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According to a recent estimate, almost 700,000 megabytes of data were created every minute in 2021. This incomprehensible tsunami of data has created an increased demand for data scientists and data analysts who can look at, comprehend and provide useful insights based on the information.
These roles have become so pivotal, in fact, that Glassdoor included them in its Top 50 Best Jobs in America list, coming in at #2 and #35, respectively.
It might be tempting for leaders who are starting their companies’ data journeys to cut corners by choosing either a data scientist or an analyst. In reality, both are necessary to achieve data nirvana because they have more differences than similarities. Let’s examine five major differences between data scientists and data analysts.
Also see: Best Data Analytics Tools
1) Data Analysts vs. Data Scientists: Use of Data
Data scientists are usually the first employees to look at data sets and can be thought of as their decoders. The data sets they work with are large, poorly structured, and require the data scientist to perform abstract analysis tasks such as sorting through it to find “signals” or previously unknown insights in the data.
Think of data scientists as your do-it-all resource, from wrangling/mining data, cleansing and structuring data, extracting business insights, building machine learning models, to delivering reporting to the business.
After getting a better understanding of their data, they can curate insights that can be put into action by data analysts. Once they receive known data sets with insights, data analysts are commonly tasked with finding trends in the data, creating reports and metrics that answer specific business questions, and communicating to a non-technical audience. Their analyses are used to help understand how a business is running and where there are opportunities for improvement.
2) Data Analysts vs. Data Scientists: Use of Tools
Data scientists need to mine data, perform exploratory data analysis, and build machine learning models. So their tool set includes programming languages such as Python, Java, and R. There are also data science platforms that combine toolsets to speed up the data pipelining and mining processes.
Data analysts have a bit more leeway in terms of the tools they use. Some might set up pivot tables in Microsoft Excel, while others with more technical skills might use SQL queries against source systems. No matter the tools or platforms, the key is that the data sets are known, trusted and can be assembled into actionable business metrics for the C-suite and executive leadership.
Also see: What is Data Visualization
3) Data Analysts vs. Data Scientists: Education and Skills
Data scientists typically have a background in mathematics, programming, engineering, statistics or computer science. Since data scientists often come from a programming background, they typically have many technical skills. These skills all come into play when data scientists are searching for trends and insights the organization is not yet aware of.
Data scientists typically have graduate degrees more often than data analysts do. A data analyst’s educational background generally involves knowledge in quantitative fields such as computer science, statistics and mathematics, but also from specialized business domains.
4) Data Analysts vs. Data Scientists: Who Manages Them
Data scientists are likely to report to the chief data officer. With all the data scientists in an organization acting as a team, different business units may leverage them to provide high-value data sets for their respective data analysts to harvest. One day, a data scientist could be sorting through supply chain data and the next day combing through earnings information.
As suggested above, data analysts often reside within one business area. It is not uncommon to have one data analyst reporting to finance and a different data analyst reporting to sales or marketing, both focused on the metrics and reports that are most important to their business units. This narrow approach allows data analysts to truly become experts in their business area.
5) Data Analysts vs. Data Scientists: Business Value
Because these two roles require differing backgrounds, skills and objectives, it’s natural that they also bring different value to organizations.
For data scientists, their value comes from uncovering undiscovered opportunities in data sets. Data analysts bring value to their organizations by turning these opportunities into actionable insights.
Also see: Real Time Data Management Trends
Data Analysts vs. Data Scientists: the Big Picture
It should be clear by now that data scientists and data analysts are not the same – and that your organization needs both. That’s how you’ll get the most out of your data. To only employ one of these leaves the puzzle unsolved.
You need data scientists to identify new insights, and you need data analysts to give leaders what they need to answer specific business questions. Together, data analysts and data scientists fully use data to complete the picture of what your business could do and be.
About the author
Rex Ahlstrom, CTO and EVP of Growth & Innovation at Syniti.