Data Science VS Business Intelligence: Where to Invest?

As the head of the data science team, you always thought that your expertise in statistical analysis and machine learning would be enough to outsmart the business intelligence department, but little did you know that your biggest competition would be data science. The battlefield, Data Science vs Business Intelligence, might have you wrapped up in turmoil.

This is the question that many business owners ask themselves when they are deciding whether to hire a data scientist or an expert in business intelligence. There is no simple answer to these questions because every company has different needs.

In this article, we will discuss what data science and business intelligence are and how businesses can decide which would be best for them!

What is DLCM or Data Lifecycle Management?

Data that is initiated or processed, then created, classified, stored, accessed, processed, stored again, used, and finally destroyed is called data lifecycle management.

Typically, data in the digital world is classified as

• Structured
• Data is visible and understandable
• Very limited processing is required to interpret the data
• Weakly structured
• Poorly structured, but not random
• Have some correlation
• A little analysis is required to understand the data
• Unstructured random data
• Requires a lot of resources and processes to transform it into meaningful data

What is Data Science? 

As the demand for data-driven insights grows, the data science - business intelligence distinction becomes increasingly important for organizations looking to maximize the value of their data

Data Science is a branch of artificial intelligence. It consists of the data and information that has been collected, analyzed, and interpreted to learn something new about it. For example, using social media posts to understand what people are talking about and make decisions on how businesses should advertise or be aware of current trends.

This type of work, data science business intelligence, includes gathering data from sensors like cell phone cameras or digital grocery store checkouts, which also help us understand consumer behavior more deeply. Moreover, there is a data science course from where you can learn about it in detail.

Data Science itself consists of 5 main stages (general view):

1. Data collection
2. Saving data
3. Data processing
4. Data analysis
5. Reporting and presentation of results

What is Business Intelligence?

Business intelligence using machine learning is another term for decision support systems (DSS). These are computer programs designed to provide insight into past performance. To do so, it analyzes vast amounts of stored data and predicts future outcomes based on patterns found in historical data sets. They use predictive analytics to identify business intelligence statistics.

As a widely used application area, business intelligence has existed for quite a long time, more than three to four decades, since the end of the 20th century. As a separate discipline, it was formalized as a separate discipline sometime in the 2010s.

Types of Business Intelligence

There are four types of business analysis:

• Descriptive Analysis 

This type of analytics provides an answer to the query. “What happened?” is the main form of analytics that does not require high-end tools and can be done manually with a minimal set of tools, for example, in Excel.

• Diagnostics 

Here, the focus is on the question, “Why did this happen?” Businesses seek answers using analytical tools such as drill-down, data analysis, and correlations.

• Predictive Analytics 

People often ask about the future of business intelligence vs predictive analytics. The debate between BI vs predictive analytics has been ongoing, with each side claiming to be the superior method for making data-driven decisions.

However, experts consider predictive analysis a type of BI. Therefore, the future appears in analytics since experts associate this type of analysis with finding an answer to the question, “What will happen?” It uses statistical and mathematical tools.

Scholars further divided it into the following subcategories:

1. Predictive modeling (What’s next?)
2. Root Cause Analysis (RCA) (Why This Happened?)
3. Data identification and correlation – data mining
4. Forecast (What will happen to this trend if it continues?)
5. Monte Carlo method (Simulation determining how this will happen?)
   

• Prescriptive analysis  

This is a moment of action from a business point of view. This is where the question arises: “What should a business do?”, and primary recommendations in the style of “Do it this way.” For this purpose, optimization and modeling tools are used.

Business Intelligence vs. Data Science: What’s the Difference? 

At first glance, business intelligence analytics and data science share similarities, and professionals use them interchangeably. However, in reality, they are different from each other.

Business intelligence is specific to business-related issues such as cost, profit, equipment utilization efficiency, etc. In contrast, Data Science answers questions like how various factors, such as social, geographical, seasonal, etc. affect the business as a whole. Data Science ties data together with algorithm design and technology to provide answers to the questions posed.

Let’s define the difference between data science and business intelligence:

Recommended: ORACLE BI VS TABLEAU – WHICH BUSINESS INTELLIGENCE TOOL IS BEST FOR YOUR BUSINESS?

