Top 14 Differences Between Data Science Vs Business Intelligence

Both business intelligence and data science are important fields and help organizations make important decisions. In both these fields, you can gather and work with data to produce useful insights that will help you make better decisions. It is important to understand which of them to use according to your business goals and requirements. We will be looking at the features of data science vs business intelligence in terms of skills, careers, types of data processed, uses, and other aspects.

So, in this post, we explore the importance of data science vs business intelligence and the top 14 differences between data science and business intelligence.

What is Data Science?

Data science is collecting, storing, and analyzing data for an organization, deriving useful insights, and making predictions from this data.

Through these insights and predictions, you can help businesses make decisions, improve products and services, and achieve desired outcomes such as increasing sales, reducing costs, and improving customer experience by recommending relevant products and services to customers.

Starbucks uses data science to create (and make changes to) menus based on location, time of day, past customer purchases, and other relevant factors to make sure that customers find these items appealing and feel like buying them.

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Importance of Data Science

In data science, you can use programming, statistics, mathematics, machine learning, and AI skills to combine data from multiple sources to provide useful insights for decision-making. Businesses can also use data science to plan future courses of action. Here are some of how data science can help you:

Predicting Future Trends: With predictive analytics, you can find possible future trends and patterns in your target market.

Making Better Decisions: Data science can help you gain actionable insights from data and make informed decisions.

Testing Your Solutions: Data modeling can help you test solutions before they are implemented.

Creating Better Products and Services: Through data science, you can design products and services that are more personalized and relevant for your customers and enjoy higher customer satisfaction and brand loyalty.

Finding Innovative Solutions and Growing Your Business: With data science, you can collect and process large volumes of data from many different sources, derive insights from it, and find innovative solutions that can help grow your business.

What is Business Intelligence?

Business intelligence is the process of collecting, managing, and analyzing data in an organization and converting it into meaningful insights that can help you make decisions.

Through business intelligence data, you can find trends and patterns in business processes such as marketing, sales, customer service, finance, and operations improve business processes, and make other changes to increase revenue and reduce costs.

You can collect data from internal and external systems, analyze it, and prepare reports and dashboards to communicate the data and the insights derived from it. You can use these insights to make both operational and strategic decisions.

Importance of Business Intelligence

Business intelligence can help you make smart, data-driven decisions and get better business results. Here are some of how you can benefit from business intelligence:

Improving Business Performance: Through business intelligence, you can gain insights into business processes, remove inefficiencies, fix problems, and improve your business performance.

Gaining Competitive Advantage: Business intelligence can help you make decisions faster than your competitors and give you a competitive advantage.

Generating Useful Reports: With the help of business intelligence processes, you can generate clear, effective reports through which users can visualize information quickly and easily.

Compiling Data from Multiple Sources: In business intelligence, you can gather data from multiple sources (internal and external) and compile them in one place. You can then analyze this data and gain useful insights from it which drive business decisions.

Understand Your Customers Better: Business intelligence can enable you to understand the buying patterns, behaviors, and preferences of your customers. This will help you design products and marketing and advertising campaigns that work best for your customers.

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Let Us Now Check Out the Top 14 Differences Between Data Science and Business Intelligence.

1. Focus Areas

While both data science and business intelligence work with data, there are some major differences in the focus areas of data science vs business intelligence. In data science, you will focus on long-term, forward-looking projects such as building a recommendation agent for a streaming service.

Netflix uses data science to recommend movies and TV shows that you may like based on what you have already watched.

You can use business intelligence when you want to focus on achieving short-term business results that rely on past performance data, e.g., through business intelligence, you can generate quarterly sales reports that can help you find information on the highest-selling products or the month with the highest sales so that you can plan sales activities accordingly.

2. Type of Analytics

Data science mostly uses predictive analytics to predict future outcomes based on which businesses can make decisions.

For instance, Subway uses predictive analytics based on past sales data (such as items purchased by customers in a given order), to determine whether raising the price of its $5 sandwich would increase sales.

However, business intelligence focuses on using descriptive analytics to help you make decisions by understanding past performance.

For instance, descriptive analytics can help you learn about age groups, buying preferences, and other characteristics of customers who have purchased from you in the past and help you plan marketing and advertising campaigns.

Hence, we can see that there is a major difference in the type of analytics used for data science vs business intelligence.

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3. Types of Data Processed

Similar to the type of analytics being different for data science vs business intelligence, the types of data processed by these fields are also different. Through data science, you can process both structured and unstructured data.

Structured data is data that can be organized in the form of tables with rows and columns. Examples of structured data include purchase amounts and the number of people who have used a specific discount code.

Unstructured data is not organized as per any particular format when it is collected. Examples of unstructured data include customer feedback, social media posts, customer reviews, and open-ended survey responses.

Business intelligence deals mainly with structured data. Examples of structured data include supply chain or production process data and sales and marketing data on promotions, pricing, and customer actions such as making a purchase or leaving a review.

