Ways To Address Challenges Surrounding Data Science

According to a Software firm Anaconda, data science has been facing some instability now, and it is yet to overcome innumerable problems.

 

It can be a bit challenging to pursue a career in data science industry, nevertheless, lucrative. There is a manifold of demand for human resources in this field.

 

Let's explore some challenges that data science faces, and how to evade it.

 

1) Identifying the value of Data Science

 

 

One of the problems of Data Science Companies is to determine the order of the task that needs to be accomplished first in the current situation, absence of organization skills required to put in place the production models, and security issues.

 

According to the study, an estimated 52% of Data Scientists say that they are facing issues with displaying the amount of impact Data science places on Business outcomes. The Health care data professionals are having the most trouble in demonstrating the benefits, 66% of them saying they can barely or never do so.

 

It has become increasingly vital to get the Data Science outputs into production, requiring leader and Data Scientist pros to get rid of the bottlenecks to deployment, thus helping professionals convey the significance of their roles.

 

2) Problems with Open-Source Data Science Tools

 

As per a report, Python, an open-source programming language, is the most useful amongst the Data scientists, 75%, to be exact. These Professionals use this tool most of the time at their workplace.

 

Despite the recognition of open-source programming language amongst the Data Science companies, 30% of the professionals said they are doing nothing to fix their open-source pipeline. When asked, these professionals said that they prefer the open-source tools for their work because it is innovating quicker and meets their needs. As per the Anaconda report, the security issues represent that Data Science companies are slow to embrace these open-source tools.

 

Data Science companies must take a bold approach that combines the open-source Programming tools into the development pipeline. This will prevent the professionals from using it outside their policy confinements.

 

3) Trouble with the Right Professionals

 

There are many issues to analyze here. Firstly, as per the report, Data Science companies do not require the information that the students learn in classes, and the teacher imparts them.

 

Another is that the two most often cited skill gaps by enterprises i.e., big data management and engineering skills - didn't even rank within the prime ten skills that universities hand out the Data Science students.

 

The next aspect of dealing with is Talent retention. The report shows that how frequently do Data Scientist pros are eligible to demonstrate the significance of their work. Almost 44% of professionals said they would change their jobs shortly.

 

There are three recommendations as per Anaconda, the manufacturer of Python.

 

Data Science companies must affiliate with the educational institutes to ensure that the students get imparted the knowledge these enterprises demand from them.

 

The management team must develop a holistic approach for their employees to support them emotionally, mentally, and physically. They should be vocal about the significance of their work in a positive direction and also provide career growth opportunities via proper programs and training sessions in place.

 

Data Science Companies must look towards cross-training sessions for their employees to increase the value of their work.

 

4) Data Cleansing

 

Big data is assumed to be a bit costly for revenue creation because data cleansing poses as trouble to operating expenses. It can engender a big-time problem for the Data Scientist pros to work with the inconsistent databases and glitch, which might lead to unwanted results. They would have to clear the data's inconsistencies, which can take a massive amount of time and energy.

 

Data Scientist pros can resolve these issues by using proper tools to improve their overall accuracy and data formatting. The goal of the Data Science companies must be to accumulate a good-quality data.

 

5) Correct Data

 

 

It is incredibly crucial to get the correct data, which can be a bit time-consuming since you need to access it in the most suitable format. There can be innumerable problems starting from the concealed data, inadequate data to less diversified data. It can be quite challenging to get a permit to access the data from many such enterprises.

 

The role of the Data Scientists professionals is to manage the Data Management System. The software named Stream analytics, used for data filtering, allows these professionals to connect the external data sources and arrange them in a proper flow.

 

6) Removing the Bias and Gain clarity with Machine Learning

 

According to the report, addressing the bias and discrimination has made slow progress as compared with other trends. The most concerning one is to explain the machine learning.

 

The problems revolving around ethics, fairness, and responsibility have started to rise from machine learning and artificial intelligence. As per Anaconda, the enterprises should make sure to treat ethics and integrity as a strategic risk vector, and treat them with equal observation and care.

 

Despite emphasizing addressing bias present in machine learning models and Data Science, the same isn't happening. As per respondents, only 15% had implemented the bias elimination solution, and 19% for explainability.

 

Moreover, when surveyed, 39% of the Data Science companies said that they had no plan of action to discuss bias in Data Science and machine learning, and 27% of the enterprises said that they have no plans to make the process more understood.

 

Anaconda reports says that a failure to addressing these problem areas can be of significant concern in the future to all the dimensions including, financial and legal.

 

Final words:

 

There is a strong recommendation that Data Scientist pros become leaders and drive change in their enterprises. This will not only increase the discipline structure of the enterprise, but also engender innovation and problem-solving. The Data Science Industry is fast-paced, and it can get challenging to keep up. However, a career in this industry needs the expertise to understand how to meet with the demands of the industries.