Read my annotated bibliography paper "Title: Data as a Strategic Resource: Annotated Bibliography on Data Resource Management (DRM)
Abstract
This annotated bibliography features various sources that examine the topic of data as a strategic resource, specifically on how the data driven insights can be integrated within the organization in order to accomplish the broader organizational goals. Through a critical analysis of the latest literature, the chosen papers offer a theoretical understanding of the approaches and implications of data assetization strategy, big data adoption by SMEs, the shift of the concept of data assets, and the relevance of organizational mindfulness in digitalization. Thus, this research enriches the methodological discourse regarding data’s evolving role as a strategic resource, which supports decision-making and optimizes market responsiveness.
Introduction
Today in the world of digitized economy, data is accepted as an administrative by-product as well as a strategic asset. Various organizations always base their competitive business strategies on data analysis and results in order to improve market competitiveness and respond to ever-changing business environments. This annotated bibliography outlines how DRM practices have changed so that data becomes an essential strategic asset that is aligned with business goals. The subsequent annotations include critical reviews of four essential journal articles, which address the recent DRM activities and consequences for organizations.
Annotated Bibliography
Article 1
Birch, K., Cochrane, D., & Ward, C. (2021). Data as asset? The measurement, governance, and valuation of digital personal data by Big Tech. Big Data & Society, 8(1), 205395172110173. https://doi.org/10.1177/20539517211017308
Purpose of the Study: This article examines how Big Tech companies, such as Google and Facebook, transform personal data into valuable assets through governance, measurement, and valuation mechanisms. 
Main Variables: Data assetisation, user metrics and governance in the context of the tech industry are key variables in the study.
Summary of Findings: The authors describe the concept of “techcraft” and talk about the fact that Big Tech companies monetize user attention rather than personal data. They find that companies generate value by focusing on user metrics (e.g., daily average users) and building ecosystems that drive engagement The study also emphasizes the power dynamics associated with data governance and points out how Big Tech has a monopoly over the control of user data even though users own the data.
Article 2
Wang, S., & Wang, H. (2020). Big data for small and medium-sized enterprises (SME): a knowledge management model. Journal of Knowledge Management, 24(4), 881–897. https://doi.org/10.1108/jkm-02-2020-0081
Purpose of the Study: Knowledge Management (KM) in SMEs is another area that Wang and Wang (2020) seek to address in the light of big data integration. Accordingly, the researchers seek to design a KM model that can help SMEs leverage big data even with limited resources.
 Main Variables: There are four key variables associated with the study. This includes Strategic data management, IKM project planning & implementation based on knowledge, and support for small & medium-sized enterprises through IT solutions and Knowledge Services. 
Summary of Findings: The authors introduced a cyclical KM model in which data strategy fits the establishment’s goals, knowledge-based planning drives IT solutions, and the process generates more knowledge products for decision-making. While the model provides a strategic framework for SMEs, it is based on limited case studies, which may affect its generalizability. Nevertheless, the study offers valuable insights into how SMEs can approach big data without sophisticated IT infrastructure.
Article 3
Xu, T., Shi, H., Shi, Y., & You, J. (2023). From data to data asset: conceptual evolution and strategic imperatives in the digital economy era. Asia Pacific Journal of Innovation and Entrepreneurship, 18(1), 2–20. https://doi.org/10.1108/apjie-10-2023-0195
Purpose of the Study: This paper investigates the conceptual evolution of data as an asset and offers a framework for data assetisation, focusing on the role of data in driving competitive advantage in the digital economy. 
Main Variables: Key variables include data assetisation (the process of converting data into valuable assets), data governance, and data application within organizations. 
Summary of Findings: According to Xu et al. (2023), data assetisation refers to the process of transforming raw data into structured assets ready to be used to create value and innovate. The authors have identified three phases of the data management life cycle: resource readiness, capacity building, and data application. The study also shows the need for governance and investment in data infrastructure to ‘double down’ on data investments.
