Data analytics and business analytics are overlapping fields. In both cases, the essential task is to extract insight from data; to convert data into information that can be used.
When you compare data analytics to business analytics, you need to understand that data analysts and business analysts often work on the same kinds of problems. But differences exist in approach and purpose.
With data analytics, you manipulate data so as to reveal patterns, weaknesses, strengths and statistical insights. With business analytics, you use data to show what the data reveal about business performance and opportunities. Data analytics is broad and technical whereas business analytics is more narrowly focused on informing business decisions.
Differences in Data Handling Approaches
When doing analytics, a data analyst focuses on the data itself. They may work to ensure the dataset is compiled and cleaned to be in the best health for analysis. They may run tests and create models to find the stories that the data is telling.
With business analytics, you engineer the data to answer the questions you’re interested in. For example, you might want to know how consumers are responding to two different ad campaigns. You manipulate and filter the data specifically to inform business decisions.
A data analyst is like a biologist examining and documenting a new species of frog for a science journal. A business analyst is like a food scientist testing to see if a new frog species is commercially viable for French restaurants.
Differences in Purpose
You could imagine that, for a large organisation or company, it would pay to employ people to do both data analytics and business analytics. While data analytics is important in earlier stages of information processing, business analytics may come to the fore at later points such as in board meetings.
A large firm could use data analysts to maintain good data systems in terms of collection, processing and organizing. Strong data analytics might yield some surprising findings that nobody was looking for.
At the same time, business analysts are needed to answer important questions about business operations and strategic direction. Good business analytics may shape how a firm runs and evolves.
A small business, on the other hand, needs someone who can do both jobs competently. If a startup hires a single analyst, that person needs to be able to maintain information systems and do initial data wrangling. At the same time, they have to produce business-minded outcomes, informing the business owner(s) what the data indicate in terms of actionable items.
You may find when looking at analytics programs that data analytics courses have similar syllabuses to business analytics courses. And, in fact, some courses dispense with the distinction and just call themselves analytics courses.
Although we’re doing a data analytics vs business analytics head-to-head comparison in this article, the two fields share a great deal in common.
Data analytics is the foundation of business analytics. While a business analyst may be tempted to cut corners and jump straight into the commercial points of interest in a dataset, a good one will do data analytics as well. They’ll prepare data in an orderly, thorough fashion and explore a dataset dispassionately to see what it reveals.
On the flipside, data analytics must lend itself for use as business analytics or, at least, as a foundation for business analytics. Outside of academic settings, the uses of pure data analytics are limited. A data analyst risks making themselves irrelevant if they’re unable to also put on the hat of a business analyst.
In academia, you are encouraged to spend as much time as you need to find the most innovative and elegant solution. In industry, you are encouraged to spend as little time as possible to find an analytical solution that merely satisfies the need.Jacqueline Nolis
To provide value to students, a good analytics courses should offer a balanced coverage of data and business analytics. In a Master of Analytics for example, you would expect to see subjects that fall almost exclusively in the data analytics camp (e.g. Programming Principles) as well as subjects that are unique to business analytics (e.g. Analytics and Business).
Analytics Career Spectrum
The data analytics vs business analytics comparison is perhaps clearest when looked at in terms of a career spectrum.
Suppose you want someone to look after an important dataset, requiring the person to: liaise with data collection officers, establish multi-purpose reporting functionality, and produce statistical reports for people such as consultants and line managers. That person could be called be a data analyst.
Suppose, on the other hand, you want someone to advise the CEO and board members on corporate performance and strategic direction. The person needs to be able to produce a 20-minute PowerPoint presentation that visually demonstrates where the company has been missing opportunities and what it could do to expand market share. That person could be called a business analyst.
For data analytics, you need strong technical skills and the ability to work carefully, precisely and logically. For a business analytics career, you need to understand company culture and have the communicative powers to influence decisions.
Exactly where you sit on the analytics career spectrum may only become clear as you progress along. You might steer towards the technical side of things as a data analyst. You may gravitate towards somewhere in the middle. Or you might end up focusing more on communication and persuasion as a business analyst.