Leveraging data to redefine the purpose of the workplace: A case study on Cisco’s corporate real estate strategy
Bibliographic record
Abstract
Over the past five years, there has been a significant transformation in workplace dynamics that many companies are finding difficult to navigate even today. The answer to the swift but sweeping shift to hybrid work has not been easy, as companies are still grappling with determining the best strategy and how to execute it. Cisco, a global leader in networking and technology solutions, considered the disruption an opportunity to re-evaluate its workplace practices and reimagine its real estate strategy into one driven by purpose. By looking inwardly at its own data — in the form of employee feedback and metrics from its smart workplace technology — Cisco focused on understanding how and why people choose to work in the office, addressing individual and team needs. As a result, the company reshaped its views on the purpose of physical space and began investing in its global portfolio to thoughtfully create spaces that are designed to enhance the hybrid experience and bring people together for a reason. Emphasising the importance of understanding organisational goals in addition to employee preferences and needs, Cisco created a classification framework that defined what each space is for, making it easy for employees to see the value of going to the office. While creating the magnet is one piece of the puzzle, ensuring the experience is memorable and meaningful is another. Leveraging data captured from its smart workplace solutions, Cisco is enhancing the employee workplace with environmental sustainability and employee wellness in mind. Through this journey, the discoveries around thoughtfully creating spaces with intention and utilising usage and other key data metrics to manage those spaces have driven a new workplace strategy capable of evolving with the needs of the business. The lessons Cisco learned and the new practices that ensued can prove valuable for any business once they too uncover their unique purpose for the workplace. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".