Management of Data Value Chains, a Value Monitoring Capability Maturity Model

Proceedings of the 22nd International Conference on Enterprise Information Systems

2020

Self Cite

Data is becoming one of the world's most valuable resources and it is suggested that those who own the data will own the future. However, despite data being an important asset, data owners struggle to assess its value. Some recent pioneer works have led to an increased awareness of the necessity for measuring data value. They have also put forward some simple but engaging survey-based methods to help with the first-level data assessment in an organisation. However, these methods are manual and they depend on the costly input of domain experts. In this paper, we propose to extend the manual survey-based approaches with additional metrics and dimensions derived from the evolving literature on data value dimensions and tailored specifically for our use case study. We also developed an automatic, metric-based data value assessment approach that (i) automatically quantifies the business value of data in Relational Databases (RDB), and (ii) provides a scoring method that facilitates the ranking and extraction of the most valuable RDB tables. We evaluate our proposed approach on a real-world RDB database from a small online retailer (MyVolts) and show in our experimental study that the data value assessments made by our automated system match those expressed by the domain expert approach.

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Towards an Automatic Data Value Analysis Method for Relational Databases

Bendechache

Limaye

Proceedings of the 22nd International Conference on Enterprise Information Systems

2020

Self Cite

“…To build adaptive, value-driven systems, it is necessary to have concrete value assessment techniques that report over time. Without assessment, the effective management of value, and hence efficient exploitation of data is highly unlikely [6]. The measurement of data value in an unbiased manner allow for better data characterisation and classification, which then enable data exploitation optimisation [8].…”

Section: Measuring Data Valuementioning

confidence: 99%

Challenges in Value-Driven Data Governance

Attard

Lecture Notes in Computer Science

2018

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Data is quite popularly considered to be the new oil since it has become a valuable commodity. This has resulted in many entities and businesses that hoard data with the aim of exploiting it. Yet, the 'simple' exploitation of data results in entities who are not obtaining the highest benefits from the data, which as yet is not considered to be a fully-fledged enterprise asset. Such data can exist in a duplicated, fragmented, and isolated form, and the sheer volume of available data further complicates the situation. Issues such as the latter highlight the need for value-based data governance, where the management of data assets is based on the quantification of the data value. This quantification will provide an opportunity for evidence-based approaches to data governance. This paper has the purpose of creating awareness and further understanding of challenges that result in untapped data value. We identify niches in related work, and through our experience with businesses who use data assets, we here analyse four main context-independent challenges that hinder entities from achieving the full benefits of using their data. This will aid in the advancement of the field of value-driven data governance and therefore directly affect data asset exploitation.

“…Value-driven Data Governance Platform for further processing. With regard to value monitoring service, Brennan et al [31] describe a capability maturity model for data value monitoring. This will be prototyped as a new data value monitoring service based on dataset and ensemblespecific metrics and the results, defined using DaVe, the Data Value ontology [25], will also be consumed by the governance platform.…”

Section: ) Knowledge Extraction Interlinking and Cataloguingmentioning

confidence: 99%

Semantic Data Ingestion for Intelligent, Value-Driven Big Data Analytics

Debattista

Attard

2018 4th International Conference on Big Data Innovations and Applications (Innovate-Data)

2018

Self Cite

In this position paper we describe a conceptual model for intelligent Big Data analytics based on both semantic and machine learning AI techniques (called AI ensembles). These processes are linked to business outcomes by explicitly modelling data value and using semantic technologies as the underlying mode for communication between the diverse processes and organisations creating AI ensembles. Furthermore, we show how data governance can direct and enhance these ensembles by providing recommendations and insights that to ensure the output generated produces the highest possible value for the organisation.