If there’s one thing that’s common to almost all sectors of modern industry, it is Big Data. While data is the new currency of the 21st century, experts who can effectively leverage Big Data are invaluable assets of companies and organizations. Data Scientists and Business Intelligence (BI) professionals are two such valued assets for companies since they can extract meaningful insights from raw data to help boost profits and gain the upper hand over competitors. 

Yes, Data Scientists and BI Analysts both work closely to transform raw data into business-ready insights that can create value for a business. They aim to create favourable business outcomes such as boosting ROI, expanding brand reach, enhancing customer satisfaction, customer retention, and so on. In other words, Data Scientists and BI Analysts help make sense out of Big Data by delivering competitive intelligence or data-rich insights. 

But then, does it mean these two roles are the same?

No, they aren’t the same. 

Although Data Science and Business Intelligence are related fields that focus on churning value out of Big Data, they have a fair share of differences. Today, we’ll dive deep into those differences to better understand the two inter-related fields – Data Science and Business Intelligence.

Data Science vs. Business Intelligence: What Do They Mean?

At its core, Data Science is all about studying, analyzing, and interpreting voluminous data to obtain hidden insights from within by combining interdisciplinary sciences like Mathematics, Statistics, Computer Science, and Information Science. Thus, Data Science analyzes past data trends to make data-driven future predictions. Business Intelligence, on the other hand, refers to the suite of technologies and strategies a company uses to analyze business data. 

While Data Science is largely used for Predictive Analytics or Prescriptive Analytics, organizations chiefly use BI for Descriptive Analytics (reporting).

Data Science vs. Business Intelligence: What are The Major Differences?

Data Science is the game-changer of the 21st century. It has completely transformed the way businesses handle data. Earlier, BI was largely a manual domain, monitored and performed by IT professionals. However, today, thanks to Data Science technologies, most of BI and Data Analytics operations are automated – business data is stored in centralized data repositories from where data experts can extract insights and intelligence using automated tools, as and when required. In this way, Data Science has brought the core BI and Analytics operations to the forefront of the business canvas. 

Here are 6 pointers highlighting the difference between  Data Science and Business Intelligence:

1. Focus & Perspective

Like we mentioned earlier, Data Science is designed to peek into the future. It interprets past and present data to visualize what the future of a company will look like. Contrary to this, BI looks backwards on historical to deliver detailed reports, KPIs, and trends. However, unlike Data Science, BI does not depict what the insights might look like in the future through adequate visualization. 

2. Process

While Data Science is all about exploring the depths of business data and experimenting with the insights in many possible ways, traditional BI systems are static, in that they do not provide the scope to explore and experiment with how a company collects and handles the data. 

3. Data Handling

BI is built to analyze and interpret highly structured and static data, but Data Science supports high-speed, high-volume, and multi-structured complex data gathered from disparate sources. While BI is designed to understand only pre-formatted data in specific formats, Data Science technologies can effectively collect, clean, process, analyze, interpret, and visualize free-form data collected from multiple sources. 

4. Data Storage

The present business scenario is extremely dynamic. New trends, new technologies, and new methodologies constantly shape the industry as we speak. Thus, it is crucial that data, like any other enterprise asset, is flexible enough to sync with the fast-paced industry trends. This is where Data Science take the upper hand over BI – while BI systems store data in siloed in data warehouses (making it difficult to deploy across the business infrastructure), Data Science takes the central repository approach to help move data in real-time.

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5. Business Focus

Data Science and BI differ in how they deliver value to a business. Business Intelligence analyzes historical and present data to find out answers to the questions that are already on the table. However, Data Science digs into large and complex datasets to discover new and innovative questions that you did not know existed. In this way, Data Science encourages businesses to explore new opportunities, domains, and challenges with data insights. 

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6. IT-Owned vs Business-Owned

Previously, BI tools and systems were mainly controlled and managed by the IT department who extracted the intelligence manually and then forwarded it to data analysts for further interpretation. Data Science has changed this approach by collating all related actions simultaneously.

Data Science solutions and technologies are operated by data analysts, data scientists, and BI specialists who can focus on analyzing data to create actionable business predictions instead of committing their time to “IT housekeeping.”

Data Scientists vs. BI Analysts

By now it must be clear to you that Data Scientists and BI analysts are two different roles within an organization. While the former focuses on extrapolating past data to help companies mitigate potential business risks and challenges in the future, the latter focuses on interpreting past data to find answers to immediate questions and business challenges. Hence, Data Scientists and BI analysts both work hand-in-hand to equip companies with data-driven insights and help them to be prepared for the present and future business scenarios.

What unites Data Scientists and BI Analysts is their love and affinity towards data analysis. Both experts use advanced algorithms, tools, and frameworks in different capacities and degrees to empower companies with fact-based and highly accurate insights that can make or break a business.

Since Data Science and Business Intelligence are hot and trending fields in the industry right now, it pays extremely well to build Data Science and BI skills. And what’s better than enrolling in a certification course to develop industry-specific skills?

upGrad offers excellent Data Science and Business Analytics certification programs designed exclusively for both freshers and professionals:

• PG Diploma in Data Science (IIIT-B)
• Master of Science in Data Science (LJMU & IIIT-B)
• PG Certification in Data Science (IIIT-B)
• Business Analytics Certification Program 
• Global Master Certificate in Business Analytics (MSU)
• Executive PG Program in Business Analytics (LIBA)

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Each of these programs is delivered through a combination of online lectures, live sessions, and peer-to-peer learning. Students gain in-depth subject knowledge while also obtaining hands-on experience while working on case studies and assignments. upGrad promises dedicated mentor support and placement assistance to candidates to help them launch their careers successfully. 

Importance of Data Science

• Better decision-making

A data scientist is a trusted advisor and strategic decision-maker in any organisation. A data scientist works with the data and demonstrates the results to the employees and the management. The process of data allows the users to track, measure and quantify their data and bring robust decision-making into the picture.

• Better goal defining

A data scientist examines and explores the organisation’s data based on which they recommend and prescribe actions which help in improving the institution’s performance, engagement of customers, data handling, etc. 

• Opportunities identification

A data is useful in exploring the opportunities one can delve themselves into which helps identify the opportunities. The data scientists get to understand the analytics and get into asking questions to develop additional methods. This is another main difference between business intelligence and data science.

• Testing of decisions

It is important to make decisions which are impactful and are based on scientific methods. But, it is equally important to test if the decisions that were taken have been beneficial to the organisation or not. This is one of the differences between a business intelligence analyst vs data scientist. 

Importance of Business Intelligence

• Gain customer insights 

Companies can gain insights into the buying behaviour and trends of their customers. Once the trend, motivation, and buying behaviour of the customers is understood, the companies can use this information to create many relevant products which can help them to grow faster. This is another example of the data science vs business intelligence difference. 

• Better visibility 

The organisations which use business intelligence have better control over their operating procedures. The technology of business intelligence allows users to better identify the gap areas and what is lacking in their current reports.

• Sales Insight

It is essential for any organsiation to understand and grasp the real insights of their sales. The sales and marketing team constantly keep track of their customers and carry forward various strategic initiatives. It is useful in getting the analysis of the sales insights. 

• Competitive advantage 

The technology of business intelligence helps gain insights into what the customers are doing. It is also useful in making the organisation make educated decisions. 

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So, are you ready to build a career in Data Science?

Conclusion

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