Comparing and contrasting business intelligence vs. data science

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Data and the resulting insights and intelligence it can provide have become a key commodity. Organizations across every sector are looking to leverage the informational assets they have on hand, as well as other accessible data, and analyze these items to support an array of important improvements and advancements. And now that so much data is being created every day, initiatives related to data science and business intelligence are providing unparalleled insights and visibility into patterns and trends that was previously unattainable.

According to current estimates, approximately 2.5 quintillion bytes of data are created every day, and about 90 percent of the world’s data was created within the past two years. This incredible data output holds considerable potential for researchers and information systems professionals.

However, it’s important to understand the key concepts and the differences between business intelligence and data science. Today, we’re taking a closer look at business intelligence and data science, as well as what students enrolled in the online Bachelor of Science in Information Systems program at the University of Alabama at Birmingham can expect to learn in course IS 417: Introduction to Business Intelligence.

Business man in a suit holding the end of a line on a graph, representative of “harnessing data.”

Business intelligence and data science: Not a new pursuit

As David Rostcheck pointed out in an article for LinkedIn Pulse, while data science and business intelligence are typically treated as more recent information management systems buzzwords, organizations have had individuals working in data and analytics roles for years. While the related role of data scientist only emerged within the past decade or so, that doesn’t mean that data science or business intelligence didn’t exist before this.

On the contrary, organizations have been looking to leverage their informational assets for for years. Technological advancements to support these initiatives have simply made these pursuits more attainable and accessible for researchers and institutions in recent years.

“Before companies had data science they still had people working in an analytic role,” Rostcheck wrote. “They called those jobs data analysis or business analysis. More recently, the discipline of business intelligence (BI) aggregated together a core of analysts who worked on extracting insight from information, often a company’s own data.”

That being said, business intelligence and data science have recently become more critical initiatives, encouraging individuals to pursue advanced higher education and other training to ensure that information science professionals have the skills necessary to complete these types of analyses.

Business intelligence vs. data science: Where they overlap

As Deloitte researchers Stefan van Duin and Bas Schmidt pointed out, there are certain similarities between business intelligence and data science. This includes the fact that both concepts seek to utilize available data and analyze it for important insights. In business settings, these insights are usually improvement-driven and positive, and can concern things like analysis to boost profit margins, enhance customer retention, and capture a new segment of the marketplace.

However, the key goals and focuses, as well as the processes involved with business intelligence and data science will differ.

Business intelligence defined

A key objective or focus of business intelligence in information systems is providing insights for the management of assets, available resources, or processes. In this way, business intelligence initiatives are usually part of planning and control activities, with the goal of establishing a single source of truth. This “one version of truth,” as van Duin and Schmidt call it, can then be used as a comparative benchmark to gauge overall performance, or the impact of improvements made.

Many business intelligence processes and use cases involve the creation of cyclical, standardized reports that encompass structured data from reliable and quality internal sources. This data is periodically pulled from internal operational systems or software platforms, and combined into a single data set, which is then analyzed through the use of data discovery and/or analysis tools or software. From here, the analyzed data set can be used to create data visualizations or to support self-service analysis to gain actionable insights.

“BI is presenting descriptive insights, humans (e.g. managers) are interpreting it, drawing conclusions and taking actions based on it,” van Duin and Schmidt explained.

Data science defined

Whereas BI is typically focused on human interpretation of analysis for management functions, data science is more geared toward gleaning insights with the help of technology or intelligent algorithms. In this way, data science requires specific tools and analytical frameworks, including elements like:

  • Descriptive analytics
  • Identifying the relationships between specific variables or events
  • Predictive analytics

In addition, where business intelligence usually encompasses the analysis of internal data sources, data science can include internal and external data, as well as unstructured sources like documents, images, or videos. In this way, while business intelligence analysis typically results in a standardized report, data science analysis will not often conclude with a report of this kind. Instead, researchers and analysts create a machine learning model, or an algorithm with the capability to learn about a concept, make a prediction, or determine the next best action for a specific initiative.

Examples of data science use cases might include determining the next best offer for a customer based on their purchase history, demographics, and other data; or leveraging a machine learning model to recognize fraudulent activities.

What can information systems students learn?

As part of the University of Alabama at Birmingham’s online IS program, students have the option to take IS 417: Introduction to Business Intelligence. This elective course covers concepts related to knowledge management and business intelligence, and how they connect to the key processes of organizational information technology.

Students will discuss and build skills involving:

  • Knowledge discovery and management
  • Knowledge generation, capturing, transferring, and sharing
  • The core IT applications and capabilities needed to support business intelligence within organizations
  • The processes in creating and using a data warehouse to support business analytics
  • The ways to best apply business intelligence knowledge and insights

Business intelligence will continue to be a key pursuit for companies and organizations in every industry sector, and it will be valuable for information systems professionals to have skills and knowledge in this area.

To find out more about what course IS 417 entails, or the other learning outcomes for the online Bachelor of Science in Information Systems program, connect with one of our enrollment advisors today.

Recommended Readings:

Computer systems administrator vs. computer systems analyst: What’s the difference?

Best cities for IS jobs

Why UAB? The benefits for online students

Sources:

UAB BSIS Degree Program

UAB BSIS Course Description

UAB IS Course Catalog

LinkedIn Pulse

Deloitte

IFL Science

Dataversity