Data Type 

Here comes the plot line for big data. Business Intelligence primarily focuses on the utilization of structured data that is commonly housed within data repositories or discrete repositories.

Analogously, though Data Science also engages with organized data, the engineers are responsible for the manipulation of unstructured and semi-structured data, which necessitates a more significant investment of time to refine and enhance data integrity.

Deliverables 

Business Intelligence mainly focuses on creating reports and dashboards and fulfilling ad-hoc requests to provide actionable insights for decision-making.

On the other hand, Data Science emphasizes long-term and forward-looking projects, such as building models and forecasting future outcomes. While Business Intelligence prioritizes understanding the current state of a company, Data Science aims to predict future scenarios through advanced techniques and tools.

Roles & Responsibilities

Business Intelligence is the science of creating dashboards and reports that will guide the decision-making process of the business. The BI professional is the curator of data, the one who makes sense of numbers and translates them into something that can be understood by all. They put the notorious debate on business intelligence vs machine learning to rest with their skills and knowledge. 

Business Analyst uses various forms of quantitative analysis, statistical, predictive, analytical modeling, and iterative methods to interpret business data. Thus, using the results obtained, the company receives information about its past activities. The developed insights help to develop business development plans.

Important Read: Big Data Analytics Challenges and Solutions for Enterprises

Corporate business analysts work with various software packages to produce reports that executives can then use for making strategic plans. These tools include reporting programs such as Crystal Reports, analysis technologies like SPSS or SAS, statistical modeling software like R or MATLAB, and computer-based visualization applications such as Tableau Software’s Reveal product line.

On the other hand, Data Science is the poetry of numbers, the beauty in chaos. It is the practice of using data to uncover hidden patterns and make predictions about the future. He/she is the alchemist of data, the one who turns numbers into gold. They use techniques such as machine learning and statistical analysis to turn data into insights and predictions.

In addition, data scientists work with a wide range of data sources, including structured data from databases and unstructured data from social media, text, and images. They use programming languages such as Python, R, and SQL to manipulate and analyze the data. They also use tools like Jupyter, Tableau, and Hadoop to manage and visualize the data.

Process

Business Intelligence and Data Science, like two sides of a coin, are similar yet vastly different in their approach. The former, like a historian, looks back to the past to understand what has occurred, using descriptive analytics to paint a clear picture for non-technical decision-makers. 

The latter, like a detective, delve deeper into the intricacies of data, using an exploratory method to unearth hidden patterns and correlations. One may focus on the immediate, while the other looks beyond the present to predict the future.

It makes the difference between business intelligence and predictive analytics prominent. Both are essential, but their deliverables are shaped by the perspective of time, much like how a novel tells a story in the past, present, and future.

To sum up, it leverages the difference between analytics vs business intelligence vs data science. 

Salary 

As the data-driven economy continues to grow, the demand for Data Scientists continues to soar, and so does their earning potential. According to Glassdoor, while a Business Intelligence analyst may earn an average of $80,154 per year, a Data Scientist earns an average of $117,345 per year.

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Frequently Asked Questions

Will data science replace business intelligence?

While data science can be seen as a more advanced version of business intelligence, it is not intended to replace it. Both fields have their own unique strengths and are often used in conjunction with one another to gain a comprehensive understanding of data and make informed decisions.

What are the advantages of data science over business intelligence?

Data science has the following advantages: 

1. Predictive capabilities: Data science offers advanced techniques such as machine learning and predictive modeling, which allow for forecasting future outcomes and identifying potential risks and opportunities.
2. Handling of unstructured data: Data science is proficient in working with unstructured and semi-structured data, which requires more time to clean and improve data quality.
3. Advanced analytical techniques: Data science employs more advanced analytical techniques such as natural language processing, computer vision, and neural networks, which enable the extraction of insights from data that traditional BI tools cannot.
4. Automation: Data science can automate many repetitive tasks such as data cleaning, feature selection, and model selection which can save time and resources.
5. Innovation: Data science encourages innovation and experimentation which in turn can lead to new business opportunities and revenue streams.

Which is better: a data scientist or a business analyst?

Both roles have their own unique advantages, and they are often used in conjunction with one another to gain a comprehensive understanding of data and make informed decisions. The choice of which role is better depends on the specific needs and goals of an organization.