Data Science And Business Analytics: Key Differences & Similarities

What is Business Analytics?

A pioneer of the scientific management movement, Frederick Winslow Taylor coined the term Business Analytics back in the late 19th century. The one who operates in accordance with the principles of Business Analytics is known as Business Analyst.

This particular field mostly pertains to the monetary benefits that an organization is able to accrue and all the actions and choices are mostly considered in regard to this fact.

Business analytics is the process of gathering information and business intelligence to drive better decisions in an organization. In each and every industry, there is potential use of the field of business analytics.

The data gathering and analyzing capability enables organizations to make decisions instantly. Business analytics is largely concerning the assistance of decision-making within organizations.

It entails collecting information, processing it, and with a view of identifying certain factors that show the results of certain events when they occur. For instance, a retailer will have to assess data about its customers to know what goods should be stocked on the shelves to render optimal sales outcomes.

The key focus is to deliver capabilities for the creation of business value of an organization and essential alterations that are primarily needed to achieve some critical milestones of business.

Some of the methods that are widely employed include data mining, statistical analysis, and predictive analysis coupled with insight into the operations of the business environments. These technological tools are interwoven to study and transform data into constructive information in an effort to anticipate certain results, which will aid managers in making decisive and key point decisions for business and its operations.

What is Data Science?

Data science is defined as the study of data and is one of the most popular and rapidly developing disciplines of recent years. With the migration from the traditional analog world to the digital realm, many processes required the assistance of individuals capable of gathering data from extensive databases and using these data to make decisions and execute actions.

Data science and business analytics are used in many aspects such as studying customer behavior patterns, controlling and optimizing advertising promotion, analyzing business processes, controlling product quality, and many others.

The concepts of data science and business analytics have been adopted across most lines of businesses, though remain most evident in the financial sector. The 1990s saw the large customer information databases being adopted by the banks where they deployed them in an attempt to identify the kinds of customers that were likely to default on their loans.

This became effective in providing easier ways of extending credit cards in the market with relatively lower charges that banks could still make profits from. Data science is also a wider field than business analytics because the latter does not necessarily encompass predictions about future activities.

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The Role of a Business Analyst

Many people have little appreciation for what business analysts do, but they are indeed the backbone of the business world. Many of them are the ones who are able to turn the company’s ideas and concepts into reality. However, they frequently remain unrecognized for the job they do. Here are some of their roles and responsibilities –

• They design the experience of the new product or the new software, how people are going to use it, and where and what role it plays in their lives.
• They ensure that software operates effectively through means of verifying, validating, and confirming that it meets or performs optimally as required by established industry standards and that the end users can conveniently comprehend the manner in which the software operates.
• They find issues about the software that could significantly harm companies and users in the future.
• They collaborate with developers regarding the development of new features to the current software.

The Role of a Data Scientist

The position of a data scientist differs from organization to organization; nevertheless, here are some of the typical tasks that a data scientist performs: They are to engage in making sense of the data collected.

• They are knowledgeable in statistics, and machine learning and they possess considerable business acumen. Therefore, it is crucial to consider that the data scientist role is a blended occupation that involves both the technical and the managerial levels of a business organization.
• Their role includes gathering, cleansing, preparing, and analyzing data to derive actionable insight for the decision-makers.
• To be effective, they must be able to grasp the nature of the problems and decide on the most suitable approach, as well as relay this information without much delay.
• Data mining specialists are expected to sort through massive amounts of information and convert it into valuable knowledge for decision-makers in an organization.

Skills Needed To Be A Business Analyst

The key role of the business analyst is to consult with developers, designers, and other teammates to make sure that they create something that their clients will need. To be able to carry out all these, the following skills are required –

• An ability to comprehend the nature of the business challenge.
• The awareness of the nature and size of the project that is to be launched.
• Excellent communication skills.
• Some understanding of how software is developed and how software development progresses
• Knowledge of information technology systems and structures.
• Must be able to write formal reports and memos, possess good organizational skills, and be able to interact with others effectively.
• This means they must always be able to organize their working time and prioritize the tasks at their disposal.
• Must have analytical skills

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Skills Needed to be a Data Scientist

They need to possess a broad spectrum of skills and a certain amount of knowledge to become a data scientist. The following are some of the key skills that may be considered to be the most useful –

• To learn Data Science, one has to understand computer science, algorithms and analysis, linear algebra, and programming.
• It also requires you to know statistics and have a higher working concept of concepts of machine learning (supervised, unsupervised, and reinforcement learning), deep learning feedforward & Recurrent neural networks).
• Coding is inevitable in data science as most of the time optimal solutions are required to determine using many efficient processes which are coded manually by a programmer and may differ case by case.
• It helps to know programming languages, such as Python, R, SQL, etc.
• Knowledge of machine learning technologies including neural networks, support vector machines, and decision trees. These are some of the algorithms that are widely in use by the data scientist in the market today.
• Experience with other big data tools such as Hadoop or Spark.

