Blog

Learn from the analysis and research written by our data experts.

11 Questions to Decide Between an Open LLM and Third-Party LLM API for your Use Case

How are clients exploring generative machine learning for various use cases?

7 Principles for Determining Which LLM Business Cases Will Work, and Which Ones Will Fail

Interest in applying generative artificial intelligence and large language models to various business challenges has surged, prompting executives to redirect resources from traditional machine learning to bolster data science R&D focused on leveraging these technologies effectively.

LLM-based Tools for Data-Driven Applications – Decomposition of Architectural Components

Advice on breaking down LLM workflows into components for better control, optimization, and tool selection in enterprise applications.

Wolfgang Halter on Bayesian Optimization for Design of Experiment, Lab Efficiency and R&D

A conversation with Wolfgang Halter

Annemie Ribbens on AI to Monitor Neurological Disorders, Analyze Medical Imaging, and Improve Drug Development

A conversation with Annemie Ribbens

Jacob Oppenheim on Investor <> Start-up Relationships, Building Data Foundations, and the Need for a Unified Data Ecosystem

A conversation with Jacob Oppenheim

Joseph Pearson on the Challenges of NGS Data Sets, the Value of APIs, and Selecting the Right Bioinformatics Tools

A conversation with Joseph Pearson

Lana Denysyk on Real-World Data for Clinical Trials, RWD Strategy and Potential Pitfalls

A conversation with Lana Denysyk

Cody Schiffer on Knowledge Graphs in Biopharma, Using LLMs to Utilize Unstructured Data and Drug Discovery

A conversation with Cody Schiffer

Vera Mucaj on Data Fragmentation, Deidentified Data, and Using Real-world Data to Improve Research Outcomes

A conversation with Vera Mucaj

Jesse Johnson on Managing Data as a Biotech Start-up, the Need for Modular Tools, and the Role of Large Language Models

A conversation with Jesse Johnson

Ming “Tommy” Tang on Data Challenges in Immuno-oncology, the Role of the Cloud, and Growing a Computational Biology Team

A conversation with Tommy Tang

Miguel Cacho Soblechero on Reducing Dropout Rates and Risk in Clinical Trials and Wearables in Healthcare

A conversation with Miguel Cacho Soblechero

Michelle Wiest on Avoiding Bias, Maintaining Data Quality, And Collaboration With The FDA

A conversation with Michelle Wiest

Markus Gershater On Design Of Experiments, Curating Data and The Role Of AI and Machine Learning

A conversation Markus Gershater

Pradeep Ravindra on Real Human Data, Putting a Pin in Compliance, and Digital Twins

A conversation with Pradeep Ravindra

Fighting Cancer with Data Science and CAR-T Therapies

On this episode of Data in Biotech, we’re excited to be joined by Pradeep Ravindra, Associate Director, Data Analytics, Manufacturing from Lyell Immunopharma

Why Experimental Design in Biotech is Broken and How to Fix It

On this episode of Data in Biotech, we’re excited to be joined by Markus Gershater, Chief Scientific Officer and CoFounder of Synthace - a digital experiment platform built for high-performance life science R&D teams to help them run more powerful experiments and accelerate scientific progress.

Data Science and Diagnostic Models - the What, Why and How

On this episode of Data in Biotech, we’re excited to be joined by Michelle Wiest, Director of IVD Biostatistics at Freenome - a high-growth biotech company that creates tools to help prevent, detect, and treat disease.

How Biotechs Can Squeeze Value from ‘Lazy’ Data

How can Biotechs squeeze value from lazy data?

A Practical Approach: 6 Ways Biotech Manufacturers Can Unlock Value from Data

Explore the challenges and opportunities of leveraging data analytics in biotech CMOs to streamline manufacturing processes, manage tech transfer effectively, identify blind spots, enhance R&D efficiency, improve compliance, and reduce waste and downtime, emphasizing tailored solutions over off-the-shelf options to maximize ROI and operational benefits.

How to Start Event-Based Marketing: A Bottom Up Approach

In this post, we highlight a technique to help you differentiate between these potential customers to optimize your marketing spend, using technology to automate much of the process.

10 Reasons to Hire an External Data Team (and 4 Reasons Not to)

Your company’s data is one of its most valuable assets. While the promise of automated machine learning and data mining seem attractive, your company will still need people to help you generate value from data for the foreseeable future.

5 Ways to Screw Up Your Digital Transformation

3 Drivers of Data-Savvy Organizations

This writing will underscore the pivotal role of data in modern business, driven by its exponential growth and potential for competitive advantage. It outlines key strategies and challenges in maximizing data value through availability, actionability, and credibility, stressing the need for structured implementation to achieve significant ROI and support long-term business growth.

How to Budget for Data Science and Data Engineering Work

Budgeting is easy for fixed costs and relatively simple for variable costs, as long as you know the drivers of the variable expense, such as number of products sold or parcels shipped.

117 Key Questions Asked by E-Commerce Leaders

We have shared some of the questions that CorrDyn has helped our clients identify and address across six areas: Finance, Marketing, Sales, Operations, Customer Support, and Technology

How to Understand Your Customer Acquisition Cost and Lifetime Value