Why One Shouldn’t be Data Scientists? Here are Few Scary Facts.

Here we discuss challenges encountered by Data scientists in their work lives

Data science might be the sexiest job of the 21st century with fat salaries, but that does not mean it is the right career choice for you. Data scientists are employed to analyse and interpret complex digital data, such as statistics of a website, especially to assist a business in its decision-making. Data scientists' occupations include retrieving data, cleaning data, building models, and presenting their findings in business terms.

Officially, the data scientist's job is building predictive models using advanced mathematics, statistics, and various programming tools. Most people go into data science for the adventure it offers. Many organizations have to spread their time between doing technical work and the other, less exciting stuff. The hardest part of a data scientist is not building an accurate model or obtaining good or cleaning data, but defining feasible problems and coming up with reasonable ways of measuring solutions. Data scientists encounter key challenges at each step of their working process.

Here are some of the challenges of data scientists:

Finding the data:

Finding the right data is still the most common challenge of data scientists, directly impacting their ability to build strong models. Do most companies collect tremendous volumes of data without determining whether it is consumable or not?  This makes it harder for data users to find the truly relevant data assets for the business strategy. Data is scattered across multiple sources, making it difficult for data scientists to find the right asset. That's why so many companies use a data warehouse, in which they store the data from all the various sources.

Getting access to the data:

Security and compliance issues are making it harder for data scientists to access datasets. Like confidential data is becoming vulnerable to cyber-attacks, data scientists struggle to get consent to use the data, which drastically slows down their work, worse when they are refused access to a dataset.

Understanding the data:

When data scientists find and obtain access to a specific table, they can finally work their magic and build powerful predictive models. Undocumented assets roam around your business with unproductive data scientists spending 80% of their time trying to figure them out.

Right communication:

Communication is pivotal to forging a successful career for the data scientist. Working closely with the company's decision-makers and maintaining a solid relationship is essential. Always look for an opportunity to solve the business problem or in-house team concerns with a chance for automating redundant tasks or basic data retrieval. Most data science professionals in a company, by default, will be considered analytics and data experts.

Data cleaning:

Data scientists spend most of their time pre-processing data to make it consistent before analyzing it, instead of building meaningful models. Because real-life data is nothing like hackathon data or Kaggle data. It is much messier. This task involves cleaning the data, removing outliers, encoding variables, etc. The worst part of a data scientist's career is data pre-processing, which is crucial because models are built on clean, high-quality data. Otherwise, machine learning models learn the wrong patterns, ultimately leading to wrong predictions.

Communicating with non-technical stakeholders:

Data scientists work is meant to be perfectly aligned with business strategy, the goal of data science is to guide and improve decision-making in organizations. Hence, one of their biggest challenges is to communicate their results to business executives. Data scientists often have a technical background, making it difficult for them to translate their data findings into clear business insights.