Peer-Driven Insights

Data Science & Algorithms

About this topic

Data science & algorithms are disciplines centered on the use of advanced analytical methods, statistical techniques, and computational algorithms to extract insights, identify patterns, and drive decision-making from large and complex data sets. These practices enable organizations to solve complex problems, optimize processes, and create predictive models that support strategic objectives.

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Sept 2024

How are U.S. CISOs Addressing Liability Risk?

New regulations taking effect in the U.S. mean that cybersecurity leaders could face legal liability in the event of an incident. What strategies are they using to protect themselves?

Community Posts

For data lakehouse platform utilization, our use cases vary between 1. respond to smart applications' data enquiries and 2. AI/ML data exploration. The first use case type mandates low latency responses, while the second consumes computational resources for long periods. Should we create two different lakehouse platforms to serve both use case types?

Do your company’s machine learning or data science teams fall under the purview of the CTO?

Yes74%

No21%

Other (please share below)3%

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We would like to implement an IDP (Intelligent Document Processing) solution within our organization. Does anybody have experience with the implementation of IDP solutions? If so, what should we be mindful of when selecting a software provider in order to be successful?

I'm interested in learning more about how you leverages self-serve analytics and natural language processing (NLP) capabilities to extract insights from data. Could you share your experiences and best practices? Specifically, I'm curious about: 1. Tools and Platforms: What tools or platforms do you use for self-serve analytics and NLP? How do they integrate with your existing data infrastructure? 2. Forums and Communities: Are there any forums or communities where IT and business groups discuss insights and collaborate on data-driven projects? 3. Challenges and Solutions: What challenges have you encountered while using these technologies, and how have you addressed them?

Has anyone implemented a Computer Vision solution that will find particulates in a clear liquid? Would like to hear pluses / minuses with the implementation.

When it comes to organizing your teams, how do you make sure you’re maximizing the capabilities of your data scientists within the enterprise? What is the best way to support innovation that leads to value?

Can machine learning assist in reporting safety events In hospitals?

No76%

Yes23%

What is the ideal ratio for Data Engineers to Data Scientists to ML Engineers to Product Managers? What ratio works at your organization and why?

What advice would you give to CTOs newly managing machine learning (ML) and data science teams?

Why are machine learning (ML) and data science teams reporting to the CTO?

Is anyone using the company KeystoneAI for your data science or AI work? Wondering what your thoughts are on the company and if they can scale a solution for an enterprise.

How do you balance model accuracy and explainability?

Context is more important than datum.

Strongly agree15%

Agree59%

Neutral19%

Disagree4%

Strongly disagree