Business Intelligence vs. Data Analytics: Key Differences

Both business intelligence (BI) and data analytics help businesses make the most of their data through insights. While the two terms are related, they’re not the same and cannot be used interchangeably. The biggest difference is that business intelligence requires data analytics to generate results, but data analytics works well even independently of business data. 

Let’s do an in-depth comparison of the two and see if the business intelligence vs. data analytics debate is justified.

What is Business Intelligence? 

Business intelligence refers to an infrastructure that helps businesses analyze and interpret data to draw meaningful insights and make better, more well-informed decisions. 

Often shortened to BI, Business Intelligence involves the following:

• Technologies like Artificial Intelligence, Machine Learning, data warehousing, and Extract, Transform, Load (ETL) processes. 

• Strategies such as data governance, quality and performance management, and security and compliance. 

• Practices such as data collection, integration, visualization, and analysis. 

By leveraging these three elements, business intelligence analysts can collect complex data, apply the necessary quality control and compliance measures, and then break it down into easily accessible and understandable formats for analysis and exploration. 

Purpose 

The primary purpose of business intelligence is to help companies make more informed business decisions. It accomplishes this by presenting high-quality data in a timely, accurate, and easy-to-understand form.

Additionally, business intelligence helps track and assess key performance indicators (KPIs) and metrics across various business functions, including operational efficiency, financial position, and customer satisfaction.

BI also serves external purposes besides its internal uses. You can use BI tools to determine market trends, conduct competitor analysis, and evaluate your position in the industry.

How Does Business Intelligence Work? 

Business intelligence processes begin with data collection and culminate with insights to support decision-making. Here’s a closer look at each stage: 

• Data Collection: Data integration tools collect data from different sources. These sources can vary depending on your business model and industry but generally include internal and external sources. 

• Data Integration: Following collection, your data is integrated into a single source of truth (SSOT). Data integration also involves data quality management measures to ensure that the data you’re using is valid, accurate, and consistent.

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• Data Analysis: Data analysis comprises the next stage, where you use techniques such as data mining for in-depth analysis and create dashboards, reports, and visualizations to share your findings. 

• Data Presentation: In the data presentation stage, you’ll use business intelligence tools to present your findings to business users and non-technical stakeholders via charts and graphs.  

• Implementation:Lastly, you can implement the insights obtained through the preceding processes into strategizing, new product development, marketing, and operational optimizations.

Examples 

Here are a few examples of how different organizational departments use BI: 

1. Marketing can use BI tools to analyze customer data and segment the market based on demographics, buying preferences, and overall behavior to create more customized marketing campaigns. 
2. The sales team can track and analyze sales data and use it to compare performance with assigned targets. They can identify trends and determine which offerings perform better than others to boost sales. 
3. BI tools can consolidate financial data from numerous sources for real-time insights into the company’s financial performance. Finance personnel can use these insights for budgeting, forecasting, and reporting. 
4. Human Resources can use BI to monitor and assess standard employee performance metrics such as attendance and productivity. 
5. Customer Support can use BI tools to analyze customer feedback from different channels (such as social media, surveys, or calls) to determine common problems and identify areas for improvement.

What is Data Analytics?

Data analytics is a technical process that uses various methods — such as data mining, cleaning, transformation, storage, modeling, and querying — to extract useful information from data.  

 There are four kinds of data analytics: 

• Descriptive Analytics: Focuses on past performance and uses historical data to analyze what has already happened. 

• Diagnostic Analytics: Examines the underlying reasons behind what happened. 

• Predictive Analytics: Uses previous data and statistical techniques to predict what will happen. 

• Prescriptive Analytics: Answers the “What can we do?” question by offering a potential course of action for the future.

Purpose 

Data analytics primarily aims to convert raw data into actionable insights that can be used for various purposes. These purposes can include, but aren’t limited to, business intelligence.  

Data analytics supports data-driven decision-making by providing insights that minimize reliance on guesswork or a trial-and-error approach.  

