Becoming a data scientist is all about finding useful insights and implementing them in company policies. As important as these moves may be, they are not without challenges Data gathered from research has shown that most of these data scientist are either battling with dirty data, lack of management support or lack of data science talent. This conclusion was derived from Kaggle 2017 State of Data science and Machine learning survey which sampled the opinions of 16,000 data professionals. Based on their findings, the most common issues arising from data science include the following;
- Dirty data:
This can be described as inconsistent or incomplete data, especially when it has to do with a database or computer system. They also contain lots of mistakes like punctuations and spelling errors, which may eventually affect the outcome of the result obtained from the analysis. The best way to take care of this error is to use data cleaning.
- Lack of data science talent:
Data science talents are not readily available to take on the available jobs, thus increasing the workload on the few available and competent talents, thereby affecting the results obtained.
- Company politics:
Some company policies may hinder data scientists from discharging their duties. These company policies may withhold vital information and data from the data scientist, thus limiting the resources available for use during work.
- Lack of clear questions:
Asking the wrong question will only lead to obtaining the wrong data and processing the wrong results. For a data scientist to be successful, there is a need to direct the right question at the right individuals.
- Data inaccessible:
Data to be used for analysis may be inaccessible to data scientist. This could limit their influence in the market and also reduce the quality of results available for use.
- Results not used by decision makers:
It is one thing to utilize data science skills but entirely a different ball game to use the recommendations made by the scientist. Most often than not, decision makers may likely refuse to use results obtained from data analysis, thus serving as a barrier and source of discouragement to the scientist.
- Privacy issues:
Privacy may limit the amount of data available for use during analysis. The lesser the amount of data available for use during analysis, the more confined and biased the result could be.
- Lack of domain expertise:
An inability to acquire the right domain knowledge may deal a great blow on the final results of the data scientist. In some cases, it may even make the results unusable.
- The organization is small and cannot afford to pay a data science team:
The size of the organization may not be large enough to afford the services of a professional data scientist, and this may limit the operation of these experts to the big and rich firms who have some extra cash to invest in knowing how their data is faring.
Data professionals are not immune to challenges, and they also face one problem or the other while discharging their duties with the most common being dirty data, lack of data science talent, lack of management support and inability to ask the right questions. Kindly contact us if you have any questions on data science talent issues, we will love to hear from you.