✨ Try our new interactive demo
Data science and data analytics have become hot topics in recent years, especially with the rise of big data and other similarly popular topics. That being said, many don’t understand the distinctions between those important practices and, more importantly, how these two concepts interact. This piece aims to dive into the nuances of these two fields and help clarify any common misconceptions.
Data science is the field of study regarding exploring raw data. Typically, data science practitioners are highly technical individuals with proficiencies in data mining and manipulation via programming, machine learning, and AI. Data science is closely related to computer science and oftentimes is a less structured job, focusing less on gaining a specific, quick insight, but rather answering more abstract questions or even entire business functions. For example, data scientists may work on predicting weather patterns.
Data analytics is a practice focused on answering immediate questions. Data analysts are typically less technical individuals who utilize tools such as business intelligence (BI) tools to gain specific insights. Data analytics is oftentimes done by product managers and founders to gain specific business insights, such as answering questions like ‘what is my drop-off rate in my sign up funnel’.
Data science is focused on broader organization goals by mining data for insights, whereas data analytics is focused on answering more specific questions. Data science is a practice done by highly technical individuals, whereas data analytics is a practice with a broader range of technical aptitudes. Many times, data science is focused on machine learning and pattern recognition technology to gain pattern insights from data via transformation via complex algorithms, whereas data analytics is focused on SQL queries and organizing existing data without material transformation beyond summations, counts, and other commonly used SQL-esque terms. Data analytics is often focused on longer-term objectives, whereas data analytics has a more immediate-term focus.
These are broad generalizations and the exact distinction between these two efforts can often blur, with individuals oftentimes doing both of these functions as part of a project. For example, optimizing a social media content serving algorithm may involve data science of finding statistical significance in certain variables to increase average end user engagement, but would involve data analytics when telling an advertiser how many customers engaged with their content.
There is often a lot of overlap between these two roles. Typically engineers will do data science, whereas data analytics is done by founders, product managers, and non-technical business individuals.
There are a lot of tools that unify data science and data analytics. At their most fundamental level, things like semantic layers, data warehouses, and more work to unify these tools by providing locations for organizing and defining data.
Absolutely! Data science and data analytics requires a lot of data sanitization and organization prior to insights being gathered. LLMs and AI are exceptionally good at providing scaffolding code for defining base elements such as semantic layers, SQL queries, and sanitization activity. LLMs and AI can be seen as accelerants to the overall data science and data analytics process, enabling people to focus on the in-depth analysis and insights aspect as opposed to the tedious yet well-defined sanitization process.
Over time, we hope that LLMs and AI will provide even more scaffolding services, especially for data analytics. LLMs and AI should be able to guess which fields may have predictive power and be able automatically create charts and alerting functionality for data analytics. A human can then provide feedback on the importance of those metrics for LLMs and AI to iterate.
In many ways, this is the promise of an embedded analytics platform, enabling users to quickly gather insights with the help of easy-to-use guardrails. As LLM and AI technology improves, so too will the top embedded analytics platforms on the market.
This is a personal call that every individual must make, as there is no one answer. That being said, it really depends on the kind of work someone is looking for. Data analytics oftentimes has a much faster iteration cycle, as analytics are immediately fed into the organization for improvements. Data science focused on generally longer-term projects that are oftentimes product-focused, such as consumer algorithm improvements. Both are incredibly viable as career opportunities. The two fields do differ a bit in the average pay, as data scientists can expect to make on average $123,628, whereas a data analyst can expect to make about $76.698 on average, implying that organizations may see more value from data science as opposed to data analytics. That being said, there are many roles that are a hybrid between these two fields, or even a hybrid between other roles and data analytics. For example, a lot of what a CFO may do is based around data analytics, as analyzing company revenue and expense patterns is incredibly important to the role.
Data science vs data analytics is a big question asked, but generally is a distinction that isn’t important across the industry, namely because each task can cross over between these important fields. That being said, having an understanding of what generally a data analyst or data scientist does can be helpful in contextualizing the work, scope, and timeframe of insights. Both data science and data analytics will continue to be important and will become more important as people expect experiences to be tailored to their needs and businesses require more data-driven decisions.
