The arrival of big data into the picture of industries all over the world has considerably increased the importance of data science. However, many tend to confuse the terms ‘data science’ and ‘business analysis’, often taking them to mean the same thing. In reality, there are distinctive differences between the two, and each has its own pros and cons. If you’re ever planning to make a career in either of these fields, it’s important to learn the difference between the two.
Business Analysis vs Data Science Difference #1: Definition
Put simply, the overarching goal of a business analyst is to help grow a business in a given market under certain conditions. They have a direct influence on critical financial decisions made in the company. They use data to create and change policies, improve productivity and stabilise systems. A data scientist, on the other hand, is responsible for collecting, processing and reporting findings from a massive data dump. Data scientists convert raw data into structured, meaningful silos which are then used to generate reports on trends or make forecasts.
Business Analysis vs Data Science Difference #2: Analysis
A business analyst perceives data, insights and requirements from the perspective of the business and its overall operational systems. A data scientist observes and visualises the relationship between data in a database. While data analysts derive insights from data dumps, business analysts take what is needed to make the business function better and meet certain milestones. Both problem-solving roles are highly data-focused; the difference is that they enter at different stages of the operation and manipulate data to different goals.
Business Analysis vs Data Science #3: Skills
While the skills required for both roles might overlap by virtue of dealing with data, in the end, there are some differences. The following skills apply for data scientists:
● Programming: Where premade software might not be flexible enough, data scientists must be equipped to make personalised changes
● Software: Data scientists must be well-versed in a plethora of tools serving different purposes, from statistics to visualisation
● Data management: From collecting to segregating and organising massive data dumps, data scientists are expected to be adept at managing and manipulating data
When it comes to business analysts, the following apply:
● Analytical skills: By virtue of their job role, business analysts need to be excellent at analysing data and immediately spotting significant information and leveraging it
● Technical understanding: though not as much as data scientists, business analysts are expected to understand databases, basic software and visualisation systems to interpret data
● Soft skills: Business analysts need to network and brainstorm with multiple key players, so they must have the necessary soft skills to work well under pressure
Business Analysis vs Data Science #4: Outlook
Business analysts are strictly organisation-centric, though their policies might have a national or global impact. They deal with strategies, alliances and networks a lot more than data scientists. The latter is more likely to be mathematicians and statisticians in their outlook, sifting through data to find salient patterns and outliers. Whether a finding is of significance to the business or not, is up to the business analyst. That said, the business analyst heavily relies on data scientists to collect and segregate data they can work with.
Conclusion
Despite the differences in tools and skills, it’s safe to say that both business analysis and data science deals centrally with data– the shift lies in where they stand in the operational process. If you’re confused between the two roles, its best to envision the type of work you see yourself doing. Would you rather be making business decisions or collecting and making sense of data? Are you people-centric or technology-driven? The answers to these questions will help you zone in on what career suits you the most.
