Snowpack Data

Data Maturity Benchmark

Please fill out all of the questions below to the best of your ability. We will use your responses to send you a free summary of how you compare against similar companies, as well as recommendations on what to focus on next to further improve your data capabilities.

Personal Information

Please provide your email address to receive your benchmark summary and recommendations, as well as your current role/company to help us a better tailor your summary & recommendations.

Email Address

Company Name

Role/Title at your Company

1. Do you have a data warehouse set up? If yes, what do you use?

Yes, we use a modern cloud data warehouse (e.g., Snowflake, BigQuery)

Yes, we use an on-premise data warehouse

No, we don’t have a data warehouse

Additional Details

2. Who manages data pipelines at your company?

Dedicated data engineering team

Shared responsibility across different teams

Engineering team currently fills the gap here

No one currently owns this

I'm not sure

Additional Details

3. What tools do you use for data ingestion and transformation?

Custom-built scripts or ETL processes

Third-party tools (e.g., Airbyte, Fivetran, dbt)

We don’t have a defined process for this yet

I'm not sure

Additional Details

4. Do you currently have any data engineers, analysts, or data scientists on your team?

Yes, we have a dedicated data team

No, we rely on external support or contractors

We have engineers that support ETL, but no dedicated analytics support

We have data-savvy business analysts, but no dedicated owners of our ETL process

Other (Please provide additional details)

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5. What is the approximate scale of data you work with?

Thousands of rows

Millions of rows

Billions of rows

I'm not sure of the exact scale

Additional Details

6. How frequently do you refresh or update your data?

Daily or multiple times per day

Real-time or streaming

Weekly

Monthly or ad-hoc

I'm not sure

Additional Details

7. What kind of business insights do you generate from your data?

Descriptive (e.g., reporting, dashboards)

Diagnostic (e.g., root cause analysis)

Predictive (e.g., forecasting, machine learning)

Prescriptive (e.g., optimization, recommendation engines)

We don’t currently generate business insights from our data

Additional Details

8. How do you ensure data quality at your company?

We perform manual checks

We have automated data validation processes

We rely on external tools for data quality

We don’t currently have a data quality process in place

I'm not sure

Additional Details

9. What are your current pain points in your data processes?

Inconsistent or unreliable data

Difficulty integrating data from different sources

Lack of expertise or resources to manage data

Difficulty drawing insights or value from the data collected

Other (Please provide additional details)

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10. What are your goals for improving your data capabilities in the next 12 months?

Establish or upgrade a data warehouse

Expand reporting and analytics capabilities

Improve data quality and governance

Establish a data team

We haven’t defined any specific goals yet

Other (Please provide additional details)

Additional Details

11. What BI tools, if any, do you use for reporting and analytics?

Modern BI tools (e.g., Looker, Tableau, etc.)

We don’t currently use a BI tool

In-house/custom reporting solutions

We mostly use spreadsheets for reporting

Additional Details

12. How accessible is your data within the company?

Data is accessible only to those with SQL knowledge

Data is accessible to anyone (non-SQL users) via self-serve tools

Only a few people have access to data

Data access is very restricted due to compliance/security reasons

I'm not sure

Additional Details

13. Are you currently using any AI or machine learning capabilities?

Yes, we have deployed AI/ML models in production

Yes, but we are still in the experimental or development phase

No, but we are exploring AI/ML

No, we have no plans to use AI/ML in the near future

I'm not sure

Additional Details

14. How much trust is there in the data across your organization?

High trust: data quality is consistently good, and people rely on it for decision-making

Moderate trust: occasional issues with data quality, but mostly reliable

Low trust: frequent issues with data quality, leading to conflicting insights

No trust: data quality is poor, resulting in decisions being made without data

I'm not sure

Additional Details

15. What level of standardization do you have for metrics and KPIs?

Metrics are not standardized, leading to differing conclusions

Metrics and KPIs are fully standardized and agreed upon by all teams

Some metrics are standardized, but there are discrepancies between teams

We don’t currently have any defined metrics or KPIs

I'm not sure

Additional Details