Business analytics and data science can feel confusingly similar. Both disciplines involve intensive work with data. Business analysts derive knowledge and insight from data using statistical methods, as do data scientists.
But these roles do have different purposes and priorities. For example, a business analyst often functions as a critical partner for senior management, as they help drive data-led business decisions. Meanwhile, data scientists operate more on the backend of organisations, solving complex problems that ultimately foster corporate progress.
If you’re considering a career in either discipline, a great way to start is by understanding the different tasks, skills, education requirements, and jobs of professionals in these roles. This article will try to help you truly appreciate, in different ways, business analytics vs data science.
Business Analytics Definition
Business analytics, when compared to data science, is a strictly business-focused discipline. Business analytics aims to solve a specific question or problem using data analysis, statistical modelling and other quantitative methods. It has no or very few research directions, instead centering on empowering business decisions with data insights.
Business analytics has overlapping fields and subsets, such as data analytics and marketing analytics. However, the discipline is itself a subset of data science. While a Master of Data Science can equip you to work in business analytics, a Masters in Business Analytics will not achieve the opposite effect.
Business analytics is concerned with extracting meaningful insights from and visualizing data to facilitate the decision-making process, whereas data science is focused on making sense of raw data.Matt Gavin
Data Science Definition
Data science is an amalgamation of several fields, including data mining and analysis, machine learning, natural language processing, and artificial intelligence. It is both a scientific and business-centred role with one primary goal – helping organisations harness and exploit their data assets.
The discipline started to take on a coherent form in 2008 when Jeff Hammerbacher and DJ Patil “coined the term data science to describe the emerging field of study that focused on teasing out the hidden value in the data.” Since then, data has achieved an even greater meaning and purpose for businesses. Today, data science is universally seen as pivotal to unlocking the wealth contained in data assets.
Data scientists explore data using advanced statistical methods. They allow the features in the data to guide their analysis… True data science uses custom coding and explores answers to open-ended questions.TechTarget
A Business Analyst Generates Business Insights
Business analytics is deployed to draw conclusions from data to aid business decisions. Business analysts want to answer questions that directly affect the bottom-line and short or long-term strategies of companies. For example, when there’s a problem with sales or when marketing is wondering what to improve, business analysts help provide data-led answers with valuable business context.
In a business analyst role, you’ll spend considerable time exploring how to meet corporate objectives using structured data. You might help senior leadership see the big picture of specific problems, test various hypotheses, or reach accurate decisions backed by solid data insights.
Business analysts use various statistical analysis and data analytics approaches to produce business insights and encourage good management choices. This includes leveraging descriptive, predictive, and prescriptive analytics to uncover data connections and postulate future predictions.
Additionally, because they work directly with non-technical people, analysts must interpret and communicate statistics in simple terms. As a result, they’ll typically work with tools like PowerPoint, Excel, Trello, and Smart Draw to visualise business info.
A Data Scientist Powers Data for Users
Data science is used to solve business-agnostic problems through data. A typical workday for a data scientist may involve creating a database to collect or combine various structured and unstructured data sources. Then they may clean, tag, and analyse those data sets and derive insights from them.
As a data scientist, you’ll solve complex problems and provide theoretical breakthroughs that other departments can adapt to specific business cases. But your job likely won’t include figuring out what business cases those solutions fit best or the mechanics of planning and deployment.
The work of a data scientist is highly technical, which is why their tools of trade include complex apps like Apache Spark, SAS, BigML, and Matlab. Natural language processing and machine learning are common approaches in this role, as you’ll be creating algorithms and training them to perform set functions.
As a data scientist, you’re not so focused on translating statistical information into digestible insights. Often, that job will fall to the business analyst who takes clean but high-level intel and transforms it into facts that business executives can digest.
Business Analyst Skills: Math and Communication
As an expert in business analytics, you’re are skilled at recognising the implications of data for business. Business analysts employ critical thinking and logical reasoning to break down problems and test assumptions methodically. But since their work also tends towards data collection and analysis, they have strong mathematical and statistical skills as well.
