Data engineers, data scientists and data analysts are the key players in a data science team. Though all roles are equally important in extracting insights from data, data analysts don’t have the same status as the other two.
“I think data science has become some sort of a cool term for job seekers. There is also word doing the rounds that the job is very lucrative and that is what drives people more towards it compared to a data analyst role,”said Ayan Basak, Data Scientist II at Snapdeal.
Subscribe to our Newsletter
Join our editors every weekday evening as they steer you through the most significant news of the day, introduce you to fresh perspectives, and provide unexpected moments of joy
Who is a data analyst
Any analytics project starts with raw data. The core functions of a data analyst include:
- Mining data from primary and secondary sources
- Understanding and interpreting data to solve business problems with the help of statistical tools
- Cleaning data to remove noise
- Using the insights deduced from the data to prepare reports to aid in decision-making.
A data analyst must have the following skills:
- SQL, advanced Excel,
- Data mining and data cleaning
- Knowledge of visualisation tools like Tableau, PowerBI
- Making summary reports
- Understanding trends and patterns
Not the third wheel
“Data analysts are pivotal in achieving operational excellence. Their ability to skim through vast amounts of data and generate business relevant KPIs, find meaningful insights and ensure that the organisation gets its numbers right is extremely important. The amount of accuracy with which these guys need to perform is extremely tiring to even imagine some times. Without these analysts it would be like finding a pearl in a vast ocean of data. Their ability to build a foundation layer for various entities in the organisation empowers them to create disproportionate value in the organisation,” said Dipesh Lakhotia, head of analytics, Britannia Industries Limited.
A data scientist can build models once the raw data has been processed into a structured form, removing extreme values, cleaned and ready for modelling.
“Data analyst is subset or right hand of data scientist and without them data scientist is helpless, hence the famous line “garbage in, garbage out”, because models are dumb”. Therefore, without data analyst capabilities you can not become a good data scientist,” said Sachin Birla, data scientist at EY.
In many analytics teams, data analysts have to interact with the clients to understand their problems. In a business setting, understanding how the data impacts the business and how models can improve efficiency is the end game “A data analyst can provide insights regarding what action to take next. They can diagnose issues in a business by analysing previous trends in the data, and thus bring forth millions in revenue,”said Ayan.
In response to Zach’s post on LinkedIn, Lambrie Steyn, a data architect at Decision Inc wrote: “In my experience it’s the most underrated leg in the data team. Many times great technical solutions are architected and engineered, just to miss the true business value / reason for its existence in the first place. Data analysis should be involved in every step of a data journey to ensure maximum business value is delivered.”
“Data analysts are critical for the business. Yes, you can move toward data science but there will always be room and a need for analytics teams,” said Irina Summey, senior analyst, marketing analytics and insights, Thermo Fisher Scientific.
Data analysts should be fluent in tools like SQL, Advanced Excel, visualisation and are expected to know R or Python and have business skills.
“It is sometimes the fault of the leaders today who aren’t able to build a career road map at times for this skill set and also fail to attribute success to their perseverance. This inturn has led to a market sentiment which may be negative towards the analyst roles. That being said, there are still enough opportunities for someone to grab them with both hands and build a fantastic strategic career over time. An analyst with years of experience not only develops expertise over data but also develops tremendous business/ market acumen which enables them to support in far more strategic conversations,” said Lakhotia.