Article 4
Li, H., Wu, Y., Cao, D., & Wang, Y. (2021). Organizational mindfulness towards digital transformation as a prerequisite of information processing capability to achieve market agility. Journal of Business Research, 122, 700–712. https://doi.org/10.1016/j.jbusres.2019.10.036
Purpose of the Study: The purpose of the study by Li et al. (2021) is to delve into the role of organizational mindfulness to explain how it can be used to drive digital transformation and the resulting changes in IPC (information processing capacity) and market agility.
Main Variables: Organizational mindfulness, digital transformation, IPC, and market agility are the key variables of the study.
 Summary of Findings: In this paper, Li et al. (2021) provide empirical evidence that organizations that are more mindful of the information technology changes that are happening are more poised to develop the information processing capabilities that will help them achieve better market agility. The research discussed how it is necessary to adopt digital technology as part of the flows that make up a firm’s operations in a bid to effectively counteract changes that might occur in the market. Based on the research, it becomes evident that organizations have to adopt relevant technology and keep on evaluating its evolution based on the business environment.
Discussion
The four articles discussed in this annotated bibliography focused on the transformation of data from an operational artifact into a strategic resource that forms the key to organizational performance. Each of them focuses on a specific area of data management in relation to its strategic goals. Common themes that emerged from all of them include data governance, flexibility, and integration of the data efforts with organizational objectives. The following discussion brings together these studies with special emphasis placed on the ways in which data is viewed as a strategic asset in small and large organizations, and the call for appropriate data governance, the requirement for flexibility, and the integration of data management initiatives with business requirements.
1. Data as a Strategic Asset: Small vs. Large Organizations
Wang and Wang (2020) review the prospect of SMEs leveraging big data solutions despite the challenges that most of them face due to their small sizes. Their KM model shows how SMEs can indeed find strategic approaches to data by ensuring that all their data initiatives align with their goals even though they do not have expanded IT support structures which are commonly seen in large organizations rather they adapt and make the best out of what they have in the technology aspect. The KM model adopted at the SME level concentrates on a ‘think big, start small’ approach because this way it is easier to start coming up with simple yet data-driven solutions that will improve the decision-making and competitiveness of the SMEs.
On the other hand, Birch et al. (2021) concentrate on how giant companies such as Google and Facebook use data at scale.  As detailed by these firms’ “tech craft,” they build value through meticulous control of user engagement metrics while suggesting that they do not own the underlying data, they manage the data that they store. This article emphasizes the issues of control over data, and how the major companies monopolize the markets primarily through data monetization rather than the ownership of the user data.
2. Data Governance and Assetization
The approaches of Xu et al. (2024) and Birch et al. (2021) are different but both have a common theme ie “Data Governance”. Xu et al. (2024) have identified the process of data assetization with the data passing through the stages of resource readiness, capacity building as well and data application stages, and here the requirement for the proper governance of the data assetization process is greatly emphasized. The authors emphasize the investments into the development of infrastructure and human capital and subsequently the successful conversion of the raw data into valuable.
Birch et al. (2021) explore critically and analyse how the Big Tech firms ensure the use of governance practices to control the metrics related to users instead of analyzing the data. Their idea of tech craft demonstrates how such companies create value by controlling access to users’ data while they win and hold markets by leveraging data-driven engagement approaches. This is in contrast with the work done by Xu et al. (2024) where the emphasis was on the structural frameworks for converting data into usable assets. Both articles point to governance, yet Xu et al. (2021) focus on structured assetization processes, while Birch et al. (2021) highlight control and dominance over users’ data.
3. Organizational Agility and Data Integration
According to Wang and Wang (2020) and Li et al. (2021) the organizations’ flexibility is underlined when it comes to data management. Wang and Wang (2020) claim that knowledge management is central to overcoming issues associated with big data issues and KM works as a strategic management factor for SMEs. In resource-constrained environments, decisions are improved by integrating knowledge with data-driven strategies in their model. In a similar vein, Medeiros & Maçada (2021) show how managerial mindfulness with regard to digital-social activities may increase the IPC to create a greater capacity for immediate reaction to market fluctuations.
According to Li et al. (2021), the management must be attentive when considering digital transformation to create agility in organizations. They argue that if digital technology is adopted and implemented in the business processes, then it will be possible to have an increase in the IPC such that firms will gain a better ability to handle information, especially in the uncertain market. This is in line with Wang & Wang’s model defining flexibility in dealing with data but intended for SMEs who may not afford many resources.
