Integration & IT Modernization

Data Analyst versus Data Scientist

Is Data Scientist a Data Analyst OR vice-versa? I googled this and got more than a 1000 websites that provides this differentiation. So my thought was to consolidate everything I learnt for people who choose their career path in one or the other. If you check Gartner’s Hype Cycle of Emerging Technology, the peak of inflated expectations belongs to “Internet of Things” and more data collection activities such as Machine Learning and Wearable. In addition, the big data paradigm contributes more through social media including Facebook, Twitter, YouTube and many more.

Gartner Hype Cycle for Emerging Technologies 2015

Covid 19
COVID-19: Digital Insights For Enterprise Action

Access Perficient’s latest insights into how you can leverage digital technologies to not only respond to the pandemic, but drive your operations forward and deliver experiences your customers need.

Get Informed

So what is the difference between the data scientist and data analyst? A data scientist converts volume into value and data analyst checks for the viability and visualization of the value by breaking them down into smaller topics. That doesn’t mean that you need 2 different resources to be budgeted for. These are shift in behaviors of the 2 roles. Hence a data scientist is focused on researching data anomalies and used cases from complex, unstructured data elements. However, a data analyst has a time and scope bound topic that they break down into components with meaningful objective.

I saw a nice diagram that explains the differences and overlaps between data science and data analytics here.

Here are some of the differences between the two roles:

  • Data Analyst provides a representation of what happened versus a Data Scientist provides a prediction of what is going to happen
  • Data Analyst has an objective of analyzing data which is typically time and scope driven versus a Data Scientist has an objective of predicting incidents that may not be limited to a certain scope
  • Data Analysts are more business focused and Data Scientists are more statistics focused
  • Data Analysts may use SQL programming versus a Data Scientists may use complex statistical tools such as R programming and Predictive modeling
  • Data Analysts are typically BI Developers, SQL Analysts, BI Analysts, and Operations teams versus Data Scientists are typically Data mining SME’s, Statisticians, and R&D Experts.

In summary, Data Analysts are responsible for summarizing current state using past and present data versus Data Scientists are responsible for forecasting insights using past and current data. However, the commonalities between both involve data aspects such as data governance, data quality, data preparation, data modeling, and analytics skills. Both roles are very critical for information based decision-making process.

About the Author

Arvind Murali is the Chief Data Strategist for Data Governance with Perficient. His role includes defining data strategy and governance to deliver transformative data platforms. Arvind has served as an executive advisor for data strategy and governance to organizations across several industries. Arvind’s dedication to solving challenges and identifying new opportunities has provided valuable business-focused results for clients, such as providing self-service access to data for global sales teams; helping physicians create informed wellness plans; and delivering insights about current supply chain inventories. He is a passionate Vlogger on YouTube and discusses real-world insights, data platform trends, and the importance of governance as big data continues its exponential growth.

More from this Author

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Subscribe to the Weekly Blog Digest:

Sign Up