Career Opportunities: Business Analyst Vs Data Scientist

Business analysts, data scientists, and anyone involved in the analysis of big data and utilizing it to create greater value are in high demand. The two careers have many similarities: both involve soft skills such as communication and problem-solving skills and the need for programming and statistics skills. Nevertheless, there are certain factors that make these two careers to be different.

Job Prospects For Business Analyst

Business analysts often collaborate with other departments in a firm including marketing, sales, IT, finance, and human resources in order to provide quality goods or services. These roles are generally found at every level in the business hierarchy ranging from junior business analyst jobs to more senior roles such as enterprise business analyst or even an enterprise architect.

• Financial Analyst: Understand and apply the analysis of current and past financial data to invest in a business, determine risks, and plan for the future.
• Operations Analyst: Analyze information for the purposes of enhancing supply chain management, procurement of logistics services, and overall operation.
• Digital Marketing Analyst: Collect data to evaluate the effectiveness of digital advertising and marketing communications, customer targeting and retention, and the websites that support them.
• Customer Relationship Manager (CRM): Maintain customer details and serve the purpose of Customer Relationship Management of strengthening customer satisfaction and reducing customer churn.
• Healthcare Data Analyst: Identify and evaluate the quality of patient care, facilities that must be provided to patients, hospitals queuing to receive the desired patient, and potential costs of healthcare.
• Consultant: Work as a data analytics consultant where one offers intelligent solutions for one’s clients drawn from a given industry.
• Risk Analyst: Analyze financial and operational data and design risk models to manage and control financial and business risks.

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Career Prospects Of A Data Scientist

Big data is a compelling issue, and data scientist professionals are, therefore, sought after. The subject is advancing fast, and the jobs for data scientists are, indeed, abundant.

Data science is a profession with wide potential where a person carrying out this profession can work in various fields. There are many possibilities where a data scientist can be employed ranging from working for a big firm to a small firm or even one is able to work for themselves as a freelancer. Below are most of the typical jobs of data scientists:

• Data Scientist: Algorithms and statistical methods for developing and using machine learning models to analyze data and identify potential solutions for particular business challenges.
• Machine Learning Engineer: Specializes in the development, construction, and implementation of machine learning models and algorithms to support predictive and prescriptive decision-making.
• Data Analyst: Gathers data, organizes, and processes data for the identification of valuable patterns and trends that can be instrumental to various decision-making activities.
• Business Intelligence Analyst: Design reports and dashboards as engaging mediums to present the results and further support data analysis and decision-making.
• Data Engineer: They design and implement data conduits, databases, and other systems to support the efficient extraction, collection, storage, and management of data.
• Quantitative Analyst (Quant): Jobs in finance to model the outcomes of investment decisions by deriving mathematical equations to help structure trading practices.

A data scientist may also need to develop some reports to assist the management in knowing how financially well their business is performing compared to other businesses in the same industry or across the globe in all industries.

Salary Difference Between Data Science and Business Analytics

When it comes to comparing average salaries in India, Data scientists get paid more than business analysts because the cost of operation for a Data Science project will need expensive resources and so the organization is forced to hire a candidate with specific experience in the domain thus the current average salary for data scientist being higher than that for a business analyst.

Even in the skills area, data scientists have a slight edge over business analysts, as the former must write a lot of code and work with raw unformatted big data in structured and unstructured formats.

Difference in Application between Data Science and Business Analytics

Data Science and Business Analytics is a broad field that possesses a large number of applications and utilization possibilities. This is because Data Science can be applied in technology, finance, academics, health care, retail, astronomy, climate, oceanography, stock markets, and many more.

There is no limit to which we can go and make predictions with data science and come up with solutions to any problems. Like marketing analytics, it can also be applied in fields such as retailing, finance, electronic commerce, customer relationship management, and marketing. Data Science can serve as a tool and can be applied to every sphere of our everyday life.

In healthcare industries, things like an electrocardiograph, heart monitoring, images of various organs, and sometimes even the data of each patient are analyzed to find the tendency of diseases and illnesses among others.

In the Stock Market, to be able to decide whether the particular trend will be more inclined to be on the bullish side or the bearish side, data science helps answer quite a number of questions. Likewise in sales decisions can be labeled throughout the year and the kind of sales can be established so as to boost the profits.

We even utilize it in Data Science in climate prediction, weather forecasting, and oceanography. These applications of Data Science have made predictions of humans over certain solutions more confirmed and give confidence while making the right decisions.

