Understanding the difference between business intelligence and data science

The topic of business intelligence vs. data science is a recurring theme in the data analytics community. Though they occasionally get used that way, the two aren’t interchangeable. Both have distinct but equally important roles in marketing, identifying customer behavior, and understanding the overall importance of data.

Here are a few of the fundamental contrasts between business intelligence (BI) and data science (DS) and how the distinction applies to decision-makers. Understanding the BI vs DS contrast helps you understand their respective roles and why each one matters for comprehensive campaign developments.

Key concepts of business intelligence

Business intelligence is about analyzing data to understand past events. It’s backward-looking, rather than forward-focused, because you’re reviewing past trends in order to get insight into customer behavior and patterns. Here are the main elements that make up the foundation of BI.

Data collection, integration, and management in BI

There are various means of extracting and collecting data. Common methodologies include but aren’t limited to:

• Surveys and questionnaires
• Customer profiles
• Focus groups
• Customer history (e.g. purchasing and browsing trends)
• Social media likes, comments, and engagement

The above methods represent data sets from disparate sources. There also needs to be a process in place for integrating data, preferably through turnkey and automated SaaS solutions.

Data visualization and reporting in BI

Data visualization for BI can be presented using a number of summaries and reports that show real-time data from your chosen metrics. This may take the form of bar graphs, pie charts, scatter plots, or written summaries. 

Data-driven decision-making and analysis in BI

The data reveals trends in consumer behavior. Use the information to drive decisions moving forward. For example, BI analysis from past campaigns may reveal greater conversions when customer service responded to inquiries within 24 hours during a trial period. How can this information influence upcoming campaigns? For one, BI spurs the decision-making process of various B2C and B2C industries, including sectors like medical, retail, transport, manufacturing, and SaaS niches. 

Key concepts of data science

Data science’s core focus is using the data to predict future trends and probable customer behaviors. The idea is to employ the latest in data mining and modeling techniques to create forecasts. DI main concepts include the following elements.

Data acquisition, cleaning, and preparation in DS

Data is collected using mainly the same BI methods, such as surveys, questionnaires, social media, etc. DS data may also include unstructured data, such as information from interviews, written email, and video calls. The data is cleaned to remove impurities, such as duplicates, incomplete information, and outliers.

Exploratory data analysis (EDA) in DS

EDA is the employment of data visualization techniques. The data summarizes the key points, presented in an easy-to-read format via one or more graphs/charts. The findings may be stand-alone, or explore the relationship between two or more key points.

Real-world applications of DS

DS has multi-industry applications. Financial institutions, for instance, rely on DS to examine real-time big data in areas of financial market swings and fraud detection. Similarly, retailers use DS to forecast potential items that may be hot-ticket products in upcoming sales cycles.

Business analytics vs data science: a comparative look

Here’s a side-by-side BI and data science comparison for a clearer reference.

BI and DS applications

BI and DS in modern business have similar scopes and functions. BI is about identifying historical patterns over one or multiple prior campaigns. With DS, the focus is examining datasets to form hypotheses and predict forecasts to determine the most probable campaign outcomes.

Data sources and data types in BI and DS

BI data types include:

• Surveys/questionnaires
• Social media engagement
• Transaction and browsing history
• Focus groups

Note that DS data types include the above but may also include unstructured and qualitative data. This may require human analysis or advanced AI to interpret the data.

Data processing and analytics in BI and DS

BI and DS skill requirements differ slightly. The latter requires more technical skills, such as data mining, coding, and advanced statistical analysis to discern more complex data models. DS may require IT personnel, either in-house or from third-party vendors.

Benefits of business intelligence and data science

The BI vs data science debate becomes more clear when you examine their respective advantages.

Benefits of BI

BI is a profit driver. According to one study, BI adoption can yield an average of 112% ROI within five years of implementation. Other benefits include:

• More accurate reporting
• Making fact-based decisions
• Improving inventorying efficiency
• Increasing customer satisfaction and retention

Benefits of DS

The role of data in BI and DS is similar with overlapping advantages. However, DS is typically the go-to model for creating more personalized customer journeys via leveraging detailed touchpoints and engagement history. DS is also beneficial for:

• Creating products/services with cross-demographic appeal
• Risk assessment and reduction
• Developing more refined mitigation strategies
• Identifying new audience segments

Business intelligence vs data science: get the best of both worlds with Lytics

The challenges in BI and DS are many, but they can be made significantly simpler with automated solutions. Ready-to-use BI and DS tools can be major game-changers (read: Lytics’s innovative tools like Cloud Connect and Conductor implement the latest in machine learning in BI and DS).