- A $9B AI Failure, Examined - Dec 7, 2021.
What happened at Zillow? An important real-world lesson in... just because you have a cool AI tool, doesn't mean that alone becomes your business model.
- Why Do Machine Learning Projects Fail? - Feb 24, 2021.
At the beginning of any data science project, many challenges could arise that lead to its eventual collapse. Making sure you look ahead -- early in the planning -- toward putting your resulting model into production can help increase the chance of delivering long-term value with your developed machine learning system.
- 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.
- Why are Machine Learning Projects so Hard to Manage? - Feb 3, 2020.
What makes deploying a machine learning project so difficult? Is it the expectations? The people? The tech? There are common threads to these challenges, and best practices exist to deal with them.
- 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.
- How to Turn your Data Science Projects into a Success - Jul 14, 2017.
This interview with Dr. Olav Laudy, Chief Data Scientist for IBM Analytics, is a summary of a recent conference where he participated in a panel on the Big Data and Analytics
- 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 Projects and Distributed Data Science Pipelines – online courses - Feb 15, 2016.
If you're managing big data projects or building distributed data science systems, you will find these online courses very useful: Building Distributed Pipelines for Data, March 1-3 and Managing Successful Big data Projects, March 15-16.
- The Case Against Quick Wins in Predictive Analytics Projects - Jan 6, 2016.
While “quick wins” are desirable, getting them in a predictive project can be difficult. We review 2 major obstacles to quick wins in predictive analytics projects.
- 3 Reasons Big Data Projects Fail - Aug 24, 2015.
Download Lavastorm whitepaper: How to Overcome 3 Key Big Data Challenges - how to operationalize the results, how to enable ETL to handle complexities of Big Data, and more.
- Big Data Lessons from Microsoft “how-old” Experiment - May 19, 2015.
Salil Mehta examines Microsoft’s viral “How old do I look?” site, the limits of its age recognition, possible algorithms, and implications for Big Data analysis.
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- Interview: Hobson Lane, SHARP Labs on the Beauty of Simplicity in Analytics - May 13, 2015.
We discuss Predictive Analytics projects at Sharp Labs of America, common myths, value of simplicity, tools and technologies, and notorious data quality issues.
- Interview: Ksenija Draskovic, Verizon on Dissecting the Anatomy of Predictive Analytics Projects - Apr 15, 2015.
We discuss Predictive Analytics use cases at Verizon Wireless, advantages of a unified data view, model selection and common causes of failure.
- Interview: Satyam Priyadarshy, Halliburton on Unlocking Success for Big Data Projects - Mar 31, 2015.
We discuss Predictive Analytics in Oil & Gas industry, Big Data analytics, key drivers of success,common reasons of failure, trends, advice, and more.
- Interview: Richard Wendell, VP, Data Science, TE Connectivity on Strategy for Analytics Projects - May 23, 2014.
We discuss the last mile of the execution path of Analytics projects, five critical pillars of success and data-driven decision making through advanced analytics.
- Big Data for Business Managers - Jan 23, 2014.
Why do Big Data projects fail to deliver the promised value, that too despite the “clearly” established potential? What should business managers do to avoid the media hype and focus on achieving sustainable benefits from big data investments?