Frequently Asked Questions
About Data Compass
Data Compass measures data maturity by looking at the following seven facets:
- Leadership: Is the leadership responsiveness and understanding of pain points? Do they have a strategic vision for how to use data to drive the business?
- Governance: Is there a clear process for how data is managed? Are there clear guidelines around data privacy, data sharing, data access, etc.?
- Data Quality: Is the data of high quality? Is the data consistent? Is the data accurate? Is the data useful?
- Tech Stack: Is the tech stack up to date? Is the tech stack scalable? Is the tech stack easy to use?
- Project Management: Is there a clear process for how data projects are managed? Is there a clear process for how data projects are prioritized? Is there a clear process for how data projects are delivered?
- Culture: Does the data team feel like first class citizens? Is the culture of the organization supportive of data work? Is the culture of the organization data-driven?
- Impact: Is the impact of the data projects measurable? Is the impact of the data projects significant? Would the organization feel an impact if the data team were to disappear?
For now, there is a focus on the broad category of "data scientists". Of course, there is a spectrum of overlapping data science job titles/personas. We think any of the following types of roles would benefit from this current survey:
2) Decision scientist: Someone experienced with experimatation and causal inference to do smart, data-driven decisioning.
3) ML Engineer: An ML generalist with additional expertise in software engineering, data engineering, and productionizing ML systems.
4) AI/GenAI Engineer: An ML Engineer with particular expertise in deep learning, LLMs, and generative AI.
5) Data scientist: The 2010's definition was a scientist that could combine rough hacking / engineering skills with a strong math and statistics background, and domain knowledge to create a ML model to solve a problem. Nowadays, this title is a grab-bag of various responsibilities and can include potentially any of the above.
At a later date, we may expand the platform to include other types of data and data-adjacent roles (data engineers, etc.)
Yes! Although we hope you'll understand that we may need to use ads in the futureto keep the lights on (infrastructure ain't free).
There is no catch. You remain anonymous, it's a free service, and you can withdraw your data at any time. Note though, you must submit your own survey answers before seeing everyone else's.
We're aiming to collect sincere, accurate data-related feedback from data team members in good faith.
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The single individual SIGMA SQUARED LLC (Registered in Maryland, USA)
Survey Process
The survey typically takes fewer than ten minutes to complete, but you can save your progress and return later if needed.
Please reach out to us (via the Contact page) and we'll try our best to respond in a timely manner.
Results and Privacy
We hold your data and maintain your anonymity. Identifying information information such as email address, individual names, etc are not released. All data is stored securely following best practices.
Please see our community guidelines for more information on this point. We will regularly review our data (both by human eye and by analysis with large language models) to ensure that materials like proprietary source code, customer lists, executive names, manufacturing techniques, R&D activities, budgets, projections, and technical know-how are not shared.
We periodically review the open-ended text users provide with large language sentiment analysis models to examine for high and low sentiment text. We also have a team of human moderators who review flagged content.
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