DATA SCIENCE MANAGEMENT SURVEY

Thanks in advance for taking 10 minutes to share your insights. In appreciation of your time, you'll be entered for a chance to win a $250 Amazon.com gift card upon submitting. You'll also automatically receive the final report compiling survey data and key learnings.
(Just make sure you enter your email address when prompted.)
start
 
First, let's learn a bit about you.

 
What's your organization's annual revenue? *


 
What type of role are you in? *


 
Where do you go for trusted information on data science? *

Share specifics if possible, i.e. name specific media publications, analyst research, conferences, groups, etc.
 
Next, let's learn about your organization's data science story.

 
How many data scientists does your organization currently employ? *


 
How is your data science team organized? *


 
What areas are your data science team focused on? What use cases does data science at your organization support? *

 
How many models do you have in production today? *


 
How many models are being actively developed across the organization? *


 
Do you currently use a data science platform to capture team’s work, enable collaboration, ensure reproducibility, track versions, and/or monitor models? *


 
By what factor do you expect your data science department to grow in 2018? *


 
How much does your organization plan to invest in data science tools/technology in 2018? *


 
Of what percentage of data science projects can you quantify the business impact? *


 
How much influence / impact does your data science organization have with business stakeholders? *


 
Below, rank each of the following in order of their importance to your organization: 
- Team productivity
- Team management
- Model risk
- Business value

(1 = least important; 4 = most important. Only assign one value to each.)
 
Team productivity: accelerating workflows to increase output *





 
Team management: retaining and hiring talent *





 
Model risk: increasing confidence in the veracity or reproducibility of production models *





 
Business value: clearly demonstrating/quantifying the return on our data science investments *





 
Is data science contributing to your business' innovation? *

     
 
Last section: Let's dig into your day-to-day workflow.

 
At your organization, how long does it take (on average) for a model to traverse the full lifecycle from ideation to data gathering to experimentation to validation to production (deployed as data product or used to make a decision in the business)? *


 
If you ran into a challenge with a production model, how long would it take you to fix? *


 
What are the top capabilities contributing to your data scientists’ success? *


 
What are the biggest challenges hindering the business impact of data science at your company? *


 
What percentage of your data scientists’ time is spent doing work they enjoy (exploring data, building models, testing hypotheses)? *


 
What percentage of your data scientists’ time is spent on DevOps or administrative functions (documentation, reverting project, setting up compute environments, searching through artifacts)? *


 
What types of data science tools/technologies will be high priority investments for your organization in 2018? *


 
What other feedback or thoughts do you have about the future of data science and/or improving data science processes at your company?

That's it!

If you provided your email address, we'll be in touch soon with the final report and will let you know if you've won the $250 Amazon.com gift card.

In the meantime, you might benefit from our new Practical Guide to Managing Data Science at Scale. Click the button below to download.

Thanks for your time and insight.
- Domino Data Lab -
Download: Guide to Managing Data Science at Scale
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