4. Strategic Alignment of Data Initiatives with Business Goals
All four selected articles have a common theme Data Management: aligning with organizational goals. Again, based on the study conducted by Wang and Wang (2020), data initiatives should be integrated with business strategy for enhancing the decision-making and growth of the SMEs. In the same context, Xu, et al (2024) posited that data governance should be pulled into line with business objectives to optimize the benefits derived from data assetization. In the same vein, both articles reveal that data management has to be more than an initiative, it has to be an organizational priority.
Li et al., (2021) show that these digital initiatives need to be in sync with organizational strategy so as to operate competitively in the market. Firms that incorporate information technologies into organizational practices have the ability to adapt to change and sustain market relevance. However, Birch et al. (2021) are more skeptical, explaining how Big Tech firms’ data capabilities are positioned not only to accomplish internal goals but to dominate external market dynamics.
Recommendations
Based on the extensive research and selected articles listed major strategies should be applied to manage data properly as the key strategic resource of an organization. Firstly there is a need for investment in implementing a comprehensive data governance that will facilitate correct security, ethical use, and most importantly legal compliance of such data. According to Mahanti (2021), this governance framework should also contain rules for data quality, access, and general data protection policies and procedures that will help to minimize the risk and increase accountability.
Secondly, organize data strategies to address core business goals so that the data initiatives are in a positive trajectory. According to Mikalef et al. (2019), data strategy is better integrated into an overall business plan to achieve innovation, higher-quality decisions, and competitiveness. This way, any data project resources are directed to achieving measurable business value.
Thirdly foster an organizational culture that would enable employees to embrace lifelong learning and become adaptive to changes. All the employees should have knowledge of the developments in the field of technology and policies regarding data usage. Shan et al. (2018) pointed out that cultivating an organizational mindset with mindfulness can assist organizations to be alert and sensitive to changed environments and prepared to seize any opportunity as and when it happens.
At last, organizations can focus on implementing a structured framework for data management as supported by Abraham et al. (2019). The authors suggest that such a framework will guide the entire data lifecycle on how to strictly follow and navigate data from the phase of their collection and storage, as well as their utilization and monetization. As Abraham et al. (2019) suggested, this ensures that the data is managed as an important resource that other organizations can leverage strategically throughout the firm.
 
References
Abraham, R., Schneider, J., & vom Brocke, J. (2019). Data governance: a Conceptual framework, Structured review, and Research Agenda. International Journal of Information Management, 49(2), 424–438.
Birch, K., Cochrane, D., & Ward, C. (2021). Data as asset? The measurement, governance, and valuation of digital personal data by Big Tech. Big Data & Society, 8(1), 205395172110173. https://doi.org/10.1177/20539517211017308
Li, H., Wu, Y., Cao, D., & Wang, Y. (2021). Organizational mindfulness towards digital transformation as a prerequisite of information processing capability to achieve market agility. Journal of Business Research, 122, 700–712. https://doi.org/10.1016/j.jbusres.2019.10.036
Mahanti, R. (2021). Data Governance and Data Management. Springer Singapore. https://doi.org/10.1007/978-981-16-3583-0
Medeiros, M. M. de, & Maçada, A. C. G. (2021). Competitive advantage of data-driven analytical capabilities: the role of big data visualization and of organizational agility. Management Decision, 60(4). https://doi.org/10.1108/md-12-2020-1681
Mikalef, P., Boura, M., Lekakos, G., & Krogstie, J. (2019). Big data analytics and firm performance: Findings from a mixed-method approach. Journal of Business Research, 98(2), 261–276. Sciencedirect. https://www.sciencedirect.com/science/article/pii/S014829631930061X
