May 23, 2024

Business intelligence (BI) vs. data analytics vs. data science: Understand data-driven decision-making

Table of contents

In data-driven organizations, the terms 'Business Intelligence' (BI) and 'Data Analytics' are often used interchangeably, yet they serve distinct functions within an organization. Adding to the complexity, 'Data Science' — though sometimes mistaken as synonymous with BI or Data Analytics — is actually a broader field of more advanced techniques, setting it apart from the more focused applications of business intelligence and traditional data analytics.

Understanding the differences between these fields is crucial for leveraging them to drive better business outcomes. Here’s a quick breakdown and sample question before diving a little deeper into each:

  • Business intelligence focuses on analyzing past and present data to inform business decisions
    • Example data question: what are the 10 top-selling products in the last 12 months?
  • Data analytics involves analyzing raw data to identify trends and patterns for strategic insights
    • E.g., what is our inventory gap based on rolling 12-month sales?
  • Data science combines scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data
    • E.g., what will be next year’s top-selling products and how can inventory be optimized accordingly?

What is business intelligence (BI)?

Business intelligence is the practice of using data to make business decisions. That’s a pretty simplistic and broad characterization, though. More specifically, BI refers to the technologies, applications, and practices used to collect, integrate, analyze, and present business information to support better decision-making processes. 

It uses historical data (often in a data warehouse), providing insights through reporting, online analytical processing (OLAP), and alerts to inform strategic decisions based on past and present data. Business intelligence helps organizations run their day-to-day operations and make short-term decisions. BI is often associated with reporting, dashboards, and data visualization tools that help leaders understand and present trends and outcomes from accumulated data. By offering a clear view of the past and present, it helps optimize operations and enhance decision-making speed and efficiency.

Business intelligence grew out of the challenge organizations face as they create more data-driven experiences, collect more data, and strive to keep up with faster, more competitive markets. Business intelligence solves many of the challenges associated with:

  • Data overload – the sheer volume of data and lack of tools to organize and analyze it can lead to decision paralysis or inadequately informed decisions
  • Inefficient decision-making – manual approaches to finding, analyzing, and extracting insights from data are slow, inadequate, and prone to human error that delay market responses
  • Competition – insights on market trends or competitor behavior can be buried in a company’s own data, but can’t be quickly leveraged without efficient discoverability
  • Predictions and planning – trying to make strategies for the future is only as easy or valuable as the historical data available to analyze and understand efficiently
  • Fragmented data views – with data collected and stored in various places, leading to data silos, it can be difficult to get a holistic view of the business or align cross-departmental strategies
  • Performance measurement – Manual methods for measuring and visualizing performance and trends lead to inefficient and delayed insights with less precision and interconnectedness

Overall, business intelligence tends to be focused on operational insights. It’s often used by executives, managers, and operational leaders who need immediate, actionable information from data that’s curated or processed to some degree. A few examples of the leading business intelligence platforms include:

  • Tableau
  • Microsoft Power BI
  • SAP Business Objects
  • Oracle BI
  • Looker

BI is more descriptive, focusing on informing the current and historical performances. In contrast, data analytics is more predictive and prescriptive, offering deeper insights that guide future strategies. 

What is data analytics?

Data analytics, on the other hand, encompasses a broader scope than BI, including:

  • Descriptive analytics, like those found in BI dashboards
  • Predictive analytics for forecasting future trends (e.g., sales forecasting, risk assessment, and customer behavior prediction)
  • Prescriptive analytics for suggesting actions based on trends

Data analytics is the use of more complex and sophisticated techniques, including statistical analysis, predictive modeling, and machine learning, to understand data and extract more nuanced insights. These techniques help businesses with challenges like:

  • Hidden patterns – advanced analytics techniques help uncover hidden patterns not immediately obvious through simple observation or basic analysis (e.g., buying patterns that can inform more targeted marketing)
  • Operational inefficiency – with everything from workflow to sensor data, predictive and prescriptive analytics can highlight efficiencies and reduce costs (e.g., predict equipment failure or plan for maintenance)
  • Evolving customer demands – with advanced analysis of customer data, companies can personalize experiences, improve service, and develop products better aligned with their preferences
  • Making better strategic decisions – by going deeper, asking more complex questions, and layering in advanced techniques and aggregated data sources
  • Managing risk – by analyzing historical data to predict risk potential, which enables proactive measures to avoid problems or reduce the fallout
  • Maximizing profits – analyzing financial data in combination with market, operational, and other data can help organizations optimize pricing, reduce waste, and attract more customers
  • Identifying innovation opportunities – data can provide context, inspiration, and justification for product and technological innovations, so businesses make investments in the right places

The roles and use cases for data analytics are nearly endless – have data, will analyze, after all, and there’s always more data. Data analytics is often practiced by data scientists, data analysts, and IT professionals such as data engineers and database administrators. Their goal is to transform raw data into actionable insights, allowing organizations to make informed decisions, predict future trends, and gain a competitive edge in the market.

For instance, database administrators might leverage data analytics for performance optimization and query speed, capacity planning, predictive maintenance, security monitoring, compliance reporting, and more. Common data analytics tools and platforms include:

  • SAS Analytics
  • R and Python (coding languages)
  • Apache Hadoop
  • Google Analytics
  • IBM Watson Analytics
  • Microsoft Azure Machine Learning

But wait – data scientists also use data analytics? That gets confusing when you add “data science” to the comparison.

What is data science?

