Data Science vs. Business Intelligence: Data-Driven Decisions & Key

In today’s business world, it seems like all decisions and strategies ultimately point back to one thing: data. However, how that data is being used to find value and produce insights from within the data stack is a different story. Business intelligence and data science are two terms often used interchangeably when talking about the who, what, why, and how of working with data. 

While they both appear to work with data to solve problems and drive decision-making, what’s the real difference between the two? Let’s get back to the basics by diving into the similarities and differences of each when it comes to their core functions, deliverables, and overall role as it relates to data-driven decision-making.

Business intelligence is developing and communicating strategic insights based on available business information to support decision-making. The purpose of business intelligence is to provide a clear understanding of an organization’s current and historical data. When BI was first introduced in the early 1960s, it was designed as a method of communicating information across business units. Since then, BI has evolved into advanced practices of data analysis but communication has remained at its core.

Additionally, BI is much more than processes and methods for analyzing data or answering specific business questions, it also includes the technologies behind those methods. These tools, often self-service, allow users to quickly visualize and understand business information.

Since data volumes are rapidly increasing, business intelligence is more essential than ever in providing a comprehensive snapshot of business information. This gives guidance towards informed decision-making and identifying areas of improvement, leading to greater organizational efficiency and an increased bottom line.

While there is no universally accepted definition of data science, it’s generally accepted as a field that embraces many disciplines, including statistics, advanced programming skills, and machine learning, in order to generate actionable insights from raw data. 

In simple terms, data science is the process of obtaining value from a company’s data, usually to solve complex problems. It’s important to note that data science is still developing as a field and this definition is continually evolving with time.

Data science is a guide through which companies are able to predict, prepare, and optimize their operations. Moreover, data science can be pivotal to the user experience, for many businesses data science is what allows them to offer personalized and tailored services. For instance, streaming services, such as Netflix and Hulu, are able to recommend entertainment options based on the user’s previous viewing history and taste preferences. Subscribers spend less time searching for what to watch and are able to easily find value amongst the hundreds of offerings, giving them a unique and personally curated experience. This is significant in that it increases customer retention while also enhancing the subscriber’s ease of use. 

Generally speaking, business intelligence and data science both play a key role in producing any organization’s actionable insights. So where exactly is the line between the two? When does business intelligence end and data science begin?

BI and data science vary in a number of ways, from the type of data they’re working with to project deliverables and approaches.