Developments in Practice VII: Developing and Delivering the IT Value Proposition
Bibliographic record
Abstract
In spite of many years of effort, we are still not able to articulate and deliver IT value accurately. Unfortunately, "silver bullet thinking" still predominates (i.e., plug in technology and deliver bottom line impact) in organizations today. IT value is a multi-layered concept, far more complex than it first appears. To examine this complex concept and how it is understood in IT organizations, the authors convened a focus group of practicing IT managers from a number of different industries. This paper, using the inputs from the focus group, explores how organizations are attempting to determine and develop effective IT value propositions. It describes the three components of this proposition: identification of potential value, effective conversion, and realizing value. The paper then derives a number of principles of delivering IT value. We conclude that there is no single agreed-on notion of business value. Therefore, it is important to make sure that both business and IT managers work to a common value goal whether traditional cost reduction, process efficiencies, new business capabilities, improved communication, or any other objectives. We also suggest that technology is being used as a catalyst to drive many different types of organizational transformation and strategy. Therefore, IT value can no longer be viewed in isolation from the other parts of business, namely people and information.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.019 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".