Shan, S., Luo, Y., Zhou, Y., & Wei, Y. (2018). Big data analysis adaptation and enterprises’ competitive advantages: the perspective of dynamic capability and resource-based theories. Technology Analysis & Strategic Management, 31(4), 406–420. https://doi.org/10.1080/09537325.2018.1516866
Wang, S., & Wang, H. (2020). Big data for small and medium-sized enterprises (SME): a knowledge management model. Journal of Knowledge Management, 24(4), 881–897. https://doi.org/10.1108/jkm-02-2020-0081
Xu, T., Shi, H., Shi, Y., & You, J. (2023). From data to data asset: conceptual evolution and strategic imperatives in the digital economy era. Asia Pacific Journal of Innovation and Entrepreneurship, 18(1), 2–20. https://doi.org/10.1108/apjie-10-2023-0195 
S1 Table. Table 1. Description of the included studies

First author, year of
publication	Type of paper	Field (subfield)	Motivation for using unstructured data	Type of unstructured and structured data being integrated
Badawy R., 2019	General (Review)	Neurology (Parkinson’s disease)	•	Information enrichment: insights into patients’ symptoms, disease progression, and treatment efficacy
•	Cost effective and efficient
•	Remote, long-term monitoring in naturalistic settings
•	Increased statistical power of clinical trials
•	Reduction random sampling issues	Data from digital health technologies; focus on metadata
Baldassano S.N.,
2019	General (Review)	Neurology (Status Epilepticus)	•	Personalized medicine
•	Patients’ stratification for prognostication
•	Patients’ identification for interventions	Big data, more specifically: EHRs, administrative databases (e.g., data collected for billing purposes), iEEG, multimodal ICU data, physiological data (e.g., heart rate, respiratory rate)
Blair L. M., 2016	General (Overview)	Mental Health (Pediatrics)	•	Ease of access
•	Nationally representative sampling
•	Efficiency and lower cost	Publicly available big data
Deferio J.J. 2019	General (Perspective)	Mental Health	•	Richer contextual information
•	More robust set of patients social variables
•	Identification of social correlates of health outcomes	Data related to social determinants of health
EHR
Espay A. J., 2016	General (Review)	Neurology (Parkinson’s Disease)	•	Information enrichment
•	Treatment optimization
•	Maximization of “ecological” validity
•	Monitoring
•	High resolution descriptions
•	Quality of care
•	Personalized medicine	Data from health technology (e.g., wearable devices)
 

Foreman B., 2020	General (Review)	Neurology (Neurocriticial Care)	•	Patient cohorts’ enrichment
•	Population-level insights
•	Precision medicine
•	Evaluation of interventions
•	Cost efficient
•	Precision medicine	ICU data, EEG, unstructured free-text clinician notes, Digital Imaging and Communications in Medicine standard imaging, Logical Observation Identifiers Names and Codes (LOINC), standard laboratory values
Gillan C. M. 2021	General (overview)	Mental Health	•	Higher-order level of analysis
•	Dense sampling
•	Repeated-within subject measurements
•	Data collection in naturalistic setting
•	Longitudinal data
•	Prediction
•	Better understanding of mental illnesses	Smartphone data Social media data
Structured data from neuroscientific research/neural measurements(e.g., fMRI, EEG)
Hafferty J. D., 2017	General (Commentary)	Mental Health	•	Naturalistic clinical settings
•	Analysis of rarer clinical conditions or subject areas
•	Prediction
•	Personalised medicine research
•	Better modelling of the ‘bio-psycho- social’ outcomes of psychiatric illness	Big Data in general
Hemingway H.,
2017	General (Overview)	Cardiology	•	High resolution results and large-scale studies
•	Real time analytics
•	Treatment improvement
•	More efficient and cost-effective methods	EHRs (structured and unstructured data); biobanks, genomic consortia; any researcher-generated data (e.g., omics data)
Rodriguez A., 2018	General (Overview)	Neurology (Neurocritical care)	•	Better patients’ stratification and management
•	High resolution descriptions	Genomic, imaging, physiological, and phenotypic
information (e.g., EEG), pre- and post- psychometric testing, EHR
 
Rumsfeld J. S., 2016	General (Review)	Cardiology	•	Prediction
•	Disease detection
•	Monitoring
•	Providing high resolution descriptions and large-scale studies
•	Prescriptive analytics
•	Quality of care and performance measures
•	Public health improvement	Big Data in general
Schofield P., 2017	General (Editoria l)	Mental Health	•	Information enrichment: data about hard- to-reach groups, new relations between clinical outcomes and other factors	EHRs, data from other databases (e.g., hospital episode statistics, educational data)
Shen B., 2018	General (Review)	Neurology (Parkinson’s disease)	•	Screening
