What’s the most challenging part of being a data analyst?

No occupation comes with an entirely good side; even entrepreneurs are often messed up by one business misfortune or another. Data analysis is one of the very notable careers that are very sensitive, detailed and demanding and as such attracts a lot of challenges to itself. This article however in a bid to examine challenges faced by data analysts also points out the most challenging part of being a data analyst.

From data gathering to data presentation and analysis, a data analyst is saddled with the duty of presenting an objective research summary and conclusion no matter how much time and resources it will take. So amidst all the troubles the following are the most challenging part of being a data analyst.

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Big data management  

One of the challenging things that data analyst have to worry about is how to manage big data. It is easier to conduct a research with a manageable number of data but what would you do when even the sample data does not measure up to the actual research population because it’s very large? That’s a big challenge in data analysis.


 I met a priest who narrated how he spent over five years doing his master’s degree just because he experienced some delay in realizing considerable outcome in his data analysis. For data analyst, this kind of delay can limit timely delivery of information for further purposes.

Availability of useful information

The job of data analysts does not make room for waste of data no matter how insignificant they may seem; data must be analyzed or tested to ascertain its usefulness. This is a challenge for data analyst because it takes time and effort which should have been used to further the data analysis.

Data Assumptions

In data analysis, you must take cognizance of other factors that may influence your research data; that’s not the issue here. The issue is the inability to specify exactly how many they are; so it is possible to fix a lower quantity a or one that is excess.


Poor availability of financial resources needed to acquire materials for efficient data analysis is an issue in the industry; many times, it can delay or slow down data analysis procedures.  

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