- Setup: project planning, preparation of business case, endorsement, organisational communication, and preliminary analysis.
- Review: strategic analysis (internal and external), organisational (cultural) analysis.
- Survey: survey of information users, identification and inventory of information resources, mapping of information flow.
- Account: cost, business benefit and/or value of information resources.
- Analyse: analysis of findings.
- Report: production and dissemination of IA findings and recommendations.
- Guide: organisational information management policy and/or information strategy development, implementation of recommendations, establishment of the IA as a cyclical process, and monitoring and control.
Some existing IA methodologies cover every step of this baseline. Others cover all of the steps but in a different sequence, while others add additional steps, or leave out the setup or guide stages.
However, while IA is a powerful information management practice, the methods described in the literature have not been married with recent developments in the study of information management capability and information quality management.
A recent study2 has set out to help address this gap through the conduct of a systematic literature review. The systematic review process used combines concept mapping, review scoping, and a structured search and analysis process. In the structured search, 997 initial results were returned. From these, 40 remained after title and abstract analysis, and then 22 remained after full text analysis.
Three research questions were investigated:
RQ1: What recent research (from 2011 to 2016) has been done on IA?
RQ2: What recent research (from 2011 to 2016) has been done on quality, evaluation, measurement, and maturity in the context of information management?
RQ3: In the future, how might IA researchers and practitioners synthesize the recent research on IA with the recent research on information management quality, evaluation, measurement, and maturity?
Two perspectives on quality were considered: information quality and information management quality. Information quality was understood as the “categories, dimensions, purpose-depth, and purpose-scope that shape a unit of information.” Information management quality was understood as “the ability to provide data and information to users with the appropriate levels of accuracy, timeliness, reliability, security, confidentiality, connectivity, and access and the ability to tailor these in response to changing business needs and directions.”
The results provide a high-level overview of recent trends in IA methods and theories rather than a complete analysis of every reviewed article.
Recent literature on IA:
- Much more attention has been given to theories and methods of IA than IA research outcomes and applications
- There has been limited research that focuses on IA applications and case studies
- There have been few notable attempts to extend or modify established IA methodologies for specific industries or specialized domain areas
- There is a near absence of research that focuses on IA implementation and outcomes.
Recent literature on quality, measurement/evaluation, & maturity/benchmarking:
- The greatest research attention has been given to measurement/evaluation
- There is a surprising lack of overlap between information quality and IA as conceptual foci in the literature
- There has been limited research attention on maturity/benchmarking in the information management context
- The concept with the least attention paid to it was information management quality.
From these results, the researchers conclude that:
The IA is clearly an extremely valuable information management practice, but there is still much work to be done by both IA researchers and practitioners to make the practice more widely known and used. It is unfortunate that at a time information management has become a necessity in nearly every industry and sector, IA research and practice have lagged behind other facets of information management.
In response, they outline implications for researchers and practitioners.
Implications for researchers
Direction 1: pursue contingency frameworks rather than standardization. Future research should focus on designing contingency frameworks for IA practice, with different sets of methods and tools being prescribed for different industries, business scenarios, and information cultures.
Direction 2: explore the relationship between IA and quality dimensions in more detail. Future research, which seeks a deeper understanding of the causal relationships between IA and quality, will not only provide novel contributions to the research agenda, but would also validate the power of the IA if a causal link between IA practice and heightened quality could be empirically demonstrated.
Direction 3: apply more foundational IA methodologies in full to case studies. Future research focusing on the outcomes of applying foundational and recent IA methods and theories could make a significant contribution to the IA literature by reporting on the strengths and weaknesses of IA methodologies in different use contexts, resulting in more empirical studies of IA outcomes.
Direction 4: develop theories of IA maturity and IA maturity modelling methods. In the reviewed literature, there is no article which exhibits conceptual overlap between IA and maturity.
Implications for practitioners
Recommendation 1: Measurement and evaluation of information management quality and information quality are necessary parts of the IA. Define information management quality and information quality dimensions in advance of the audit, then measure and evaluate those quality dimensions as part of the audit.
Recommendation 2: In the reviewed literature, modelling and diagramming techniques are rarely recommended or situated within a larger IA process, but the use of these techniques is essential to the holistic mapping of information resources and flow.
Recommendation 3: Document and publish more applications of IA methods so that researchers and practitioners have a greater knowledge base to draw from in studying the performance of different IA method and in innovating new IA methods and tools.
- Buchanan, S., & Gibb, F. (2008). The information audit: Methodology selection. International journal of information management, 28(1), 3-11. ↩
- Frost, R. B., & Choo, C. W. (2017). Revisiting the information audit: A systematic literature review and synthesis. International Journal of Information Management, 37(1), 1380-1390. ↩
Also published on Medium.