Data Science vs. Business Intelligence

Both data science and business intelligence (BI) focus on data processing. However, there are some key differences to be aware of. For instance, data science can predict trends, while business intelligence provides analysis of past events. Moreover, data science uses a more technical skill set compared to business intelligence’s practical approach. To make these concepts sound much easier for you, Sencury defines each and compares them in a nutshell.

What is Data Science?

To initiate the process of data science, there is a need for concrete data sets. So, data scientists collect and maintain data beforehand. Afterward, this data is processed via data mining, modeling, model training, and summarization. To receive future forecasts, data scientists use machine learning, descriptive analytics, and other analytics tools.

According to Harvard Business School, Data Science requires you to:

• make hypotheses

• gather data while running experiments

• assess the quality of data

• clean and optimize datasets

• organize and structure data for analysis

While analyzing specific datasets (raw data) it is possible to spot patterns. These patterns are the basis for forming future predictions. This kind of analysis requires text mining, regression, descriptive, and predictive analytics. Being helpful in gaining future insights based on their data, many industries turn to data science services. Businesses can prosper with such a convenient approach at their disposal. For example, by analyzing customer preferences, it is possible to predict future market trends and develop new products that will be close to 100% successful.

Data Scientists need 6 steps to do the work:

1. Craft the problem

2. Organize raw data for the problem

3. Process raw data for analysis

4. Analyze data

5. Conduct in-depth analysis

6. Report analysis results

What industries can benefit from Data Science?

Here’s the list of industries that find Data Science a successful approach:

• Healthcare and Pharmaceuticals

• Finance

• Retail and eCommerce

• Manufacturing

• Automotive

• Energy and utilities

• Government

• Construction

• Communications, Media, and Entertainment

Data Science Tools

Among some of the most popular tools used by Data Science are:

• Python

• PyTorch

• Pandas

• TensorFlow

• Scikit-learn

• Project Jupyter

• DataRobot

• Databricks

• Keras

• Apache Spark

• Matplotlib

• Apache Hadoop

• NumPy

• Orange

• MATLAB

• SAS

• Julia

• BigML

What are the advantages of using Data Science for businesses?

Data Science advances your organization’s growth as it:

• improves business predictions

• cooperates with business intelligence

• helps in sales and marketing

• increases information security

• interprets complex data

• helps in decision-making

• automates recruitment processes

These are the main facts to know about Data Science. Therefore, let’s proceed with Business Intelligence and discuss it next.

What is Business Intelligence?

The process of business intelligence requires proactiveness. To drive changes, business leaders need to process and analyze data to obtain actionable insights. For instance, you may use your business's key performance indicators (KPIs) for strengths and weaknesses identification. With this knowledge, it becomes easier to improve operating efficiency and the company's business performance as a whole.

Business Intelligence requires the following:

• organizing data collection process

• storing the data obtained

• analyzing data

• generating reports for further insights

Data can truly support an organization's decision-making. Great improvements in BI technology are equal to improvements in speed, efficiency, and effectiveness. For example, transformational effects in BI and significant changes can be achieved with the help of automation and data visualization.

Business Intelligence analysts require 7 steps to perform the process. These are:

1. Identifying the use case

2. Forming a hypothesis

3. Examining data

4. Collecting data

5. Drawing conclusions

6. Presenting findings

7. Implementing solutions

What industries can benefit from Business Intelligence?

There are various industries that would like to learn from past data. For example,

• Retail

• Telecommunication

• Fashion

• Human resources

• Healthcare

• Fintech and Banking

• Sales and marketing

Business Intelligence Tools

These are the most used BI tools on the market. Some of them are already in use by Data Science.

What are the advantages of using Business Intelligence for businesses?

Among the advantages businesses can obtain there are:

• implementation in different industries and departments

• usage of Artificial Intelligence and Predictive Analytics

• provision of 24/7 real-time monitoring and data access

• exploitation for both data analysts and business users

Now, let’s compare the two approaches – Data Science vs Business Intelligence, to understand what makes them different.

Key differences between Data Science vs. Business Intelligence

The practice of Data Science as well as Business Intelligence turns data into information, which is new knowledge. This information is crucial for business decision-making. However, the concepts are still different. Let's visualize these differences.

As you can see from the table above, the differences are in the main goal, skills needed to perform Data Science and Business Intelligence processes, gather data, maintain it, and manage, and the complexity of business operations.

Sencury’s Experience

Our company offers customers Sencury’s top Data Engineering Services. We carry out both Data Science and Business Intelligence methods choosing the best toolsets on the market. Sencury provides smart personalized recommendations, transforms company data, and enhances organizational performance. Our team focuses on customer requirements and, also, can suggest or advise on the best-fit solutions per your case.

Choose Sencury to get quality company data analysis. Today, data insights are the best way to initiate business growth and become a leader in the competitor market.