By Tarique – Senior Associate at Credforce,
Tags: data science professional, big data analyst
The rise of data science is a core reason for the majority of industries to flourish and attract the kind of success that they had not experienced before. But, as they say, every rose has its thorn. You cannot adopt data science without embracing the challenges that come with it.
Everything has its pros and cons and data science is no different. This article is going throw light on the negative consequences that one is likely to face if they choose to employ data science in their lives. But, before that, let us have a look at how data science is reshaping business and why has it become an essential part of today’s world.
Data Science – How is it important?
Data is surrounding us everywhere we go and this torrent inspired the business realm to capture relevant parts of it to attract success. Data is being produced every second by every second person on this planet. With mobile phones and other devices that support the internet, everybody is producing data and with technology advancing to an unimaginable level, businesses have found ways to capture this data and read it to extract valuable information that can help them enhance their profits.
With millions and millions of Google searches and social media posts, companies have access to the minds of the consumers. And, this is accomplished with assistance from well-read, dexterous data scientists. By seizing large sets of disparate, inconsistent and giving it the right form, data scientists are able to identify hidden patterns, analyze every inch of pertinent information and help organizations in understanding the wider scenery of their business, comprehend the needs and behaviors of their customers, build more effectual strategies and make wiser decisions.
Therefore, the golden age of modern business started with the birth of data science and its subsets.
However, as mention above, like everything else has a downside to it, data science has a group of limitations attached to it. Here are the challenges that the Data Science Industry has to offer.
Data Science Challenges
1. Lack of Data Science skills:
This is the first and foremost problem in connection with data science. Many companies are facing a difficult time as a result of the data science skills gap. The demand for a big data analyst is going off the charts, but alas, is not being met as a consequence of a shortage of people with the right expertise.
Top notch companies are in a profound need for talent that can fulfill the requirements of the transforming business landscape. They are working towards building a team of people who own the right mixture of data science knowledge and business acumen.
2. Getting The Correct Data & Correct Data Sizing:
It is needless to say that for a model to work, it is essential to collect the ‘correct data’. These days, the three core traits of the data are – Volume, Velocity along with variety. So, you can picture how easily things can go wrong with incorrect data in hand.
The ‘big issue’ with ‘big data’ is that it is highly complex and making sense out of it is tough but to crack and organization nowadays are struggling to draw out relevant details out of large data sets to increase their revenues. However, in the case of the data explosion, actionality can get curbed. Thus, for proper data usage, one has to decrease the noise and focus of the apt analytical model.
3. Unification of Information:
The gush of data received by organizations is entirely inconsistent and scattered. Hence, it troublesome to consolidate information for a useful purpose. The mistake that most companies make is paying more attention to internal data. They are not able to develop the capacity of blending the data coming from dissimilar sources, putting them in one line in order to come to an applicable solution.
4. Ensuring that the data is secure and reliable:
Data Analytics requires businesses to handle enormous quantities of data and maintain its privacy. Which is a huge challenge as sustaining the safety of such large amounts of data demands the usage of complex software and hardware. They have to make certain that important data is not used wrongly.