If you were to gather the world's prominent business luminaries and ask them to determine the most significant difference underlying the business in the 20th century and 21st, more than half would say it's 'Data.'
With the emergence of IoT, social media, smartphones and other tech advancements, data expansion in business had significant growth. The data growth has led small-scale and large-scale companies to think of how to leverage information for business benefits. Meanwhile, people started seeking different options to develop their data skills, advance their careers and gain job security.
If you have no prior command of data and are trying to bolster your skills, 2 terms you are likely to encounter two terms: Data Science and Business Analytics. Though both seem like a similar job role at first, there are several differences.
Data Science and Business Analytics involve knowledge & information gathering and modelling. However, the difference is that Analytics is specific to business-related issues such as profit, cost and so on; on the other hand, Science answers questions such as geographic influence, customer business demands and seasonal factors.
In a simplified version, we can say that Data Science combines data with algorithm technology & building to answer a wide array of questions. In contrast, Business Analytics is the company data analysis with the statistical concept to obtain insights and solutions.
To have a deep understanding of the differences between the two, let us look at some of the basic concepts: What is data? Definition of Data Science and Business Analytics.
What is ‘Data’?
Data is a set of values in different formats such as text, numbers, bits & bytes, and human memory facts. It is meaningful only when Data is placed in a context. We have noticed the interchangeable use of ‘information’ and ‘data.’ Though they seem similar, they have a vast meaning difference.
Information is more of an abstract concept, whereas Data is a solid concept. Computers leverage different kinds of data stored in digital formats such as multimedia, text, and numbers. Professionals dealing with these data are known to be Business Analysts and Data Scientists.
What are Business Analytics?
It is all about determining different ways or methods to enhance a business. Though the medium has evolved over the years, Business Analytics as a concept has been leveraged since the 19th century.
With the help of data and statistics, Business Analysts will resolve an issue faced by a business or a company analytically. Business Analytics may include analyzing company data, forecasting from previous data, strategical enhancement through optimization, data visualization enhancement via charts and graphs.
Their role is to analyze data and be responsible for sharing their finding with other workforces, supporting them to make changes that you are suggesting.
What is Data Science?
Data Science follows a similar practice to Business Analytics but is much more comprehensive. It is the data examination and research revolving around the developing methods to store, record & analyze data for efficient extraction of relevant information.
The term 'Data Science' was coined in 2008 by Jeff Hammerbacher and DJ Patil when working for Facebook and LinkedIn. Its primary focus is to derive insight and knowledge from any data form, whether structured or not. Data Scientists leverage their skills in a wide range of industry verticals like technology, academia and finance.
Data Science Vs Business Analytics
Here are the top comparisons have seen between Data Science and Business Analytics:
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Coining of Term
The term 'Data Science' was introduced in 2008 by Jeff Hammerbacher and DJ Patil when working for Facebook and LinkedIn, respectively.
Business Analytics as a concept has been leveraged since the 19th century when it was introduced by Fredrick Winslow Taylor.
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Concept
Data Science leverages the interdisciplinary field of algorithm building, data inference and systems to obtain data insights.
Business Analytics uses statistical concepts for extracting business data insights.
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Industrial Application
The top 5 industries where Data Science is leveraged are:
- Academia
- Financial
- Technology
- Internet-based
- Hybrid fields
The top 5 industries where Business Analytics is leveraged are:
- Retail
- CRM
- Technology
- Hybrid fields
- Financial
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Coding
Coding is widely used in Data Science. The field mixes traditional analytics principles with in-depth computer science knowledge.
Business Analytics does not involve much coding as it is more statistics oriented.
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Language Tools
The language tools used in Data Science are:
- C/C++/C#
- Stata
- MATLAB
- Scala
- Haskell
- SAS
- R
- SQL
- Java
- Python
- Julia
The language tools used in Business Analytics are:
- SQL
- C/C++/C#
- Scala
- Java
- R SAS
- MATLAB
- Python
-
Statistics
In Data Science, statistics is leveraged at the end of analysis following coding and algorithm building.
