The Biggest Challenges Data Scientists Face
by Sumona Job & Career 22 March 2022
Data is considered new electricity that bought the fourth industrial revolution. In the era of Artificial Intelligence and Big Data, data is considered to be the heart of any business or industry. Around 2.5 exabytes of data is considered to be created each day.
With the tremendous increase in data, many companies have centered their business on data. With the advancement in technology and data, the decision-making process in any sector has been simplified and more accurate and precise results are being produced.
Data science helps create accurate and precise results and make a correct decision in any firm utilizing the raw data. A skilled data scientist focuses on analyzing and managing the data using a variety of tools and technology.
A data scientist with great problem-solving skills is assigned to find patterns in data and draw insights from them. Hence, any firm needs data science and data scientists to adopt appropriate strategies and plans to, in turn, make better data-driven decisions. For example, e-commerce websites use personal data to assist their customers and enhance customer experience.
6 Biggest Challenges in Data Science and Data Scientists Face:
1. Challenges in Data Science
In data science, using the ML model benefits as a service is considered the hardest part and probably least well-understood. However, with the increase in data science and big data adaptation, other challenges except using the ML model as a service have also increased.
For building the right model and taking the model into production, good quality of data and a clear vision of the project or problem statement are required. Some of the key challenges in data science are:
- Lack of reliable data
- Data cleaning and understanding
- Data security
- Unclear vision and problem statement
2. Lack of Reliable data
The first step on any data science project is to find reliable data to start working. However, getting reliable data to work is a common challenge faced by any data scientist. Some of the factors that affect getting reliable data are highlighted below.
First, due to fear of losing key insights, the company stores all the data without considering whether all of them are useful or not, making it harder for data scientists to analyze it. Secondly, many data are stored in multiple sources, which makes it harder to analyze the insights and combine them from multiple sources.
3. Data Cleaning and Understanding
The database is usually designed by people other than a data scientist to store data. Once a data scientist gets access to those data, he is unaware of the jargon in the database. To understand the data, a data scientist has to have a great knowledge of the domain, which takes a lot of time. So, understanding domain-specific data is tedious, which makes it challenging.
Also, the real-life data are messier or contain more information than required; hence data scientist spends most of their time cleaning and processing the data. In order to build a high-quality model, data plays a major role, so ensuring high-quality data is a tedious task.
4. Data Security7
Data is one of the important assets of any company which can improve any business performance. However, data security has been one of the main concerns in data science. Security issues make it difficult for the data scientist to explore any tools or technology to process data.
As data is not only the asset of the company but also contains sensitive information about customers or financial information, companies need to follow some of the fundamentals of data security, such as confidentiality, integrity, accessibility.
5. Unclear Problem Statement or Vision
Any problem statement or vision should have a clear, quantifiable goal. But it is always difficult to have a clear problem statement because the stakeholders do not have a clear vision of what they want. They do not have a clear end goal for data scientists initially; however, they make amendments in their requirements or goals as the analysis progresses because they now get some idea about the problem they want to solve.
6. The Final Challenge
In this era of artificial intelligence and big data, every company is trying to adapt to the change in markets needs and leverage data science to meet those changes. However, there are various challenges faced that hinder the development.
But well planned and organized workflow can help exscind or address these difficulties and meet the problem requirement. So the issues discussed above can be resolved by having a clear problem statement and a proper understanding of the data.
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