•	Holistic descriptions
•	Information enrichment	EHR, physiological data (EEG, electrocardiogram), omics data, neuroimaging data, epidemiologicaldata
Silverio A. 2019	General (Overvie w)	Cardiology	•	Insights into patients’ life, behavior, and symptoms
•	High resolution descriptions and large- scale studies
•	Monitoring
•	Public health improvement
•	Prediction
•	Personalized medicine
•	Quality of care and performance measures
•	Genetic insights	‘Big Data’ in general
Stephenson D., 2020	General (Review)	Neurology (Parkinson disease)	•	Evaluation and subsequent approval of novel treatments
•	Assessment of aspects of the disease
•	Improvement of outcome measures
•	Better patient enrolment and stratification
•	Monitoring
•	Real-world data
•	Implementation of remote trials	Data from broad range of digital health technologies (e.g., smartphone applications, wearable sensors, GPS, EEG, digital diaries)
 
Termine A., 2021	General (Review)	Neurology (neurodegenerati ve disorders such as Alzheimer’s
and Parkinson’s diseases)	•	High resolution descriptions
•	Accuracy of analytical results
•	Quality of care
•	Prediction
•	Personalized and precision medicine
•	More efficient and cost-effective	EHRs, multi-omics, neuroimaging and wearable sensors data
van den Heuvel L., 2020	General	Neurology (Parkinson’s disease)	•	Information enrichment
•	Real-time data
•	Personalized precision medicine and decision-making process	Big data in general
Andy A. U., 2021	Research article	Cardiology (atherosclerotic cardiovascular disease)	•	Prediction of individual risks
•	Better understanding of individual risks
•	Precision medicine	Facebook status updates, medicalrecord data
Clark R. A., 2019	Research article	Cardiology (Acute coronary syndrome)	•	Real world assessment	Quantitative and digital data
collection (GPS and GIS), qualitative patient-reported experiences (satisfaction survey), a
discharge education recallquestionnaire
Haines- Delmont A., 2020	Research article	Mental Health (Suicide Prevention)	•	Exploration
•	Prediction
•	Real-time, context-related monitoring
•	Ecologically momentary assessment in naturalistic settings
•	Real-time classification
•	Quantification of human behavior	Facebook, user data, clinician ratings, passive sensor data
 
Jacobson N.C., 2020	Research article	Mental Health (Social Anxiety)	•	Prediction
•	Monitoring	Social Interaction Anxiety
Scale; Depression, Anxiety, and Stress Scale; Positive Affect Negative Affect Schedule; passive sensor data (accelerometer data), incoming and outgoing calls and text timestamps
Li B., 2019	Research article	Cardiology (cardiovascular disease)	•	Disease diagnostic
•	Early-risk prediction	Medical big data, medical diagnostic records
Papadopoulos A.,
2020	Research article	Neurology (Parkinson’s disease)	•	Unobtrusive, remote detection of early symptoms
•	Real-time data	Passively captured data from smartphones: the tri-axial acceleration values obtained from the
Inertial Measurement Unit sensor of the
smartphone and the keystroke timing data (press and release timestamps of each keystroke capturedduring typing with the smartphone’s virtual keyboard)
Payrovnaziri S. N.,
2019	Research article	Cardiology (Acute myocardial infarction, post myocardial infarction syndrome)	•	Prediction
•	Effective and efficient
•	Precision medicine
•	Quality of care
•	High resolution descriptions	Demographic and admission data (from EHR), free text from dischargesummary
Ross E. G., 2019	Research article	Cardiology (periphery artery disease)	•	Risk prediction of local populations or more specific disease states
•	Automatization of risk stratification	International Classification
of Diseases, Ninth Revision (ICD-9) codes, Current Procedural
Terminology codes, lab test values, prescription medications,
vital signs, unstructured
clinical notes
Sajal S. R., 2020	Research article	Neurology (Parkinson’s diseases)	•	Increased accuracy
•	Understanding of disease history, progression, and risk factors	Rest tremor and vowel phonation data acquired by smartphones
 
Sükei E., 2021	Research article	Mental health	•	Prediction of mood states
•	Assessment of behavioral features	Passively sensed behavioral data from6 sources, users’ record information
Ahn I., 2021	Paper on Database s	Cardiology	•	Early-risk prediction
•	Discovery of risk factors and detection of their interactions
•	Prevention
•	Improvement of treatment planning	Structured data from electronic health records: demographics, vital signs, medication, laboratory test results, patient history questionnaire.
