4 Types of Challenges in DS/AI Projects & Initiatives
In this post, I have analyzed frequent challenges encountered by DS/AI leaders & experts in their projects/initiatives. I have re-organized these challenges into 4 areas of concerns, my idea is to build a framework around these challenges to suggest possible solutions in upcoming posts.
DS/AI promises considerable economic & social benefits, even as it disrupts the way we work. Almost everyone agrees that it is a field which can change the world, from healthcare to countering terrorism and even arts & sports.
DS/AI leaders & experts are facing different challenges from ideation to productization, it is estimated that between 70% and 85% of DS/AI projects fail.
To get a holistic view of the challenges & failures, I looked at various articles available on the internet about the challenges in DS/AI projects & initiatives. You can find all the articles I referred at the end of this post.
In the past few weeks, I have also spoken to many DS/AI leaders & experts on this topic and they have also given some useful insights. I will cover these specific insights into the challenges in the upcoming blog-posts.
In order to shed some light on the reasons why we observe such a high failure rate, I also analyzed the results of a survey conducted by Kaggle in 2017. You can see the full report here. The part of the survey relevant to this article is about the challenges companies face as far as their DS/AI efforts are concerned. The following chart shows the top fifteen challenges:
While I am still documenting the challenges & possible solutions, I can categorize these challenges in four broad segments: — Cultural challenges (right talent, data literacy, realistic expectations etc) — Data-related challenges (data access, data quality, lifecycle management etc) — Operational challenges (suitable operating model with specific roles & responsibilities) — Technology-related challenges (appropriate tech-stack, right infrastructure etc).
Challenges in DS/AI Projects
Based on the above-mentioned sources (surveys, articles & experts) in this post, I have collected all the challenges and tried to categorize them into four areas of concern — Culture, Operation, Data, Technology:
Data Quality — Data
Talent Gap — Operations
Company Politics — Culture
Data Access — Operations
Data Literacy — Culture
Data Privacy — Data
SME Gap — Operations
Heterogeneous Tech-stack — Technology
Unrealistic Expectations — Culture
Co-ordination with IT & other deptt — Operations
Stakeholders’ Buy-in — Culture
Sponsorship — Culture
Deployment — Operations
Algorithms Limitations — Technology
Data Consolidation — Data
Opportunity Assessment — Operations
Model Explainability — Technology
Agility — Operations
Data Security — Operations
Solving the Wrong Problem — Operations
Organizational Maturity — Culture
Storytelling — Culture
Area of Concerns in DS/AI
Now, let’s consolidate the above challenges in the respective area of concerns:
Company Politics, Data Literacy, Unrealistic Expectations, Stakeholders’ Buy-in, Organizational Maturity, Storytelling
Talent Gap, Data Access, SME Gap, Coordination with IT and other deptts, Deployment, Opportunity Assessment, Data Security, Solving the Wrong Problem
Data Quality, Data Privacy, Data Consolidation
Heterogeneous Tech-stack, Algorithms Limitations, Model Explainability
I believe that if we categorize these challenges in the respective area of concerns, we can build a framework around it which can address these challenges and suggest possible solutions.
I intend to explore these possibilities in the upcoming blog-posts, if it looks interesting to you, stay tuned.
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