Benchling vs. FlaskTrack

Benchling is especially well suited to groups or contexts where there are many users who do not have a coding background but need a seamless and structured approach to data. Benchling is particularly useful in cases where there are data flows from instruments and other devices where the data can be deposited in an automated fashion. It is likely less appropriate or useful to users who are just looking for a general data warehouse solution.

Incentivized

• Excellent easy to use and beautiful UI
• Great customer support and training
• Has no code ways of retrieving and depositing data

Incentivized

• Some of the integrations can be a bit spotty so it depends on what kind of data source you are integrating
• Sometimes new users are not always aware of all the various functionality that Benchling has - can do better to provide more user awareness of more complex features

Incentivized

Benchling was much more of a full stack solution and provide much more features that were relevant to the group. Airtable was more of a generic way to manage large amounts of data, but the complexity was still high for the types of data that would be need to be managed and there would need to be some workarounds. Overall Benchling was selected since it also had an electronic lab notebook feature which was very useful to associates in addition to its data workflows.

Incentivized

• It had a positive ROI in terms of reducing the amount of time spent on data movement and curation by associates
• It had a positive ROI in terms of increasing the number of insights from structured data
• It reduced the number of data entry and analysis errors by associates which led to a positive ROI in terms of efficiency and reducing time wasted by tracking down errors in data

Incentivized