Unstructured data from text readings of electrocardiogram, coronary artery computed tomography, single-photon emission computed tomography
Matoba T., 2018	Paper on Database s	Cardiology	•	Real-time data
•	Precision medicine
•	Complementation to randomized clinical trials	Medical records, electronic
data regarding medical activity and procedures; key data regarding coronary angiography and percutaneous coronary intervention
Perera G., 2016	Paper on Database s	Mental health	•	Information enrichment
•	Novel investigations
•	Detection of patterns in patient care and treatment habits
•	Enhancement of research question	EHRs, diverse databases

" Now based on this and to further extend this paper I need to do the next project report. This is an individual project that will give you an opportunity to research the DRM (Data Resource Management) in some depth. The project will develop your understanding of DRM technologies and essential data DRM concepts and their application in an organisation.
It is most important that you address clearly and deeply how unstructured data can be exploited for the benefits of the organisation. Therefore, the write-up should include the identification of beneficial unstructured data sources, the identification of what data is therein marked for extraction, the tagging of these data to existing entities within the structured data architecture schema, and finally, the additional, significant, benefits obtained from the analysis when unstructured data elements are included in the overall strategic plan.
It is essential you research DRM policies in an organisational setting using real-time business materials.
Choosing a topic and working with an organisation
•	You may choose the same DRM issue used in your group presentation.
•	You are to engage with a real organisation and assist them with understanding a Data Resource Management (DRM) issue that would benefit from unstructured data inclusion and analysis.
•	Alternatively, you are to develop a comprehensive case study that discusses a DRM issue that would benefit from unstructured data inclusion and analysis in an organisation and use this as your practical reference.
Word count
•	Approx. 3000 words (not including Executive Summary, Reference List & Appendices)
Format
You will prepare an academic paper containing:
•	Abstract (brief summary describing the issue and possible resolutions or ways forward)
•	Title
•	Executive Summary
•	Introduction
o	Overview of the business issue
o	Research Approach
•	Literature review
•	Results
•	Discussion
o	Suggestions for further work
•	References
•	Appendix: Complete case study (if applicable)


Please strictly follow this marking Criteria for each section:
Abstract - Research relevance
The paper demonstrated ability to identify relevant research problems and scope of the research.
The paper was excellent with outstanding ability to identify relevant research problems and opportunities. Ability to conceptualise problems and situations from a range of perspectives.

Introduction
Present an excellent introduction that Accurately and succinctly outlines all critical elements of the document; information is tailored toward audience.
Research Approach: Demonstrated ability to design quality research approaches and use appropriate research methods
Showcase an Outstanding ability to design quality research approaches and use appropriate research methods. A logical argument for the approach is clearly articulated.
Literature Review: Ability to use references to support arguments
Ensure that all Relevant resources are expertly used to support arguments throughout.
Discussion: Analysis and reasoning (a): Quality of summarization and analysis of data
Make sure to present a comprehensive summarization of data with extensive and in-depth analysis of data in terms of the research objectives.
Discussion: Analysis and reasoning (b): Quality of reasoning and insight
The paper should present an analysis that is well reasoned and highly insightful in terms of the research objectives and the context of the research.
Suggestions for further work: Synthesis and Interpretation
Present an excellent comprehensive synthesis of available information; limitations are clearly and appropriately addressed.
Presentation of Results
Ensure that the results are clear, accurate, and detailed presentation of results that are consistent with the method used and provides a strong basis for discussion.
Presentation, Mechanics, and Referencing (Style)
The paper must completely follow the APA reference style guidelines; paper has virtually no grammar, spelling, and/or formatting errors.