20 Challenges faced by Data Science Experts for Digital Transformation

by July 2, 2020

Data Science

Artificial Intelligence (AI), Data Science, and Analytics make the world a better place and we know that!

“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.


One Size Does Not Fit all Data

While it is true that data can be used in multiple business problems and data science models, there is no unique model which can fit it all. Data is unique and generated along different datasets so long as business problems are addressed and the data is handled properly, organisations need not worry.

However, problems arise when data is used for niche business problems like a priori algorithm for predicting a superstore’s sales patterns may not apply to predict a different business problem at hand. So, organisations need to develop an agile framework in accessing which model platform to use and in which data environment.


Overcoming Big Data Challenges

Overcoming Data Challenges can be a tough ballgame. Not only for the management but for the Data Science professionals as well. Until we get to the perfect balance, organisations need a potent mix of self-sufficiency, efficiency, time, agility and ease of use to help reduce the burden on their data science team who are working diligently to make sense of the data that is reflective of the paradigm digital transformation shift.

The challenges related to data, and creating data pipelines are directly affected how the right data management environment is chosen. The adaptability to a scalable data governance structure does not only make data more accessible but also synergises the ecosystems where a reduced subset of siloed data is migrated for AI and ML projects.

Data Challenges are tough to overcome, but a dedicated approach to demystifying data silos and data lakes may just do the trick!