Implementing Enterprise Content Management Services with an Agile Approach
Bibliographic record
Abstract
Every organisation needs access to fast online data in order to meet increasing customer demands. As a result, the information architecture of a company, how it is designed, and how information is managed are very important. Because the amount of content is growing at a precipitous rate, in order to manage it with the greatest efficacy, an online content management system is recommended. The challenge for organisations is to implement and manage a dynamic content management system that is responsive to changing requirements, while providing a structure that contributes to organisational efficiencies. How a content management system is designed and implemented (e.g. with Office 365, SharePoint, G Suite, or SAP OpenText) determines its success. This paper discusses using Agile approaches for business content management systems, to ensure that they will be used to their full potential.This conceptual research paper looks into the current context of Enterprise Content Management (ECM) and the desirable qualities of an Agile approach. First, the literature review explores the definitions, elements, benefits, and recent trends around ECM. Then, the paper discusses Agile project management (APM) with some major characteristics and typical features. Thirdly, the paper describes how an Agile ECM system should be (e.g., takes advantage of the growing Internet, cloud services, and mobile computing). An Agile ECM can be developed with an APM or a hybrid approach (i.e. using an Agile software development process to create an Agile end product).This paper covers possible methodologies based on traditional and Agile software development approaches. These encourage a flexible development approach to ECM implementation, and promoting end user involvement and their needs. An ECM system should be dynamic enough to meet the demands of future organisational growth and the business environment. It should allow integration with other new software, including those in the organisation and those in the Cloud. Two methodologies (Scrum and MIKE2.0) are explained, along with their strengths and weaknesses. The recommendation is to synthesize them so that future implementations may take advantage of techniques from both. MIKE2.0 can ensure good information governance, while Scrum can provide an innovative team-based approach and user empowerment, which are highly useful for implementing Content Services rapidly.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".