4. Skills

For data science, you will need skills in programming (especially Python and R), mathematics, statistics, AI, machine learning, and other areas. The skills needed in business intelligence include data management, data analysis, data visualization, and other areas.

There is some overlap in the skills required for data science vs business intelligence. However, you will usually need more complex, advanced skills for data science than for business intelligence.

Business intelligence, however, may require more presentation and data visualization skills than data science.

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5. Tools Used

Some of the tools you will use in data science include Python, Python libraries such as NumPy, Pandas, Scikit-learn, and Seaborn, SQL, Tableau, and Power BI. You may also use AI and machine learning tools.

Business intelligence uses tools such as Excel, Tableau, Power BI, and SQL are the same as those used in data science. But it also uses other tools that are different from those used in data science.

Some of these include dashboards, online analytical processing (OLAP) tools, and ETL (extract, transform, and load) tools.

Data science uses advanced, detailed processes such as data modeling for capturing and working with data while business intelligence focuses on basic data analysis, reporting, and visualization.

Due to this, there are some differences in the tools used for data science vs business intelligence. Data science uses a larger number of complex tools while business intelligence uses fewer, simpler tools.

6. Flexibility

Another subtle difference to consider while working with data science or business intelligence is the flexibility of data science vs business intelligence in terms of processing data.

Data science is more flexible than business intelligence because while data is being gathered in the data science process, you can easily add new data sources as per business requirements.

However, in business intelligence, data sources need to be pre-planned, so you cannot add new data sources once the data-gathering process has started.

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7. Complexity

The level of complexity that can be handled by data science vs business intelligence is an important factor to consider while using them. Data science uses more advanced skills and resources than business intelligence and is more complex.

Data science tools can store and process larger volumes of data than business intelligence tools, which work with smaller datasets.

Through data science, you can forecast possible future outcomes and business requirements and work with dynamic data. Data science projects are expensive and take up more resources than business intelligence projects.

In business intelligence, you will mostly work with static data. Business intelligence projects are less expensive and take up fewer resources. It is more practical to use business intelligence for day-to-day business management activities.

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8. Uses

Data science is used by companies to predict future outcomes and scenarios that can maximize their sales, revenue, and customer retention and minimize costs and risks. On the other hand, business intelligence is used to understand and evaluate past performance.

You can also use business intelligence to identify the reasons behind past successes or failures in specific areas of business and make changes to business processes accordingly.

Lotte, an online shopping mall in South Korea used business intelligence tools to identify the issues due to which customers were abandoning shopping carts and then found ways to fix these issues.

Instead of data science, Lotte used business intelligence to gather data, find trends and patterns, and make decisions that could fix the issues.

There is also a difference in the areas of an organization that uses data science vs business intelligence insights.

In general, data science insights are used at multiple levels and departments in an organization whereas business intelligence reports are used mainly by the departments for which they were generated.

9. Stages of Data Processing

In data science, you can process data in stages such as data collection, data cleaning, exploratory data analysis (EDA), model building, and model deployment. Model building involves machine learning and other tools and statistical techniques to predict future trends and patterns based on past data.

For example, that predicts demand for a particular menu item based on outlet location, time of day, and other factors will use data modeling to make these predictions.

The stages of processing data for business intelligence include data collection, data preparation, data storage, data analysis, and data visualization.

In business intelligence, you will not focus on predicting future outcomes through modeling. Instead, you will focus on communicating past events to relevant business stakeholders.

10. Scope of Assignments

You can use data science to test a specific hypothesis or solve a specific problem by using predictive analytics and data modeling, while you can use business intelligence to give a general report on the past performance and current situation of a business by using descriptive analytics.

Hence, business intelligence has a wider scope than data science. Understanding this difference in the scope of data science vs business intelligence can also help us understand how these fields can complement each other and work together.

Due to this difference in scope, you can use data science and business intelligence to complement each other.

Business intelligence can provide data on a company’s past results. Data scientists can use this data to find areas of the business that they can focus on while carrying out predictive analytics and hypothesis testing.

11. Advantages

You can use data science to enrich the quality of data collected by businesses and create “smarter” products and services. For instance, recommendation engines on e-commerce websites can make personalized suggestions on what customers can buy next, based on what they have already purchased.

Data science can also be used in healthcare to help with the early detection of illnesses and save patient lives.

Business intelligence can be used to convert data into a form that can be understood easily by the relevant stakeholders and share this data quickly, through accurate reports.

With business intelligence, you will be able to make informed, data-driven decisions, increase revenues, and reduce the costs of making decisions based on incomplete data. Business intelligence can also help you find trends and patterns in your past performance, improve business processes, and increase productivity.

12. Disadvantages

Data science does not have a clear definition and its definition may vary according to the company or industry you may work in. It requires expertise in many fields such as computer science, mathematics, and statistics which are quite vast in themselves.

