Challenges Faced by Data Scientists

Data Scientists are a very reputed personality for any company. They are considered as the people who can redirect the company to the path of success. They have to analyse a humongous amount of data to solve the various business problems. It is a very lucrative job that pays well. But, besides being so luring to people there are various challenges that data scientists have to face every day. They have to tackle all those challenges and still keep going. Some of the common challenges faced by data scientist are:

  • Gain Access to Right Kind of Data

Data scientists have to encounter a variety of data. These data are of varying volumes and types. To segregate the data and use the required data is one of the tedious challenges faced by data scientists. Sometimes, they need access to those data that belong to different business domains. So, to collate all the correct data and work on the business plan is one of the challenges faced by them.

  • Vulnerable Security of Data

Data has to be collected through various channels. These channels have interconnectivity to various other nodes. This puts the security of the system to stake. Confidential documents can be easily hacked when the security of the system is not up to the mark. It also poses a problem when data scientists need to access some confidential data for their analysis.

  • Communicating the Ideas to Non-Tech People

Data scientists are always surrounded by tech people. They have to deal with the technical terms all the time. But, when it comes to communicating their idea to the other executives and stakeholders, they have to express in layman terms. If they won’t be able to express their ideas to them then their hard work will go in vain.

  • Clear Idea of Business Problem

Data scientists must understand the problem clearly before going to the solution. Most of the time it has been observed that they follow the same mechanical approach for solving business issues. It is always better that they should define the business objectives and goals to have better performance.

  • Undefined KPIs and Metrics

Management teams tend to have unrealistic expectations from data scientists. This not only builds up the pressure upon them but also deteriorates their performance. There should be proper KPIs so that the impact of data analysis can be examined on the business proficiently. There should be well-defined goals and metrics so that the authenticity of the analysis made by the data scientists can be viewed.

        Data scientists are one of the most respectable individuals considered in a company. Despite all the hurdles faced by them in their jobs, they still manage to conquer them all. With their right skills and analysis, they define various data models and algorithms that can benefit businesses. They should have a clear objective and business goal to proceed with. Moreover, it would be of great help if they can get some filtered data. This will not only save the effort but it will also cut down on the overall cost. So, Data Scientists can be of great asset to the company.