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:
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:
Craft the problem
Organize raw data for the problem
Process raw data for analysis
Conduct in-depth analysis
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
Retail and eCommerce
Energy and utilities
Communications, Media, and Entertainment
Data Science Tools
Among some of the most popular tools used by Data Science are:
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
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:
Identifying the use case
Forming a hypothesis
What industries can benefit from Business Intelligence?
There are various industries that would like to learn from past data. For example,
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.
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