You may also require a large amount of specialized domain knowledge, depending on which sector you choose to work in. Data science projects can be very expensive and time-consuming and data collection methods can lead to privacy and security concerns.

Also, if the quality of data collected is poor or if there are errors in the processing of data, you may get unintended results in the data science process.

In business intelligence, you cannot forecast possible future outcomes and recommend actions that a company can take in the future to maximize revenue or reduce costs.

Business intelligence reports do not provide additional context, through which users can interpret their findings. Business intelligence tools can only work on smaller volumes of data and can only analyze structured data.

13. Job Roles

In data science, you can work in roles such as data scientist, data analyst, data engineer, machine learning engineer, database administrator, data architect, and data storyteller.

In roles such as data architect, machine learning engineer, and data engineer you will create and develop systems, tools, algorithms, and pipelines for collecting and analyzing data while in other roles such as data analyst, you will analyze data and derive insights from it that can solve problems.

If you are working as a data scientist, you may perform a wide range of functions from data collection to data analysis and visualization.

Some examples of job roles in business intelligence include project manager, business analyst, technical business intelligence project manager, business intelligence consultant, ETL developer, database administrator, business intelligence architect, and system administrator.

In some of these roles such as business intelligence architect, ETL developer, and business intelligence consultant you will build and maintain infrastructure and processes for collecting and storing data.

In roles such as business intelligence analyst, you will analyze data and present it through reports, visualization, and dashboards.

14. Careers

Moving beyond the job roles that exist in both categories, what does the actual work of a typical data science vs business intelligence professional look like? Data science professionals currently enjoy high demand across industries.

If you develop data science skills and expertise, you can enjoy a high-paying career. Data science careers are also quite versatile – there are plenty of data science roles that you can choose to work in across various sectors such as e-commerce, healthcare, and finance.

As a data scientist, you can enjoy prestigious, important positions in companies and work closely with key decision-makers to help them make the right decisions.

Data science roles include both tech and management-related work, so you can gain exposure to both these areas. Working in data science roles is challenging and interesting and can also improve your problem-solving and communication skills.

Business intelligence professionals also enjoy high demand and high pay packages. Your average pay as a business intelligence professional may be lower than that of your data science counterparts, but not by much.

You will play an important role in an organization in collecting data generating reports related to important areas of the business and providing useful insights that guide decision-making. With your expertise, you will be able to share business information with relevant stakeholders promptly.

You may not be as involved in decision-making as a data science professional. If you work in business intelligence, your work may be more tech-related than management-related. However, you will still get to interface with many different stakeholders, including managers regularly.

FAQs

1. Which is Better in Data Science vs. Business Intelligence?

The usefulness of data science vs business intelligence depends on the business requirement. If you are looking at a project with long-term planning and forecasting based on a variety of factors, then data science may be more relevant. For immediate, short-term, or day-to-day decisions, you can use business intelligence.

2. Can I Use Data Science and Business Intelligence Together?

Yes, definitely. Data Science and Business Intelligence can be used together. Business intelligence has a wider scope while data science has a narrower scope. So, you can use business intelligence to produce general reports on the past performance and present status of a company.

Then you can use data science to find areas to focus on, for predictive analytics, model-building, and other activities that can help businesses make better decisions.

3. Can Data Science Replace Business Intelligence?

No. Data science deals with predictive analytics and modeling that is focused on the future, while business intelligence focuses on descriptive analytics and reporting of a company’s past performance and present status.

So, the differences in focus areas and functions of data science vs business intelligence make it difficult for either of them to replace the other.

4. Can I Move From a Career in Business Intelligence to One in Data Science?

Yes. As you will already be working with data in business intelligence, it may be easier for you to move to data science than for others.

However, you may need to pick up additional skills in areas such as programming, statistical analysis techniques, machine learning, and AI and must be prepared to handle large volumes of data.

You will also need to master data science tools and programming languages such as Python, SQL, R, and Tableau.

5. What Are the Areas That Data Science and Business Intelligence Focus on?

Data Science and Business Intelligence focus on areas such as:

In business intelligence, you will focus on providing regular reports that give decision-makers an idea of how the business has done in the past and how it is doing in the present through descriptive analytics.

Data science focuses on predicting the possible outcomes of future decisions and courses of action that a business can take through predictive analytics and data modeling.

Conclusion

We hope this post helps you understand the importance of data science and business intelligence in an organization and how important factors such as types of data handles and job roles work differently.

Depending on your requirements, you can choose to use data science or business intelligence for a given task. Data science can help you look into possible future scenarios and make decisions based on data-driven insights whereas business intelligence can be helpful when you want to look at a company’s past performance and present situation and suggest process improvements and other solutions that can increase revenues and reduce costs.

In data science, you can work on long-term, forward-looking projects whereas in business intelligence, you can generate timely reports and quickly communicate information about a business to the relevant stakeholders.