Information

This page provides basic information and observations about the Global SERS website. If you need further information please connect with us through the contact us page.

 

Why are visualizations only for private companies?

Note that the total number of companies in our database exceeds the number of visualizations of private companies. The difference is a result of our database also including companies founded post December 31st, 2009, achieved a US$1 billion evaluation, but then either went IPO, were acquired or otherwise redacted from the database.

Loading of visualizations

All SERS visualizations are drawn from live data. Please be patient with the initial visualization load. After the initial load, you will be able to move between the visualizations quickly. Reloading the page will result in another initial load. We suggest you use the generated visualization window for the extent of your visit to our site. Cookies must be enabled.

Recommended Browsers and Settings

Our experience has been that the visualizations perform best on Chrome and Edge. Cookies must be enabled.

Company profiles

As of writing (and the foreseeable future) the number of companies in our database will be greater than the number of profiles. A profile is a “scale exemplar,” a document that makes explicit the most important things a company does to scale early, rapidly and securely.

 

Structure of generic company profiles

A generic company profile consists of nine components:

  1. Image – a succinct statement that the company that has scaled uses to represent itself to the public
  2. Basics – tombstone information, overview, performance metrics, and achievements
  3. Sell (market offers) – products and services the company sold to scale
  4. Channels – what the company did to find and secure customers, including what it gave away or sold at cost to attract customers
  5. Competencies – what the company did very well to better competitors
  6. Resources – assets, processes and priorities the company used to scale
  7. Assertions – set of assertions that best represent what the company did to scale early and rapidly
  8. References – sources of information provided
  9. Contributors – names of the people who contributed to the development of the company profile

 

Structure of AI company profiles

  1. Image – a succinct statement that the company that has scaled uses to represent itself to the public
  2. Basics – tombstone information, overview, performance metrics, and achievements
  3. Sell (market offers) – products and services the company sold to scale
  4. Channels – what the company did to find and secure customers, including what it gave away or sold at cost to attract customers
  5. Competencies – what the company did very well to better competitors
  6. Distinct AI features -AI competency, AI use, expected return of customer’s investment, unique database and quantum computing
  7. Resources – assets, processes and priorities the company used to scale
  8. References – sources of information provided
  9. Contributors – names of the people who contributed to the development of the company profile

 

Topic Modelling Use Cases

There are a number of use cases for which the corpus and topic profiles may apply. These include:

  1. Apply topic model to address the needs of an industry segment
  2. Apply topic model to produce course content
  3. Apply topic model to develop an index that can be used to track performance of new companies that wish to scale
  4. Apply topic model to make explicit a particular domain (e.g. AI and ML powered remote devices for health diagnostics)
  5. Apply topic model to define a part of the domain and relate the part to the other parts of the domain (see Bailetti-Tannev and Bailetti-Craigen articles in the February 2020 issue of TIM Review for examples)
  6. Apply topic model to produce course outlines
  7. Apply topic model to provide experiential learning experiences to students or practitioners
  8. Apply topic model to sharpen our understanding of a domain (e.g., Environment and Innovation noted in Category C above)
  9. Apply topic model to do research (e.g., stability of topics, user understanding of topics, keywords and assertions
  10. Apply topic model to better understand the external environment (e.g., who is interested in applying AI to scale companies)
  11. Apply topic model to match buyers and sellers
  12. Apply topic model to examine takeaways
  13. Apply topic model to examine competitive offers
  14. Apply topic model to organize notes extracted from reading article or books
  15. Use the topic model to visualize data and information

 

W-tool

Professor Micheal Weiss (Micheal.weiss@carleton.ca) has developed the tool we used to perform the topic modelling analyses. We are very grateful for his contribution to this project.

To perform the analysis, the researchers used the W-tool, which can be found at W-tool.

To use the tool, one must submit a corpus (in this case the SERS corpus assertions); stop words (these are words that are not meaningful to topic identification); and concepts (where we identify phrases that capture specific concepts that we wish to highlight.

Please contact Dan Craigen (Daniel.Craigen@carleton.ca), Stoyan Tanev (Stoyan.Tanev@carleton.ca) for further information on the topic modelling analyses.