Here’s where things take a turn down a new path. While BI and data analytics are closely related in uncovering insights, data science goes deeper and takes an advanced approach to combine historical, predictive, and prescriptive analytics to uncover knowledge – complex patterns, strategic innovations, and essentially, new information. 

Data science utilizes scientific methods, algorithms, machine learning, data mining, computer vision, and other systems and processes to extract knowledge from structured and unstructured data. It combines aspects of statistics, mathematics, and programming to analyze complex data sets, enabling informed decision-making, predictive analytics, and data-derived discoveries.

BI and data analytics, on the other hand, (typically) stick to structured data and a shallower, tighter scope. In data science, exploratory data analysis (EDA) is used to discover, understand, and summarize data sets, often with visualizations, to help data scientists uncover new questions and opportunities. These characteristics help clarify the difference – going deeper, broader, and into less “known” territory as you go from BI to data analytics to data science. 

For modern organizations, data science helps tackle challenges, including:

  • Complex problem-solving – when a question requires more than descriptive or diagnostic analytics, data science deploys models and algorithms to make predictions and automate decisions
  • Advanced prescriptive insights – beyond the capabilities of traditional data analytics, data science can make more nuanced and detailed recommendations
  • Handling unstructured data – using techniques and processes like natural language processing (NLP) and computer vision, data science excels at processing and analyzing unstructured data (e.g., text, images, videos), which traditional BI and data analytics tools may struggle with
  • Scalability – to leverage large data sets and real-time data streams, data science emphasizes building systems that can automatically act on data insights at scale
  • Competitive advantage – providing richer, more advanced insights tan analytics alone, data science can, for example, test products, strategies, and other creations against models to measure and make recommendations
  • Artificial intelligence adoption – data science enables the development and deployment of AI models that automate, enhance, and improve the efficiency of strategic decisions, complex tasks, and time-consuming processes in ways that best benefit the business

Data science is typically used by – you guessed it – data scientists, but also by data engineers, machine learning engineers, and AI specialists who focus on building and deploying complex models and algorithms. 

Common data science tools and platforms include:

  • Python and R (coding languages)
  • Apache Spark
  • Jupyter Notebooks
  • Google Cloud AI
  • Microsoft Azure ML

Additionally, business analysts and IT leaders use data science to harness unstructured data, develop predictive models, and implement AI solutions, distinguishing it from data analytics which often focuses more on analyzing structured data to identify trends and patterns.

While BI provides a snapshot of where a business has been and where it stands, and data analytics helps chart a course for where it could go, data science predicts future trends and prescribes actionable strategies using advanced algorithms and machine learning.

But these practices are only as strong and powerful as the data pipelines that feed them. Without an advanced approach to data pipeline change management, BI, data analytics, and data science capabilities are severely limited. 

Common challenges: BI, data analytics, and data science

These data practices differ in approach, goal, and users, but they all face a common set of challenges when deployed within enterprises. First of all – how do organizations maintain data integrity, quality, and consistency throughout processes with so many touchpoints, collaborators, and transformations? Inaccuracies, incomplete data, and inconsistent formats can skew insights and lead to poor decision-making. Effective data integration strategies are essential to overcome data silos and ensure that information flows seamlessly across various departments, enhancing the organization's ability to utilize data comprehensively.

Scalability is another pressing concern, as IT teams flex to expand storage without bursting budgets or sacrificing query performance. This issue is compounded by the need for real-time data processing, which is critical for operational decision-making and timely analytics but often unsupported by traditional data storage solutions.

The growth of data and analytics are one thing, but the growing regulations surrounding data collection, use, and protection are a whole different beast. Managing security and compliance across databases was tricky enough when connected to traditional pipelines. Additional use cases and vulnerabilities, plus more complex auditing requirements, require a proactive approach to monitoring and tracking. 

Addressing these challenges requires a mix of robust data management technologies and comprehensive governance strategies to safeguard data integrity and privacy.

Leveraging DevOps to improve BI, data analytics, and data science 

Business intelligence, data analytics, and data science are all united by the need for fast, trustworthy, and reliable data. At the same time, they need methods to enforce rules and compliance standards, plus tracking and measurement for easier auditing, root-cause analysis, and workflow optimizations. 

The processes in place for changes to data and schema within various databases and other data stores tend to fall short, though. Manual, prone to human error, and delayed by inefficient pipelines, these change management methods struggle to keep up with the demands of BI, analytics, and data science. 

That’s when a DevOps approach makes sense – extending Continuous Integration and Continuous Deployment (CI/CD) workflows to data pipelines. With a database DevOps automation platform like Liquibase, these data practices can rely on a modern, streamlined, error-free data stream and structural updates. This is particularly beneficial for BI, which relies on accurate and timely data to generate insightful reports and dashboards.

Liquibase helps you find and solve problematic database changes quicker, easier, and sooner, shifting quality left in the database change management workflow. With tools to check code quality, deploy consistent workflows, and track changes in version control, you can elevate quality, integrity, and consistency to meet the demands of modern data pipelines. Liquibase’s automation capabilities streamline data integration and processing, enabling faster and more reliable analysis. 

Liquibase’s observability tools provide deep insights into data changes and pipeline performance, helping data scientists monitor and optimize their models. By automating version control and deployment processes, Liquibase allows data teams to focus more on innovation and less on managing infrastructure, driving more advanced and actionable insights.

Share on:

See Liquibase in Action

Accelerate database changes, reduce failures, and enforce governance across your pipelines.

Watch a Demo