This presentation discusses Challenges, Problems, Issues, Measures, Mistakes, Opportunities, Ideas, Technologies, Research and Visions around Data Science HashGraph, Data Mesh, Data Trajectories, Citrix HDX and Anonos BigPrivacy Combination of these 5 and few other ideas will ultimately lead us to the VGB Platform. Will soon come up with other document explaining the vision and how exactly work on the vision to gradually develop this Platform, which fixes Data Science Efforts Globally.

1. Fixing Data
Science
Challenges, Problems, Issues, Measures,
Mistakes, Opportunities, Ideas,
Technologies, Research and Visions
Manoj Kumar Ragupathi

2. Challenges
Source:
https://www.kaggle.com/survey
s/2017
Current Relevance Discussion:
https://www.reddit.com/r/datas
cience/comments/eeok6g/how
_relevant_are_these_challeng
es_in_data_science/

3. Issues
• Wrong Focus
• Wrong Commitments and Promises
• Misunderstanding-led Wrong Expectations
• Unexplainable AI
• Narrowed and Inability to Transfer Knowledge

4. Problems
• The Over Hype – Failed Promises
• https://www.reddit.com/r/datascience/comments/egqsmy/how_many_successful_aiml_models_i
mplementations/
• https://analyticsindiamag.com/the-role-of-big-data-analytics-in-the-future-of-managers/,
accordingly says,
• Gartner reported in November 2017, that 60% of big data projects failed. A year later, Gartner analyst Nick
Heudecker said his company was “too conservative” with its 60% estimate and put the failure rate at closer to
85%. Today, he says nothing has changed.
• In July 2019, VentureBeat AI reported that 87% of data science projects never make it into production
• In January 2019, NewVantage survey reported that 77% of “business adoption” of big data and AI
initiatives continued to represent a big challenge for business, (which meant three-fourth of the software
being built is apparently collecting dust)
• Another AI Winter
• https://mindmatters.ai/2019/12/just-a-light-frost-or-ai-winter/

5. Data Transformation
Technical Efforts Segmentation in Data Science
Data Engineering Data Preparation and Analysis Productionization
Modelling and Validation
Data Exploration
Domain Understanding
Insights Gathering
Hypothesis Validation
Feature Engineering
Data Visualization
APIfication
Containerization
Continuous Train & Test
DevOps CI/CD
Monitoring
Data Architecting
Data Acquisition
Building Data Pipeline
Ensuring Reliability
Performance Tuning
Providing DS Infrastructure
Data Discovery Enablement

6. Mistakes
• Professionals & Students are mostly focusing on learning ML,
DL, NLP, while it needs least effort in the entire Data Science
Cycle
• Fastest Growing Technical Ecosystem (Software, Tools,
Techniques and Practices) without Standardization
• Reusability of efforts spent is lacking

7. Mistakes: Data Infrastructure Sharing
• Businesses have Data Science Infrastructure, which is for
internal DS team
• Rarely, it is open for one IT vendor
• Cloud Data Science Infrastructure Providers’ Profitability is
more, due to data infrastructure redundancy and often leads
to huge waste of resources
• Need for Data Mesh

8. Mistakes: “My Precious” Data
• Businesses won’t share data, easily. So, no way for “Open-
Data”, unless Governments mandate it.
• Data Science Projects won’t succeed without using external
data
• Data Vendors’ Profitability is more
• Data Monetization is not done, due to lack of trust and
visibility

9. Mistakes: If Data = Oil, then, from Power
Perspective

10. Mistakes: The Silent “Linked Data”
• Social Media and Tech Giants
• Cloud Providers with Admin Access
• Blockchain Systems connects global business data together
“Artificial General Super Intelligence Powered By Tech Giants”
- Safe AI or Dystopic Future?

11. The Vision: A Platform
• Serves as Global Data Hub for Global Linked Data
• Anybody with access Can Peek & Work, Cannot Sneak and Steal
• Data Science for Digital Nomads and Telecommuters
• Hyper Data Monetization by Businesses
• Data Control and Tracking
• Nano-Payments for Outcomes
• Data Science Effort Reutilization and Transfer Learning
• A Safe Artificial Super Intelligence (ASI) Powered Global Auto Governance

12. The Virtual Glove Box
Platform
For Global Data Science Efforts, Tracking,
Monetization and Safe AI Governance

13. Safe ASI
• According to wiki, glovebox (or glove box) is a sealed
container that is designed to allow one to manipulate objects
where a separate atmosphere is desired.
• We need a virtual glove box for ASI Initiatives
• We can accelerate ASI Development through this Platform

14. Vision Enabler 1: Data
Mesh
https://www.slideshare.net/ManojKumarR41/data-mesh-212917511
https://martinfowler.com/articles/data-monolith-to-mesh.html
https://fast.wistia.net/embed/iframe/vys2juvzc3?videoFoam

15. Vision Enabler 2 : Data Trajectories
http://www.ijdc.net/article/view/11.1.1/419
If Data = Oil,
then, where
are the
refineries?

16. Vision Enabler 3: Hash
Graph
https://www.swirlds.com/downloads/SWIRLDS-TR-2016-02.pdf
https://www.hedera.com/hh-whitepaper-v2.0-17Sep19.pdf

17. Vision Enabler 4:
BigPrivacy from Anonos
https://www.anonos.com/ : Anonos technology is ”cool” because it enables the
creation of re-linkable non-identifying privacy-enhanced data called Variant
Twins that enable lawful analytics, AI, ML, data sharing and combining.

18. Vision Enabler 5: Citrix HDX
https://www.citrix.com/en-in/digital-workspace/hdx/

19. Combination of these 5 and few other ideas will
ultimately lead us to the VGB Platform. Will soon
come up with other document explaining the
vision and how exactly work on the vision to
gradually develop this Platform, which fixes Data
Science Efforts Globally and also accelerates
ASI Development.

20. To Be Continued…

21. I thank all the ideators, inventors,
companies, who come up with
these awesome enablers.
About me: https://www.linkedin.com/in/manoj-kumar-r-427b0b195/