Big data has become a major player in the world of tech today, thanks to the actionable insights and results that businesses can obtain from it. However, the creation of large sets of data also requires a thorough understanding and the right tools to hand to analyze them and gain the right information.
In order to better comprehend big data, the fields of data science and data analytics have grown from being mostly academic-related to becoming integral elements of business intelligence.
But differentiating between the two can be confusing. Although they are interconnected, the two fields provide varying results and involve different approaches. If you need to study the data that your business is producing, you need to understand what each option brings to the table, and how each is unique in their own way.
What is Data Science?
Data science is a field that focuses on getting actionable insights from massive sets of both raw and structured data. It primarily focuses on finding answers to the things that we don’t know we don’t know. Data scientists use a variety of techniques to unearth answers, including and combining:
- Predictive analytics
- Machine learning
- Statistics
- Computer science
Massive datasets are parsed through in order to establish solutions to problems that people don’t even know they have.
The main goal of a data scientist is to ask questions and locate potential areas of study, with less concern for specific answers and more for finding the right questions to ask. This is accomplished by predicting potential future trends, exploring and analyzing disparate and disconnected data sources, and discovering better ways to analyze information.
How is Data Science Used?
- Understanding customer requirements: You can use data science to find out what questions you should be asking about your target market. What if you could understand the exact requirements of your customers using the existing data that you have such as web browsing history, purchase history, income, location, and age? Not only does it allow you to determine what you should be asking, but you can also use it to find questions for customers that they didn’t even know they had, like ‘what product should I be buying alongside this one?’
- Decision making: Data science is used in a wide range of scenarios for decision making. Imagine if your car possessed the intelligence to drive you home by itself? Self-driving cars today collect live data from sensors, radars, cameras, and lasers to create a map of their surroundings. They then use this data to make decisions like when to slow down and speed up, when to overtake, or when to make a turn, using machine-learning algorithms.
What is Data Analysis?
The main focus of data analysis is processing data and statistically analyzing existing sets of data. Data analysts focus on coming up with methods that capture, process, and organize data in order to discover actionable insights for current problems and establish the best way to present the data.
Simply put, business analytics are used to find answers to questions that we haven’t yet got. It is based on producing results that can lead to immediate improvement.
Data analytics also encompasses several different branches of broader analysis and statistics, which help to combine diverse data sources and locate connections while simplifying results.
How is Data Analytics Used?
- Cost reduction: Big technologists and cloud-based analytics tools help companies achieve huge cost advantages when it comes to data storage, in addition to helping businesses identify more efficient processes.
- Better decision making: Similar to data science, data analytics can also be used to drive better decision-making. Businesses are able to analyze information immediately and use what they have learned to make a more informed decision.
- Understanding customer needs: Companies today use data analytics to get a better idea of what customers need. Analytics tools allow them the opportunity to gauge customer satisfaction, needs, wants, and pain points, ultimately providing them with the power to give their customers exactly what they’re looking for. Thanks to big data analytics, more companies are able to create new products and services to solve problems that we previously didn’t have a solution to.
So, What’s the Difference?
While the terms are often used interchangeably, big data analytics and data science are both unique and different fields. Data science is an umbrella term for a group of fields focused on mining large sets of data, while data analysis is a more focused, specific version of this and can be considered to be a part of the larger process. Data analytics is about realizing actionable insights, which can be immediately applied.
This guide from Suffolk University online explains further why exploration is another significant difference between business analytics vs data science. Data science is not concerned with answering specific questions and queries, instead, it explores massive sets of data to expose insights, often in unstructured ways. On the other hand, data analysis works best when it has a specific focus and there are questions that need answers based on existing data.
To sum it up: Data science produces broad insights that focus on which questions need to be asked, while data analytics focuses on discovering the answers to questions that are already being asked.
More importantly, data science is focused on asking questions, rather than finding specific answers. It is a field that is devoted to establishing potential trends based on existing data along with finding better ways to both analyze and model data.
Choosing a Career Path:
Are you interested in pursuing a career working with big data? There are several career paths to choose from working in either data science or data analytics.
Business Analyst:
Typically, business analysts are somebody with strong analytical and statistical skills, plus a deep understanding of their field of business. This could include:
- Finance
- Marketing
- Management
- Operations
The role includes a range of responsibilities, from gaining a solid understanding of the business issue to delivering data-driven evidence in order to influence better decisions. Business analysts use data to get answers to questions such as:
- Could business operations be smoother or more efficient?
- Is the business meeting its objectives?
- Is the company getting the best ROI?
- Can the business get a better understanding of its customers?
Data Scientist:
Similar to business analysts, data scientists are responsible for data collection, preparation, and analysis. Individuals who pursue a career in this field tend to have a strong math background and strong numerical skills, plus an in-depth knowledge of statistics and programming. Generally, data scientists will work with big data from various sources including:
- Social media
- Websites
- Mobile computing
They sort through the data to look for trends and patterns, in order to develop predictive models. Data scientists are also responsible for creating simulations in order to test potential solutions for overall effectiveness and cost in a business.
What Are the Main Differences Between the Two Career Choices?
- The biggest difference between working as a business analyst and working as a data scientist is the focus of your career. The business analysts will have the business model at the center of their work and are typically specialized in a certain industry. On the other hand, data scientists are not usually limited to any particular industry or specialty.
- Compared with business analysts, data scientists will typically engage with more advanced levels of coding and programming, often including machine learning and AI.
- Business analysts generally work within a team, whereas data scientists tend to work independently, consulting with others as an when needed.
- Data scientists are often tasked with data storage and architecture, while business analysts are not.
And What Do the Two Have in Common?
Although the data scientists are more math-focused and business analysts are more business-focused, there are several similarities between the two positions. Both need to have:
- Strong statistical skills and knowledge
- Good critical thinking skills
- Excellent communications skills
- Strong problem-solving abilities
Both professions are interested in using the available data to find information and look beyond it; business analysts and data scientists will often work together. If you are interested in working as a business analyst or data scientist, you should choose a degree program or course that offers the option of learning about areas such as:
- Programming and coding
- Data analytics
- Data mining
- Project management
- Enterprise products
- Predictive analytics
- Leadership training
Work Environments:
Since both data scientists and business analysts are in high demand in every industry, the environments that they work within can be significantly variable. Business analysts tend to work in corporate environments with a need for:
- Adherence to the business model
- Adherence to business practices and policies
- High data security
- Outcome reliability
Data scientists, on the other hand, need to work in relatively fluid environments. They need to work in an environment that offers:
- Freedom to use open source applications
- Freedom to use multiple applications
- The ability to test various different approaches
- Access to raw and unprocessed data
While both data analytics and data science are similar and often used together, as you can see, the two are not the same. Both are hugely important for businesses the world over, to help them make better decisions and understand consumer needs. Data science helps companies find questions they didn’t know they had, while data analytics helps them find the answers.
