Data Science in the Real World5 Real-Time Challenges Faced by Data Science Industry and How to Combat ItData Science Industry Still Facing These ChallengesStephanie DonaholeBlockedUnblockFollowFollowingJun 17Are you thinking to pursue a career in the data industry?Well, data is a lucrative field to pursue as they are plenty of demand for people with similar skills.
In this business arena.
data scientists are deemed to possess some superhuman powers as they wade across tones of data and come up with a solution for solving business problems.
There exists no career without any challenges and how can data be an exception to this.
In this article, we want to explore the real-time challenges of data science which are based on perspectives from those experts in the field.
Problem-IdentificationOne of the major concern in analyzing a problem is to identify it accurately for designing a better solution and defining each aspect of it.
We have seen data scientists to try mechanical approach by beginning their work on data and tools without getting a clear understanding of the business requirement from the client.
How to Resolve it?There should be a well-defined workflow before starting off with the analysis of the data.
Therefore, as a first step, you need to identify the problem very well to design a proper solution and build a checklist to tick off as you analyze the results.
Accessing the Right DataIt is vital to approach your hands on the right kind of data for the right analysis which can be a little time consuming as you need to access the data in the most proper format.
There might be issues ranging from hidden data and insufficient data volume to less data variety.
It is also a kind of challenge to gain permission for accessing the data from various business.
How to Resolve it?Data scientists are expected to manage the data management system and other information integration tools such as Stream analytics software which is used for data filtering and aggregation.
The software allows to connect all the external data sources and sync them in the proper workflow.
Cleansing of the Data[Image Source]Big data is estimated to be a little expensive for generating more revenue because data cleansing is making troubles to operating expenses.
It can be a nightmare for every data scientist to work with the databases which are full of inconsistencies and anomalies as unwanted data leads to unwanted results.
Here, they work with tons of data and spend a huge amount of time in sanitizing the data before analyzing.
How to Resolve it?Data scientists make use of data governance tools for improving their overall accuracy and data formatting.
Addition to this, maintaining a data quality should be everyone’s goals and businesses need to function across the enterprise benefit from good quality data.
Bad data can result in a big enterprise issue.
Lack of ProfessionalsIt is one of the biggest misconceptions to expect that the data scientists are good at high-end tools and mechanism.
But they too need to have possessed a piece of sound knowledge and gain subject depth.
Data scientists are considered as bridging the gap between the IT department and top management as domain expertise is required for conveying the needs of the business to the IT department and vice Versa.
How to Resolve it?To resolve this, data scientists need to get more useful insights from businesses in order to understand the problem and work accordingly by modeling the solutions.
They also need to focus on the requirement of the businesses by mastering statistical and technical tools.
The Road AheadIn reality, being a data scientist requires the implementation of results by making use of refined data and practical applications.
The data world is a difficult and fast challenge.
However, a career in the data industry is not only based on experts but it is based on being an expert who understands how to fit the demands of industries.
Keep Learning!Author BioStephanie Donahole is working as a Business Analyst at Tatvasoft Australia, a web development company in Australia.
Her aim is to sharpen her analytical skills, deepening her data understanding and broaden her business knowledge in these years of her career.
She loves to write about technology innovation and emergence.
Follow Her on Twitter.