Data Science – Barriers and Challenges, there is no doubt in the fact that data science has really influenced everything we do these days. From technology to automation, everything is due to data science. But some challenges, and problems are faced by data science which were supposed to be discussed to find out the right solutions and correctly implement them and the most efficient way possible. Hence in this article, we will discuss some of the common problems faced by data scientists in the various domains of data science.

Problem Identification  – Data Science

Most of the time problem identification is the major issue to curb the challenges in business. Many times it is seen that data scientists are not able to catch the crux of the problem and identify the issue it deals with. The job of data scientists is not only to understand the data but make it more readable and understandable for the users. There is a lot of software that data scientists can use in order to make data more readable and understandable by using visual aids. 

These problems if not discussed or brought in front of the audience may cause confusion in the future aspirants who want to be data scientists. These problems may be related to the data, the machine, or the computer itself and sometimes even with the security of the data of the users.

Big Data is so big that it makes it difficult to analyse. For instance, cardholder data should be managed in a highly secured data vault, using multiple encryption keys with split knowledge and dual/triple control. Big data also presents a tremendous opportunity for enterprises across multiple industries especially in the tsunami-like data flow industries i.e. Payments and Social media.

Maintaining the quality of data

We know that in various fields of data science like artificial intelligence machine learning, we are supposed to feed the computer or the equivalent machine with data which it has to carefully analyze and then figure out the patterns in the data and then apply similar concepts in unsolved problems.

But if the data is not clean, then various problems may arise. If there is a lack of good quality data, the computer or the machine would not be able to learn properly and then we will get inaccurate results. 

To leverage data from different platforms i.e. CRM platforms, spreadsheets, enterprise planning systems, social media feeds like Facebook, twitter, Instagram, LinkedIn, company website feed section, any video file, and any other source. Thanks to mobile devices, tracking systems, RFID, sensor networks, Internet searches, automated record keeping, video archives,   e-commerce, etc. -coupled with more information derived by analyzing all this information, which on its own creates another enormous data set.

Data Science – Handling large amounts of data

The problems which are more complex require more amount of data to be studied well. So if there is too much data, then it becomes very difficult to handle such a large amount of multidimensional data. There are a lot of problems associated with large chunks of data. It becomes very difficult to analyze and organize large amounts of data.

It is difficult to represent large amounts of data as visual aids like charts, tables, and graphs. Apart from it, there are some more serious problems associated with large amounts of data. It is very difficult to ensure the security of such a large amount of data, which can compromise with the privacy and security of the users and their data.

Today’s data has an answer for most of the things if not everything. Data of today can be quantified and tracked easily as it has human elements. What this means –

  • What will the temperature be next day or week or even one a particular day in the future?
  • How my follower’s trend may look like in the next 3 months?
  • How the health of the person would be based on data and the environment?
  • How many sales will be next month?

“For telecom service providers The change or shift (in data volume) that 5G and subsequent network generations will bring will not be the same as from 3G to 4G”. The powerful combination of mobile data, artificial intelligence, and machine learning algorithms will become the key driver for exponential growth in data usage. 

Lack of Skilled Personnel

This is one of the biggest problems faced by data science, so it is worth reiterating. This constitutes more than 30% of the problems faced by data scientists. There are not enough experts who have good knowledge of programming and mathematics or even business analytics that can prove useful for data science.

Points to Note:

All credits if any remain on the original contributor only. The guest author has covered all the basics around data science challenges. Machine Learning is all about data, computing power, and algorithms to look for information. How machines can do more than just translation is covered in Generative Adversarial Networks. A family of artificial neural networks.

Feedback & Further Question

Do you have any questions about Supervised Learning or Machine Learning? Leave a comment or ask your question via email. Will try my best to answer it.

======================== This is a Guest Post =================================

Tejeswini Teju – Is the author of this post and all content ownership and responsibility is on her. AILabPage has done proofreading and minor editorial changes, Her efforts eventually enhance companies’ shape by delivering stupendous solutions to their business problems.

If you also want to be a data scientist, enroll yourself in the best data science online training course in 360DigiTMG, where you will get the best possible training and facilities so that you can achieve your goal.

Thank you all, for spending your time reading this post. Please share your feedback / comments / critics / agreements or disagreement.  Remark for more details about posts, subjects and relevance please read the disclaimer.




Posted by V Sharma

A Technology Specialist boasting 22+ years of exposure to Fintech, Insuretech, and Investtech with proficiency in Data Science, Advanced Analytics, AI (Machine Learning, Neural Networks, Deep Learning), and Blockchain (Trust Assessment, Tokenization, Digital Assets). Demonstrated effectiveness in Mobile Financial Services (Cross Border Remittances, Mobile Money, Mobile Banking, Payments), IT Service Management, Software Engineering, and Mobile Telecom (Mobile Data, Billing, Prepaid Charging Services). Proven success in launching start-ups and new business units - domestically and internationally - with hands-on exposure to engineering and business strategy. "A fervent Physics enthusiast with a self-proclaimed avocation for photography" in my spare time.


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