Data Teams Survey 2024

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Thank you for taking the time to fill out this survey. This survey takes 5 minutes or less to fill out.

Our purpose is to get a wider view of the issues data teams are experiencing in the field and the best practices they are using. We really appreciate your open and honest responses on this survey.

By filling out the survey, you will be entered into a drawing to win one of five free copies of Jesse Anderson's latest book, Data Teams (https://www.datateams.io). Also, we will share the aggregated data with you. To participate, you must include your real email address. We won't spam or add you to an email list.

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Data Strategy and Maturity

This section asks about how your data strategy has been affected by the economy, how successful your projects are, and the maturity of your projects.

Have you had to pivot your data strategy in the current economy?

Yes

Somewhat/partially

Other:

Clear selection

How are you using Generative AI/LLMs/ChatGPT?

Not using

Code generation

Code Debugging

Data Processing

Ideation/Copy Creation

As The Backend Behind An API Endpoint

Other:

Has the perception of the value of data within your company changed in the past year?

Strongly Disagree

Strongly Agree

Clear selection

How successful would the business say your projects are?

Highly Unsuccessful

Highly Successful

Clear selection

How successful do you think your big data projects are?

Highly Unsuccessful

Highly Successful

Clear selection

How mature are your big data efforts?

We’re just starting to look at Big Data

We’ve chosen uses cases, started choosing technologies, and hiring a team

We’re in the middle of or have completed a proof of concept

We’ve started coding, but are early in the project

We’ve done our first release of big data code to production and we’re gaining some value from data

We’re quite advanced, have been using big data for a while, and are gaining value from data

Clear selection

Which methodologies are you using (check all that apply)?

DataOps

Data Mesh

Homegrown

No methodology

Center of Excellence

Other:

Is your company planning to change the size of the data team? Note: while companies are laying off, this question is specific to data teams.

Increase (e.g. continue hiring)

Some increase (e.g. transfers from other departments)

Keep the same size

Some decrease (e.g. hiring freeze)

Decrease (e.g. layoffs)

Unknown/Not decided

Clear selection

Best Practices and Value

This section covers the best practices you're employing and the value being created by your data teams.

What do you think have been the biggest challenges to achieving the highest possible value from your data?

Missing data science team

Missing data engineering team

Missing operations team

Poor interactions and feedback from the business

Missing technologies or bad architecture

Issues productionizing the code, models, or infrastructure

Security or privacy issues such as authorization, permissions, or access control

Lacking enough individual contributors or training

Friction from working with other parts of the organization

Inability to execute on tasks and stick to deadlines

Our goals are too ambitious to attain

We hired a consulting company and they aren't delivering

Shifting priorities

Other:

Has working remotely impacted your team's productivity negatively?

Strongly Disagree

Strongly Agree

Clear selection

Of the three data teams, which teams do you already have at your company?

Data science

Data engineering

Operations

Check each box if your company's definition of the following job titles matches this definition.

A data scientist is someone who has augmented their math and statistics background with programming to analyze data and create applied mathematical models.

A data engineer is a software engineer who has specialized their skills in creating software solutions around big data.

An operations engineer is someone with an operational or systems engineering background who has specialized their skills in big data operations, understands data, and has learned some programming.

Which best practices are you doing to make sure data projects are meeting business needs?

We only chose our technologies after understanding the use case

We have a data engineering team with all required skills

We have a data science team with all required skills

We have an operations team who understands data and big data frameworks

We have unit tests and integration tests for our code

We gained velocity by breaking down our project into crawl, walk, run phases

We’re making extensive use of automation

We’re using continuous integration and continuous deployment methods

We have provided our team with training on the technologies they’re expected to use

We work closely and consistently with the business on data projects

What do you think you really nailed in your management of data teams?

What do you think are the weakest points in your management of data teams?

About You and Your Company

Just a few final questions about you and your company

How big is your company?

1-100 employees

101-1,000 employees

1,001-5,000 employees

5,001-10,000 employees

10,001+ employees

Clear selection

What is your position at your company?

Executive Management

Middle Management

Team/Technical Lead

Data Engineer

Architect

Data Scientist

Operations

Other:

Clear selection

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