If there’s one thing that’s common to almost all sectors of the 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 deep dive 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 the 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.
Data Science vs. Business Intelligence: What are The Major Differences?
Data Science is the game-changer of the 21st century. It has completely transformed that 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 the 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.
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.
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How does Data Science differ from Business Intelligence?
The following chart illustrates some of the prominent differences between Data Science and Business Intelligence.
1. Data Science understands the hidden patterns in data with the help of statistics, probability, and other mathematical concepts.
2. It processes both structured as well as unstructured data.
3. Its main focus is on the future as it predicts what can happen in the coming era.
4. Scientific methods are used.
5. Tools are BigML, SAS, MATLAB, etc.
3. Its focus is on past and present as it analyzes the trend that has been followed.
4. Analytical methods are used.
5. Tools are Tableau, PowerBI, BiGEval, etc
What are the skills necessary for Data Science and Business Analysis?
Data Science and Business Analysis are the 2 most prominent sectors that manipulate the data for the greater good. But there is a huge gap between the demand and supply of both data scientists and business analysts as there is a lack of awareness of what skills are necessary to pursue these sectors.
The following are some of the necessary skills to master the data science and business intelligence tools:
1. Statistics and Probability
2. Multivariate Calculus
3. Programming Language
4. Data Visualization
5. Machine Learning and Deep Learning
1. Data Analysis
2. Problem Solving
3. Industry Knowledge
4. Communication Skills
5. Business Acumen
How is business intelligence as a career option?
Business Intelligence is considered to be one of the emerging sectors in the perspective of career and growth. Business consultants play a key role in decision making in business processes at all levels.
As industries are dealing with a huge amount of data, which is larger than ever, business analysis becomes a necessity. BI tools increase the organization’s growth exponentially thereby increasing the demand for business analysts.
The average salary for a business analyst is around 7-13 LPA for freshers. Experienced professionals can earn up to 22 LPA and make a good living for themselves out of it.
The growth report shows that the demand in this field will grow in the coming years and hence the competition is also going to be tougher.