in producing strategic value (Lawler et al, 2004). A data-driven approach to HRM is perceived as a possible way to address this problem, enabling decisions based on evidence instead of intuition or personal experience (Lawler et al, 2004;Rasmussen -Ulrich, 2015;Pape, 2016). However, when examining the literature more closely it has to be noted that concepts surrounding the notion of evidence-based decision-making in HRM are still not fully established.…”
Section: Background Of Hr Analyticsmentioning
confidence: 99%
“…In contrast to the statement by Bersin, data availability is an issue. On the one hand researchers report that data are not fully collected or inaccurate (Bassi, 2011;Angrave et al, 2016;Pape, 2016) On the other hand, the required data is not fully accessible as it is not integrated across functions, divisions or geographies (Douthitt -Mondore, 2014). As a result, generated reports and conducted anal- yses are very basic and only reflect insufficient efficiency-based metrics (Falletta, 2014).…”
Section: Data Infrastructurementioning
confidence: 99%
“…to acknowledge segments, outliers and anomalies from the processes and operations. Data items may be prioritized before taking into account in the descriptive analytics process (Pape, 2016).…”
Section: An Analytics Framework For Improving Public Service Operatiomentioning
confidence: 99%
“…Recently, several frameworks have been employed and successfully applied to solve decision problems in many areas, including international politics and laws [19], transportation [20][21][22][23], business intelligence [24], information and communication technologies [25], water resources management [26], environmental risk analysis [27], flood risk management [28], environmental impact assessment and environmental sciences [14,29], solid waste management [30], climate change [31], remote sensing [32], energy [33], health technology assessment [34] and nanotechnology research [35]. Furthermore, MCDM techniques have been integrated with known systems such as genetic algorithms, geographic information systems, fuzzy logic and intelligent systems, automatic control systems and neural networks which recently are being applied.…”
Section: Introductionmentioning
confidence: 99%