- 85% of data science projects fail – here’s how to avoid it - Sep 13, 2021.
Here are a few common traps that data scientists can avoid to NOT be one of the 85% of data science projects that fail.
Data Science, Failure, Project
- Top 5 Reasons Why Machine Learning Projects Fail - Jan 28, 2021.
The rise in machine learning project implementation is coming, as is the the number of failures, due to several implementation and maintenance challenges. The first step of closing this gap lies in understanding the reasons for the failure.
Data Preparation, Data Science, Failure, Implementation, Machine Learning
- Predicting Heart Disease Using Machine Learning? Don’t! - Nov 10, 2020.
I believe the “Predicting Heart Disease using Machine Learning” is a classic example of how not to apply machine learning to a problem, especially where a lot of domain experience is required.
Advice, Failure, Healthcare, Machine Learning, Medical, Prediction
- When good data analyses fail to deliver the results you expect - Nov 3, 2020.
To all those Data Scientists out there who thrive on discovering actionable insights from your data (all of you, right?), take heed from this cautionary tale of a data analysis, a dashboard, and a huge waste of resources.
Advice, Dashboard, Failure, Goodhart’s Law, Project Fail
- Why BERT Fails in Commercial Environments - Mar 24, 2020.
The deployment of large transformer-based models in dynamic commercial environments often yields poor results. This is because commercial environments are usually dynamic, and contain continuous domain shifts between inference and training data.
BERT, Business Value, Failure
- Why organizations fail in scaling AI and Machine Learning - May 29, 2019.
We explain why AI needs to understand business processes and how the business processes need to be able to change to bring insight from AI into the process.
AI, Deployment, Failure, Machine Learning, Scalability
The Best and Worst Data Visualizations of 2018 - Feb 8, 2019.
We reflect on some of the best examples of Data Visualization throughout 2018, before focussing on some of the not-so-good and how these can be improved.
Advice, Best Practices, Data Visualization, Failure, Sankey
- 6 Data Visualization Disasters – How to Avoid Them - Feb 5, 2019.
If you intend to use data visualizations in a presentation or publication, be certain that your audience will understand and trust the information. Here are six mistakes you will want to avoid.
Advice, Data Visualization, Failure
- 5 reasons data analytics are falling short - Jul 30, 2018.
When it comes to big data, possession is not enough. Comprehensive intelligence is the key. But traditional data analytics paradigms simply cannot deliver on the promise of data-driven insights. Here’s why.
Big Data, Data Analytics, Failure, SQream
- 9 Reasons why your machine learning project will fail - Jul 25, 2018.
This article explains in detail some of the issues that you may face during your machine learning project.
Deployment, Failure, Machine Learning, Project Fail
- Data Science: 4 Reasons Why Most Are Failing to Deliver - May 24, 2018.
Data Science: Some see billions in returns, but most are failing to deliver. This article explores some of the reasons why this is the case.
Data Science, Deployment, Domino, Failure, Production
- AI is not set and forget - May 3, 2018.
Just like a car, AI-based system can tick along in decent shape for a while. But neglect it too long and you’re in trouble. Unfortunately, failing to maintain your AI will destroy the project.
AI, Failure, Maintenance
- How to Fail with Artificial Intelligence: 9 creative ways to make your AI startup fail - May 4, 2017.
This post summarizes nine creative ways to condemn almost any AI startup to bankruptcy. I focus on data science and machine learning startups, but the lessons on what to avoid can easily be applied to other industries.
AI, Artificial Intelligence, Failure, Startup
- Top Reasons Why Big Data, Data Science, Analytics Initiatives Fail - Dec 1, 2016.
We examine the main reasons for failure in Big Data, Data Science, and Analytics projects which include lack of clear mandate, resistance to change, and not asking the right questions, and what can be done to address these problems.
Big Data, Data Science, Failure, Project Fail
Trump, Failure of Prediction, and Lessons for Data Scientists - Nov 9, 2016.
The shocking and unexpected win of Donald Trump of presidency of the United States has once again showed the limits of Data Science and prediction when dealing with human behavior.
Donald Trump, Elections, Failure, Hillary Clinton, Nate Silver, Poll
