Business Analytics vs. Data Science How They Compare
Data Science vs. Business Analytics
Buzzwords like business analytics and data science are often used interchangeably, but they have very different meanings. Average employees won't care about this interchangeability, but owners need to use these terms properly. This is due to their enormous impact on business operations, innovation, and growth.
So, what exactly is business analytics? Analytics is the use of historical and real-time business data to extract insights that optimize decision-making. It requires expertise in machine learning, artificial intelligence, and statistical analysis. Restaurants primarily use analytics to streamline the supply chain, reach new customers, and increase sales.
On the other hand, data science is the study of data. Those with data science degrees are in high demand in the business world. Industries such as technology and finance need data scientists to answer broad, complex questions. Data science uses statistical analysis, algorithms, and technology. While data science provides insights, these are not typically used for business decisions.
The main difference between the two fields of study is that BA deals with mostly structured business data. This is any data that is highly organized, such as information on an Excel spreadsheet. On the other hand, data science looks at both unstructured and structured data to generate insights. Unstructured data is not organized or arranged by any traditional standards. Read ahead to find out more on business analytics vs. data science.
What to Know About the Data Science Field:
Data Science vs. Business Analytics - Similarities and Differences
Data science employs traditional analytics practices along with expertise in computer science, such as coding. In a sense, data science acts as a parent to business analytics. Data science is primarily used by banks, academic institutions, technology industries, and e-commerce-based industries. This is because these industries seek out more unstructured data that they need a data engineer to drill into. For example, a health researcher may hire data analysts to identify and predict disease. An athletic organization may hire a data science specialist to find better athletes.
This requires analysts to drill down into data sources that are both unstructured and structured data. Examples of unstructured data include media and entertainment data, weather data, social media, or various business documents. They will look for specific patterns that are difficult to discern without the right type of data mining techniques.
On the other hand, retailers and marketers tend to hire business analysts more than data scientists. This is because these industries have already acquired business intelligence that is structured, or within a specific information system. It has already been captured, stored, and monitored so employees can access it. Examples of structured data include customer names, dates, addresses, or POS data.
There are circumstances where a restaurant may hire a data scientist as opposed to a business analyst. If a restaurant chain wants to identify customer preferences and assess various market trends, that market trend data is unstructured. Some business analysts may be able to conduct business analysis on this information, but it may require help from data scientists. Both practices are similar in many ways. They require data mining, modeling, and making inferences. Both involve analysis, but data scientists are more specialized.
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Business Analytics vs. Data Science Which One is Best?
When it comes to deciding between a data scientist and a business analyst, there is no right answer. It depends on the type of industry, and the business questions that need to be answered. While most professionals seek predictive analytics (a subset of business analytics), there are circumstances where they may need a data scientist.
Business analysts have more knowledge in project management, business development, and business systems. They understand what companies require because they are used to answering standard business questions. They know that the primary goal for most restaurants is to increase sales and find new customers.
A data scientist has more extensive knowledge in software engineering. It is a more specialized type of field. While BA's primary goals are to determine KPIs, extract insights, and solve problems, data scientists are different. They seek to make the company more efficient, just like in BA. Furthermore, they want to answer complex questions that cannot always be answered with structured business data. This makes data scientists more palatable to academics and anyone in e-commerce.
In short, data science is great for studying patterns and trends. Business analytics is better for sorting out specific business problems. If a restaurant wants to gain a broader perspective on consumer behavior, it may employ a data scientist. If it wants to drill down into employee performance to make improvements, it may hire a business analyst. It all depends on the specific business questions a restaurant has and what its goals are.
Business Analytics in the Restaurant Industry: Restaurants use business analytics to create better guest experiences, curate menu designs, optimize marketing campaigns, streamline inventory management, and optimize table turnover. The industry also uses analytics for sales forecasting and to study repeat customer trends.
Key Takeaways of Business Analytics vs. Data Science
In conclusion, here is what to know about business analytics vs. data science -
- Data science combines programming skills and statistical analysis to extract insights. It uses both unstructured and structured data to create models.
- Business analytics uses historical and real-time data to generate insights that help optimize decision-making.
- Industries that tend to use data scientists include technology, finance, e-commerce, and academia. Industries that use business analysts include finance, technology, marketing, and retail.
- Restaurants can use both data scientists and business analysts, depending on specific needs. If they have broader questions about consumer behavior, they may hire a data scientist. If they want to drill down into internal processes or inefficiencies, they may hire a business analyst.
