How much better can your decisions be? Many organizations have data that could give powerful insights into predicting future outcomes, such as which sales techniques will work or which employees to hire.
But the odds are, your organization is misusing data, causing wasted time, lost sales, and poor results. For all the hype about big data, data science, machine learning, evidence-based decision-making, etc., far too much research and data analysis is wrong.
Large-scale replication projects in everything from psychology to cancer research have shown that anywhere from 30% to 80% of experimental work is not replicable. These same problems can apply to experiments and data analysis in business. Indeed, even Harvard Business Review articles and well-known books (A/B Testing) have offered advice that is directly contrary to good statistical practice.
I'm one of the nation's leading experts in improving research quality, and most recently was hired to consult with the U.S. Department of Health and Human Services (HHS) to design a conference on reproducibility (for my bio, click here). I can help your organization improve your data analytics or research capacity, leading to better results.
We could start with an initial trial that will be free unless we both agree that I can substantially improve your research capacity.
If you're interested, email me at email@example.com.
Auditing data analytics done in-house or by consultants, so as to improve quality and accuracy
If you've taken the trouble to invest in data or experiments, whether in-house or via other consulting firms, you should be sure that those initiatives meet the ever-evolving best practices for reliability and reproducibility. Just as manufacturing processes underwent a revolution in quality control (e.g., Six Sigma), data analytics needs the same improvement in quality assurance. I can help.
Providing training (such as lectures or workshops) on reproducible research practices
I have been invited to lecture on reproducible research practices at NIH, DARPA, IARPA, and major universities.
Whether it's a one-time lecture or a more in-depth series of workshops, I can help your company's analysts and data scientists implement best practices.
Auditing the quality of research before a particular investment moves forward
For example, a biotech company might be considering partnering with a university to launch a product based on research from nutrition, neuroscience, etc. I can help coordinate the due diligence on that line of research to see whether it is robust and high-quality.
"I sleep better at night having him evaluating everything I do. He's like a human fitbit."
Managing Partner, The City Fund
" I cannot imagine how much progress would have been made in furthering open science without [Stuart's] leadership."
President, National Academies of Sciences, Engineering, and Medicine
“Our biomedical science enterprise depends on confidence that research results are reliable and can be reproduced when applied in a manner consistent with the initial research."
Former FDA Commissioner; Duke University; Verily Life Sciences
How to structure your funding of research so that it avoids reproducibility problems, but instead is more accurate and impactful.
Foundations fund a great deal of academic research, but too often take a hands-off approach that fails to require best practices such as data sharing. I can help develop a research funding policy that will ensure higher quality.
How and when to fund evaluations of your grantees and initiatives, and how to set up an internal research unit.
Larger foundations usually have a director of evaluation, but smaller foundations often don't. I can give concierge advice on what grantees or initiatives you should evaluate, and how.
Reproducibility review of the research supporting a particular initiative.
Many foundations have launched initiatives to address problems in education, poverty, and other social issues. But much of the social science research in those fields is of low quality, and foundations shouldn't base a multi-million dollar investment on that research without a critical look at it.