5 Differences Between Data Scientists and Data Analysts

An estimated 1.145 trillion MBs of data are created every single day. This data boom has sparked a newfound demand for employees who can sort through, understand and create actionable insights from this information. Enter the roles of the data scientist and data analyst. A decade ago, Harvard Business Review crowned data scientist the “sexiest job of the 21st century.” And last year, data scientist and data analyst both made the Top 50 Best Jobs in America list from Glassdoor, at spots #2 and #35. 

Leaders of organizations just beginning the data journey might think they can 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. 

Responsibilities and Roles  

Data scientists are the decoders of data sets, typically being the first to look at them. 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. After getting a better understanding of their data, they can curate insights that can be put into action by data analysts. 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.

If data scientists are the decoders, then data analysts are the builders. 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. 

To show how these roles work together, take this example. A data scientist may create an algorithm that predicts click-through rates on data collected from digital campaigns. After creating the algorithm, the data scientist uncovers an insight that certain times of the day deliver higher click-through rates. The data scientist would pass this information onto a data analyst, who would create tracking and reporting on actual results with these metrics and insights in mind. 

Whether it’s decoding or building, both roles need to have the highest degree of confidence in the data they’re working with to be successful. That might sound like a given, yet research from last year revealed that only 5 percent of C-suite executives have a high degree of confidence in their data. It’s no wonder that data-specific roles like data scientists and data analysts are so sought after and vital to an organization’s success. 

Tools and Technologies Used

Data scientists often rely on programming tools such as Python, Java, and R. These tools are typically used to mine data, perform exploratory data analysis, and build machine learning models. There are also data science platforms that combine toolsets to speed up the data pipelining and mining processes. 

Data analysts have more options when it comes to tools. Some may prefer to create pivot tables in Microsoft Excel, while others with more technical skills might use SQL queries against source systems. Often, they leverage the tools available to them and that they’re comfortable using. No matter the tools or platforms, the key is that the data sets are known, trusted, and can be assembled into business metrics actionable to the C-suite and executive leadership. 

Educational background and skills required

Since data scientists often come from a programming background, they typically have many technical skills. Their education background is usually in a field like mathematics, programming, engineering, statistics, or computer science. These skills all come into play when data scientists are searching for trends and insights the organization is not yet aware of. 

The educational background of a data analyst can vary, depending on where they report in the organization. Generally, one most likely has knowledge in quantitative fields such as computer science, statistics, and mathematics, but also from specialized business domains. They must be able to have an analytical view of how data can translate to business needs and questions. This also requires having clear communications with executive leadership and explaining complex data in colloquial terms. An analyst that understands what to look for, and the business purpose of a particular analysis task, can shorten the analysis time and improve the accuracy of results. 

Data scientists more often have graduate degrees compared to data analysts. According to Indeed, data scientists have doctorate degrees at nearly 10 times the rate of data analysts, and they’re twice as likely to earn a graduate degree. And while a bachelor’s degree is often expected for entry level roles in either field, there are chances for those without formalized educations in programming and analytics to pursue a career in data science or data analysis. Boot camps, or short, intense programs to quickly teach participants a particular subject, are readily available for both careers.

Teams They Sit On 

Data scientists are most often part of a centralized Center of Excellence (COE), likely reporting into the chief data officer. With all the data scientists in an organization acting as a team, different business units may tap into and leverage them to provide high-value data sets for their respective data analysts to harvest. Sitting on this team rather than having a specific focus means they’re working with data from a variety of business functions. One day, a data scientist could be sorting through supply chain data, and the next day combing through earnings information. 

Unlike centralized data scientist teams, data analysts are often dispersed across the organization and focused on the business area they belong to. For example, it is not uncommon to have one data analyst reporting to finance and a different data analyst reporting to sales or marketing, both honed into 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, using data to optimize processes, procedures, and decision-making within their business unit. 

Value They Bring to Organizations 

Since data scientists have different roles, skills backgrounds, and objectives, it makes sense that they also bring different value to organizations. When it comes to 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.  

To truly optimize and get the maximum potential out of your data, organizations should employ both data scientists and data analysts. Having one without the other leaves part of the job unfinished. Without a data scientist, no new insights would be identified; without a data analyst, decision-makers wouldn’t be able to use data to solve or answer specific business questions. By using their different skills, educational background, and technologies, data scientists and data analysts can come together to achieve one common goal: to make sense of and utilize an organization’s data to the fullest potential. 

Rex Ahlstrom is the CTO and EVP of Growth & Innovation at Syniti, a leader in enterprise data management. Rex has over 30 years of technology industry leadership experience, and specializes in enterprise software within the data integration and information management space. He is also a member of the Forbes Technology Council.