Scientific AI Readiness Assessment

Scientific AI Readiness Assessment

Assess the Scientific AI readiness of your organization related to the most fundamental aspect – your data ‍

The biopharmaceutical industry is at a tipping point. With the development of new therapies taking more than ten years on average and consuming more than $2B companies need to change their approach. A key strategy is to move away from expensive and time-consuming work performed by humans and to leverage digital technologies. These include Artificial Intelligence (AI) and machine learning, in silico, and digital twins.

Unfortunately many organizations have difficulties leveraging AI technologies and realizing performance gains. All these methods are highly data and domain knowledge dependent and have one prerequisite in common: the access to the right data in the right quality and the right amount. The complexity of scientific data is adding on the hurdles biopharmaceutical organizations encounter on their journey of Scientific AI.

Take this short survey and learn how ready your organization is to efficiently and effectively take advantage of Scientific AI through the right scientific data strategy to accelerate and improve business outcomes.

Let's Go!

First, tell us a little about yourself

We use this information to send you an assessment report. This information is never shared with any 3rd party. ‍

Name
Email Address
Company

General

Do you have a cohesive data strategy in place?

• Yes
• Not yet but planning
• No

Are you confident about the security of your scientific data?

• Yes fully
• Only to a certain extent
• No

Is your scientific data FAIR (findable, accessible, interoperable, reusable)?

• Yes fully
• Only to a certain extent
• No

Are you trusting the integrity and traceability of your scientific data?

• Yes, we do have a FAIR data initiative in place
• We are working on making our data FAIR
• What does FAIR data mean?

Are you aware of the key scientific data workflows of your scientists?

• Yes, we have defined them all
• We are in the process of identifying them but it is hard
• No, we don’t know how to identify them

Data Access

How many data repositories do your users (scientists, data scientists) have to access?

• 10 or less
• More than 10
• More than 20

How much time are your users spending to move data? (time per user)

• less than 2 hours per week
• About 2 hours per week
• Clearly more than 2 hours per week

How long does it take users to find the data they need? (time per user)

• Less than 1 hour per week searching
• About 1 hour per week searching
• Clearly more than 1 hour per week searching

How easy is it for users to access scientific data?

• They can directly access their data from their favorite application
• They have to move data to access it
• It is very hard to access data

Does your data science team have enough data for robust and meaningful AI outcomes?

• Yes, we have enough data
• We are managing but it would be good to have more
• No, we clearly don’t have enough data

Data Transformation

Are your scientists able to compare scientific data from different sources?

• Yes
• They need to use some workarounds
• They cannot easily compare their data

Can your scientists use their best-in-class application to analyze and visualize their data?

• Yes
• They need to use some workarounds
• They cannot easily compare their data

How much time are your data engineers spending on harmonizing your scientific data?

• They spend clearly less than 2 hours per week
• They spend about 2 hours per week
• They clearly spend more than 2 hours a week

Are you able to add meaningful and harmonized metadata (additional information about your data) to your datasets?

• Yes, our teams can do it
• We are trying to do it but are only partially successful so far
• No, it is too hard as we lack the resources and/or the expertise

Can you easily reuse your scientific data?

• Yes, with all the applications we are typically using
• Only with some applications
• No, only with the software we used for its creation

Collaboration

With how many internal teams are you collaborating?

• 10 or less
• More than 10
• More than 20

How many external partners (CRO, CMO, CDMOs) do you have?

• 10 or less
• More than 10
• More than 20

Can you easily exchange scientific data across the value chain (from discovery to commercialization)?

• Yes, teams can easily collaborate on data
• Only partially
• Data exchange is really hard

Are you satisfied with your ability to exchange scientific data with your partners?

• Yes, we can easily collaborate on data
• Sometimes we encounter hurdles in the collaboration
• No, exchange is difficult and not in real-time

Are the datasets complete that you send / receive from other entities?

• Yes, we get the full datasets
• No, but we get the data we need today
• No, data is often incomplete and therefore not meaningful

AI

Is data access a hurdle for your AI initiative?

• No, we can access the data we need
• It is a bit time consuming but we manage
• The lack of data access is hampering the success of our AI initiative in science

Do you have large-scale data accessible to create / train / validate your scientific AI models?

• Yes, we have enough data
• We are managing but it would be good to have more
• No, we clearly don’t have enough data

How much time are your data scientists / data engineers spending in preparing data for AI?

• They spend clearly less than 2 hours per week
• They spend about 2 hours per week
• They spend clearly more than 2 hours per week

Are you confident about the outcomes/results of your Scientific AI algorithms?

• Yes, fully
• We trust it but really understand the details
• No, it is a black box for us

What is the percentage of the prediction accuracy of your AI models?

• 80% and more
• Less than 80%
• Less than 50%

Thank you!

for your AI Readiness Assessment submission.

You should receive your assessment report via email shortly.

Learn more about TetraScience offerings at tetrascience.ai