Tech

Challenges in the World of Data Science

There is no limit to what can be achieved through data science. Unlimited opportunities, revolutionary and indispensable are some of the words which you can always find while reading about data science. Not only it is one of the greatest innovations of the technical world but also one of the best fields for a career. The career path in Data Science is so lucrative that almost every tech-professional wants to upskill with data science and fresh graduates want to enroll in the best data science training institute in Bangalore, Delhi, Hyderabad, and other A1 Indian cities.

While Data Science is indeed lucrative both for a businessman and for a student, it is important to also understand that the field is not without its fair share of challenges and hurdles. Industries around the world are facing a lot of challenges to fully realize data science capabilities, data scientists are meeting roadblocks every day, and data science aspirants are also getting disillusioned. Some even go as far as terming the field over-hyped and over-estimated out of sheer frustration and disappointment!

Let us look at some of those challenges:

Businesses unable to unlock the full potential of data science

Several business leaders around the world have been complaining that they are not seeing the profit out of data science even after investing a lot to build a team. Some even consider that companies are investing in data science just because it is trending at the moment and it looks ‘cool’ to have a data science team onboard. There are even others who think that data scientists will only conduct research that might add value in the long run but have no immediate positive impact on the business!

However, data scientists and experts in the field have correctly pointed out that the emerging pessimism cannot be attributed to the failure of data science but is a result of restlessness, incorrect expectations, mismanagement, and gross inaccuracies in measuring the value of data science.

Data science is no magic that can make a failing business successful overnight. It takes time and it is very important to manage our expectations from the beginning. Churning out deep insights from a data pool by employing a highly skilled data science team is something that has been successfully done by multiple businesses all over the world. Reading through the various success stories we can easily find how much patience one needs and how to manage the expectations.

There has also existed a human resource problem for quite some time now. Not only there is a lack of employable professionals in the field but also there are multiple below-standard institutes that fail to train their students effectively. In countries like India, we do need to have more options like the AnalytixLabs who has been highly successful in providing good quality training for promising data science aspirants.

Challenges faced by data professionals

Just like any other professionals of complicated fields, Data scientists face daily challenges too. This includes finding the right kind of data, getting access to the data legally, cleaning the data, and then translating the results in layman’s terms so that every stakeholder can understand.

Yes, data is abundant and with the ever-increasing trend of digitalization, data is being generated and captured at astonishing speeds and volumes. However, not all data is relevant to a business, and finding the relevant data can become a problematic or time-consuming affair. Data scientists need to find out and use innovative and unique data ingestion techniques regularly.

Increasing concern for data security and the implementation of harsher data security laws have also created hurdles for data scientists. Data scientists are also required to figure out approaches to access the right kind of data while abiding by all the rules and guidelines implemented by authorities. While there are various data cleaning techniques that are both effective and time-saving yet while creating data cleaning models, a data scientist needs to avoid data bias which might seep into the algorithms quite unknowingly!

Another challenge for data scientists is to convey what they have achieved or found in common language to other teams. This can be quite a challenge but skilled data scientists can translate the data science jargon into layman’s terms by the use of data visualizations.

Hence, data science is not without challenges or difficulties. However, this does not render data science useless. Data scientists are constantly coming up with innovative techniques to deal with such challenges and every day we are witnessing progress!

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