In Business Analytics, the fundamental analysis is statistical oriented.
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Work Challenges
In Data Science, the business decision-makers do not leverage the outcomes. It cannot apply findings into the decision-making process of a company. There is no accuracy on the questions that need answers with the provided data set. The top challenge among Data Science is its difficulty in data accessing and the prerequisite of IT coordination.
Similar to Data Science, Business Analytics cannot apply findings into a company's decision-making process, no accuracy on the questions that need answers with the provided data set, difficulty in data accessing, and the prerequisite of IT coordination. Other work challenges seen here are the lack of significant domain expert input, data inaccuracy, privacy concerns, fund shortage to buy relevant data sets from external sources, and tool limitations.
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Data Types
Data Science uses 2 types of data: big data and traditional data. Traditional Data means structured data stored in a database. In contrast, big data include a wide variety of Data - text, images, mobile data, numbers and audio, Velocity - retrieved and computed, and Volume - measured in Tera, Peta and Exabytes.
Business Analytics predominantly uses structured data. This historical Data helps understand the factors that may impact your company.
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Future Trends
The future application of Data Science is Artificial Intelligence (AI) and Machine Learning (ML).
The future trend of Business Analytics would be in Tax Analytics and Cognitive Analytics.
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Disciplines
Data Science provides data insights that assist companies in increasing their operational efficacy, determining new market choices, enhancing sales and marketing efforts, and many more - giving a competitive edge in the market. Some of the disciplines involved in this field are:
- Predictive analytics
- ML and Deep Learning (DL)
- Business Intelligence (BI)
- Data and Warehouse engineering
- Statistical analysis
- Data visualization & mining
Business Analytics includes determining business requirements, leveraging previous data, finding solutions - new system development, strategic planning, and process optimization. Some of the disciplines involved in this field are:
- Data analysis
- Solution assessment
- Elicitation and Analysis prerequisites
- Workflow modelling
- Business modelling
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Job Opportunities
Data Science skillsets are required in most job sectors and are not restricted to tech-related industries. However, you get an opportunity in these high-paying, in-demand professions at tech giants an advanced degree is a prerequisite.
The in-demand profession includes:
- Data Engineer
- BI developer
- Data scientist
- Applications architect
- Data analyst
- ML engineer
Recruiters in Business analytics generally look for hiring the following professionals:
- IT business analyst
- Business analyst manager
- Data business analyst
- Computer Science data analyst
- Data analysis scientist
- Quantitative analyst
- System analyst
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Salary
Data scientists enjoy high-pay salaries and job expansion. According to 2020 BLS data, the average wage earned by Data Scientists was $126,830 per year, with the highest 10% making in 2020. According to LinkedIn, the average salary of Data Scientists in India is INR 850K, and in the US, it is $125,044. Based on experience, first-level Data Scientist earns around INR 611K and $98,122 per year, while most experienced workers make up to INR 20L and $168,372 per year.
The average salary of a Business analyst in India is approx. INR 612,656 per year and in the US is approx. $70,489 per year. Based on experience, first-level Business Analyst earns around INR 363,813 and $ 60,055 per year, while proficient workers make up to INR 1,284,643 and $90,431 per year.
Final Thoughts
Provided the recent advancements, both can expect a drastic transition in analyzing method data. With the significant growth in big data, organizations will have the chance to explore a wide range of data and assist the management make vital decisions.
This is not just from the financial side but also from the customer demands, geography, etc., contributing to company expansion. In addition to the data and expected trends, a vital factor is skill learning. Both offer workforces numerous scopes to learn and boost themselves. Learning is a crucial factor in keeping up with the recent innovations or developments.
With augmenting data and learning trends, Business Analytics and Data Science opportunities can be considered an enormous trend.
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