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6 Deadly Mistakes by Data Scientist Resulting in Project Failure

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“Learning by making mistakes and not duplicating them is what life is about.” says- Lindsay
Fox, this is the learning we should take from other’s mistakes. Let us apply this thought to the Data Science Project, which is overly complex and needs the implementations of a Well skilled team, which involves data assembling, developers, data scientists, engineers, etc. to make a project successful with their special experience and knowledge.
Having said that, Companies cannot handle foul data practices and repeated mistakes from their data science team. The selection or hiring of a Data Scientist is a crucial and high cost involved job for the organization panel, even a minor mistake of a Data Scientist can cost a lot to the company. And During the Making of this team, there is many challenges faced by companies: more than 90 percent of data projects fail.
Several factors lead to the failure of Data Science Projects including- Time, team, Planning,
human Skills, and Impact. For a business to stay competitive in the market and to outrank its competitor, it takes more than just the analysis part. It's difficult to conclude whether the project turns out to successful without assessing the quality of the data extracted.


Having all this in mind, let us talk in detail about the 7 deadly mistakes, why data science projects fail.


1-Sticking with An Outdated Set of Questions 


A good idea is to start the project by considering the objective that will add value to the business. It isespecially important to choose the question wisely, which points towards which data should be analyzed. The objective of the project is to find out the solutions to those problems, which are identified in the form of questions. This practice serves to restructure the data science process by pairing business authentication with business accomplishment. Therefore, selecting the right questions is especially important for the ultimate success of the project.


2-Misapplication of Data Science ModelsOverly complex models

Many data science projects start without an outline, which leads to the misapplication of data science models. “Less is more” Sometimes data scientists make a simple problem complicated but implementing the complex model. This takes away the focus of data scientists from the final picture and distracts them from the right solution.


3-Probability and Statistics Concepts Go Wrong or Get Ignored

Working with statistics can be a tedious task because of so much of the data and datasets to work upon, but the real problem arises when data scientists consider covariance and correlations the same. Although these principles have lots of similarities, they are different and data scientists need to understand this. The clarity in the concepts is another excessively big issue, and Data Science Must be Pro at it.


4-Finalizing the Wrong Project That Already Has A Solution 

“If you are curious, you’ll find the puzzles around you. If you are determined, you will solve them.” Well quoted for Data Scientists. Slightly connected to our first point, about the right set of questions. Many times, Data scientists land up to the problem, which already has a solution, as they tend to sometimes complicate the analysis which shifts their focus. Data scientists are opportunities seeker, targeting the right project, and setting the goals considering the business, are the signs of a great Data Scientist. 


5-Poor Project Management Skills

Will competent Data Scientist with the quality including- data modeling, determining models, and then narrating it into a story. An ideal data scientist team consist of Data Scientist, Data Engineer, and subject matter expert from different department depending upon the project aim. Possibility, effectiveness, and budget are thoughtful components for any project’s success or failure. Consequently, the expertise of data science and project management skills are highly essential to be successful in desired. The Data Science project requires the skills of coding, statistics, and machine learning algorithms. With all this is especially important to include a team member who understands the internal business operations. Hence, a well-designed team act as fuel to the project which will eventually lead to success.


6-Overpromising and Over expectations 

The importance of commitment depends upon the complexity of the project. Sometimes for small projects, data scientists and business authorities play the tactic of overpromises to acquire the projects and clients. Which might backfire them at the time of the final stage of presenting it. 

Small or large project both are equally important to the company, failing in delivering as promised, leaves a negative impression and decreases data scientist credibility of handling projects successfully. 


Everyone desires a successful outcome, not only data scientists but cooperates too. Sometimes the expectations for the business are beyond what is achievable. Therefore, the best advice would be to use the Work Breakdown Structure. Start with small, stay focused, and never keep your bar to the impossible results, stick to what is possible and make sure you give your best to it. 



These above points are some of the deadly mistakes done by data scientists & the team for the project’s collapse. These are some of the common mistakes data scientists make. The above points are eye-openers so, in the future, you can minimize the risks of the failure of the projects and yield the best outcomes through detailed analysis and best practices. 


Author Bio





Senior Data Scientist and Alumnus of IIM- C (Indian Institute of Management – Kolkata) with over 25 years of professional experience Specialized in Data Science, Artificial Intelligence, and Machine Learning.

PMP Certified

ITIL Expert certified APMG, PEOPLECERT and EXIN Accredited Trainer for all modules of ITIL till Expert Trained over 3000+ professionals across the globe Currently authoring a book on ITIL “ITIL MADE EASY”.

Conducted myriad Project management and ITIL Process consulting engagements in various organizations. Performed maturity assessment, gap analysis and Project management process definition and end to end implementation of Project management best practices

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