Artificial Intelligence (AI), Data Science, and Analytics make the world a better place and we know that!
DATA SCIENCE EXPERTS- “The world runs on Data” a common adage we all would have heard, but have you ever thought how this data is churned to intelligent insights and strategic action? The megabytes of data we generate is tough to handle, even the data analysts would admit to this! A recent survey of over 5000 data professionals revealed interesting results, the most common challenges faced by them revolve around dirty data, lack of data science talent, and management support.
It is well said that data science is all about digging to intelligent insights and devising a plan to put that data into strategic use to accelerate business growth. Data science, however, doesn’t occur in isolation.
Here are the challenges that data science professionals confront in the modern-day business.
1. Uncleaned data
2. Data ownership issues
3. Live vs historical data selection
4. Eliminating data bias
5. Inaccessibility of data
6. Lack of problem statement
7. Technical complexity
8. Explaining data synergies to the management9.
9. Data privacy concerns
10. Lack of domain expertise
11. Multiple Ad-hoc development platforms used for the same project
12. IT/ management team co-ordination
13. Inability to integrate findings to the C-Suite and decision-makers
14. Lack of a skilled workforce
15. Results not used by management
16. Deployment and scale-up hassles
17. Expectations and model performance accuracy gap
18. Ageing legacy infrastructure
19. Data governance
20. Budgets and spending made on data science platform and tools
Gone are the days when the availability of data was restricted. The modern dynamic world calls in for the age of Big Data, characterised by volume, variety, velocity and veracity. Data scientists work with the particularly tough terabytes of unstructured data generated from a multitude of sources. The voluminous nature of this data, when pushed to traditional systems, results in sheer incapability of data handling.