Additionally, business analyst roles require good written and oral communication skills. Frequently, the value of data and its solutions are best appreciated when presented with just the right nuance. Therefore, analysts keen on contributing business value deliberately hone these skills to amplify their influence.
Overall, visualisation and storytelling are relevant skills for business analysts. Likewise, analytics professionals may be competent in one or more programming languages such as C, C++, and C#. A Masters in Analytics program could cover topics ranging from programming principles and big data management to digital customer analytics.
Data Scientist Skills: Advanced Math and Coding
In data science roles, greater fluency in common programming languages is required. In addition, data scientists should build competence in other languages like Haskell, R, and Scala.
Data science roles require strong computer programming and linear algebra fundamentals. Participating in a data science master’s program will provide a good grounding in machine learning and multivariable calculus.
Strong data science candidates have an affinity for data mining and manipulation and are typically good at pattern and anomaly identification. Data visualisation and storytelling are also core skills for this role since data is useless without translation into meaningful findings for both technicians and laypeople.
To be an accomplished business analyst or data scientist, you generally need a strong university education. For business analytics, postgraduate education is highly beneficial but not always essential – especially if you studied analytics or similar as an undergraduate student.
In contrast, data science training at university is largely a postgraduate phenomenon. Data science demands a wide set of skills that are difficult to master. Hence, as a discipline, data science is mainly taught to graduates who have already completed a technology degree of some kind.
Business Analytics: Business and Technology
Business analytics professionals generally have a bachelor degree in a mathematics-connected field, such as business, statistics, computer science, information technology or engineering. An ability to work with numbers is indispensable to the data-crunching and interpretation aspects of their work.
Candidates for postgraduate business analytics courses may, therefore, come from either a technology or predominantly business background.
In a master’s foundation course, such as a Graduate Certificate in Business Analytics, students from a business background may be introduced to topics such as database management, information systems, and introductory data analytics. Conversely, those with a technology background may study accounting, customer value, and financial management.
Data Science: Data Science Masters
Data science professionals often have an undergraduate technology degree, with a major in computer science, information systems, statistics, applied mathematics, or data science.
Most data scientists also have a master’s degree. The most popular is a Master of Data Science or similar, such as a Master of Applied Data Science or Master of Data Science Strategy & Leadership. In addition, professional certifications in Neural Networks, Deep Learning, or Machine Learning may be required.
Jobs in Analytics and Data Science
Perhaps unsurprisingly, business analytics professionals often work as “business analysts” while data science professionals are often classified as “data scientists”. The work can overlap and the jobs have other titles of course. Often, a careful examination of job duties is needed to really appreciate what a particular position entails.
Data Scientists have more education and a higher degree of specialization, and so they typically command a higher salary than Business Analysts.BrainStation
Data scientists tend to earn more than business analysts, as you would expect given the exceptionally high levels of skills and training needed for data science roles. That is not to say that a given individual will earn more by entering the data science profession. For example, someone with a flair for business strategy and communication who has leadership qualities could have more success in a business analytics career.
Business Analyst Jobs
Business analysts potentially have a track into business leadership roles and data-focused positions. Potential career paths for analysts include:
- Operations Research Associate
- Quantitative Analyst
- Business Analyst
- Market Research Fellow
- Business Performance Analyst
- Strategy Leader
Data Scientist Jobs
Data scientists have numerous career options in theoretical fields with a research focus and applied roles within organisations and corporations. Some of the career options for data scientists include:
- Machine Learning Engineer
- Data Scientist
- Applications Architect
- Data Engineer
- BI Developer
Summary: Business Analytics vs Data Science
Ultimately, while the work of business analysts and data scientists substantially revolves around data, they employ data to different ends. Business analysts work on the frontline of business problems, providing insights that enable sound decision making.
Data science professionals operate more in the backroom where they collect, clean, tag, and manipulate data to deliver business value. While operating behind the scenes, the essential skill requirements for data science are greater and the salary compensation is commensurately higher.