Business Analytics has emphasized its significance based on fields that are specifically relevant to business-related applications commonly found in the domain of finance and retail.

This domain makes it possible for business owners and management-level authorities to make the right decisions such as whether a certain scheme would be advantageous to the bank and its customer or whether a particular product would increase its sales or not. It even goes a step further to help the analysts determine which are the most probable implementable solutions to a problem.

Key Differences and Similarities of Data Science and Business Analytics

In some ways data science and business analytics have similarities; however, it is crucial to understand that they are distinct in terms of their purpose, strategies, and uses. It is therefore possible to note that having a clear understanding of both disparities makes it possible to combine these two fields to enhance the result of both professionalism and business outcomes.

Differences

• While Data Science seems to address predictive analytics and process automation, Business Analytics is involved in creating decision-making tools and strategies.
• Data Scientists are also usually involved in the process of data preparation and are able to work with different types of data, such as unstructured and semi-structured data, whereas Business Analysts work mostly with structured data in their role.
• Data Science utilizes complex methods such as machine learning and artificial intelligence, whereas Business Analytics focuses more on descriptive and prescriptive analyses.

Similarities

• Both fields of data science and business analytics require data collection, and data processing and analysis activities in order to arrive at some conclusions.
• They do have some similarities in terms of tools and technologies such as SQL, Python, and visualization tools.
• While the Data Scientists engage with cross-functional teams and other stakeholders to convey insights and influence decision-making, the Business Analysts likewise work with cross-functional teams and stakeholders to present information and make important determinations.

Pros and Cons of Business Analytics

Pros

• Contributes to a better assessment of risks and opportunities and contributes to the development of successful strategies.
• Enhances the business’s functioning and its various activities
• This leads to the development of growth strategies and competitive advantages
• Improves the firm’s offerings and customer capture
• It helps to foster the culture of big data and analytics in organizations.

Cons

• Dependence upon the reliability and accessibility of information
  Implementation of change and increased use of data in decision-making
• This is likely to contribute to the inability of the system to integrate data from different sources with ease.
• Chances of subsequent misinterpretation or improper use of analytical outcomes.

Pros and Cons of Data Science

Pros

• Enables data-driven decision-making
• Automates and optimizes processes
• Discovers data points or trends that may not be easily found
• Improves customer experiences
• Promotes innovation and competitiveness

Cons

• Demands specific training and knowledge.
• Requires handling large and complex data sets
• Privacy issues, bias issues, and matters of transparency.
• A model is never perfect and requires frequent updates or retraining due to the evolving complexity of systems.

Frequently Asked Questions

1. Which is better MBA or Business Analytics?

While MBAs and Business Analytics both deal with business problems, their methodologies differ. An MBA program will prepare you for improved company planning, expansion, and overall business growth. Any Business Analytics course will teach you how to enable data-driven strategy, expansion, and growth.

MBA is ideal if you want to become an executive or establish your own firm. If you are a data enthusiast who wants to learn about the possibilities of data utilizing analytical tools and predictive models, a business analytics course will assist.

2. Is coding required in business analytics courses?

Substantial knowledge is always useful for developing data models and making data-driven predictions. However, it is not a required ability to become a data analyst. Most data science and business analytics courses will teach you the fundamental programming ideas that will help you shift your career in the appropriate way. So, not knowing how to code is not a barrier.

3. Which is better Master in Data Science or Master in Business Analytics?

The final decision on whether to pursue an MS in data science or an MS in business analytics will be based on your intrinsic skills and future job ambitions. However, there are a few courses available that combine data science and business analytics programs to prepare you to be a master in both domains.

4. Which one earns more data scientists or business analysts?

Following the current trends in the market and data obtained through Glassdoor, the average remunerations of a Data Scientist are now higher than that of a business analyst in the same company for an equivalent level of experience.

5. Is it possible to transition from business analyst to data scientist?

Yes, it is possible to switch from a business analyst to a data scientist with additional training and by getting some practical exposure in live projects. Since business analysis falls under the Data Science umbrella, skills in Machine Learning and more coding practice along with the use of tools such as Pandas, NumPy, and Sci-kit-learn are going to push your skill set to resemble that of a Data Scientist.

Conclusion

Data science serves as the foundation for business analytics, which focuses on data analysis relevant to business. Mastering the art and science of data handling necessitates the use of numerous advanced tools. These are two disciplines that have similarities as well as dissimilarities in how they approach and manage data. Further, without these tools and techniques, complicated and unstructured data could not be transformed for insightful operations.

The general concepts are similar, as both are involved in obtaining information from data; however, the methodologies are quite distinct. Data science lays more emphasis on coming up with new methods and approaches to managing data while business analytics emphasizes the application of standard approaches in analyzing data.