Additionally, data analytics helps improve customer satisfaction and the overall customer experience. Analysis of customer data can reveal customer expectations, which an organization can meet through improved customer service initiatives and fine-tuning its marketing strategies. 

Risk management and compliance is another domain where data analytics proves highly useful, enabling enterprises to identify potential risk factors and ensuring consistent compliance efforts through monitoring and analysis.

How Does Data Analytics Work?

Data analytics comprises several stages: 

• Data Collection: In the first stage, data is collected from different sources — such as databases, transaction records, social media logs, or surveys — using a combination of methods. These methods can include web scraping, automated data capture, manual data entry, or APIs. 

• Data Cleaning: Data analytics relies heavily on high-quality data. The collected data must be cleaned before it can undergo any analytical processes. Data cleaning involves rectifying errors, fixing duplicate or redundant data, and handling missing or incomplete values. Data is also standardized at this stage to ensure a uniform format. 

• Data Exploration and Visualization: Preliminary analyses reveal the data’s structure and any anomalies. This exploration also helps identify patterns in the data. Lastly, visualization converts the data into easily understandable graphs, charts, and dashboards. 

• Data Modeling: Data models can vary depending on your objectives. Statistical models can aid in hypothesis testing, inferencing, or understanding relationships. Machine learning algorithms help with pattern recognition, classification, and anomaly detection. Predictive models can assist in risk management, operational efficiency, or strategic planning. When applied to the data, the selected model facilitates predictive analytics, trend analysis, or sentiment analysis. 

• Recommendations and Implementation: Recommendations are provided to the stakeholders based on the results of data modeling and their interpretation to guide their decision-making.

Examples 

Below are a few examples to illustrate data analytics’ utility across different industries: 

• Healthcare: Using factors such as demographic data, treatment plans, and medical history, data analytics helps healthcare professionals gauge a patient’s risk of readmission. Healthcare providers can use such findings to identify high-risk patients and reevaluate interventions if necessary. 

• Finance: The finance sector uses data analytics to identify instances of fraud. Trained machine learning models analyze transaction data and flag irregular activities for follow-up and investigation. 

• Sports: Professional sports teams use data analytics to improve player performance and develop game strategies. Coaches and managers analyze game footage, player stats, and biometric data to fine-tune their training programs and play tactics. 

• Education:Data analytics in education help institutions evaluate and improve student performance. Educators analyze grades, attendance records, and engagement metrics to identify underperforming or truant students and develop targeted interventions for them.

• Telecommunications: Service providers in the telecommunications sector can predict churn by analyzing customer data such as usage patterns, billing information, and customer service records (complaints and feedback). Once identified, such customers can be given special offers to retain them.

Business Intelligence vs. Data Analytics

1. Scope

Business intelligence focuses primarily on descriptive analytics to deliver insights into business performance. It also incorporates some elements of diagnostic analytics to identify the root causes of issues. BI supports business operations and strategic decision-making by reporting, monitoring, and visualizing data. 

On the other hand, data analytics includes descriptive, diagnostic, predictive, and prescriptive analytics. It explores data to reveal patterns, predict future trends, and suggest the measures you can take. 

2. Techniques 

Common BI techniques include Online Analytical Processing (OLAP), Extract, Transform, Load (ETL) processes, data warehousing, dashboarding and reporting, and KPI tracking. 

Data analytics techniques include machine learning algorithms, statistical analysis, and predictive modeling. 

3. Data Types

 BI uses structured data. While this is primarily sourced internally from databases, data warehouses, or spreadsheets, BI processes can also use external data.

Data analytics uses structured and unstructured data from various sources, such as databases, social media, IoT devices, or text files.

4. Complexity 

BI is typically less complex because it extensively uses visualization and summarization. This makes its findings accessible to business users or non-technical stakeholders. 

In contrast, data analytics is more complex since it applies advanced statistical and machine learning techniques to the data. Users need some proficiency in statistics and data modeling to fully grasp their findings. 

5. Data Volume 

BI typically deals with vast volumes of structured data but within the constraints of data warehousing. On the other hand, data analytics can handle much bigger data volumes, including big data. 

6. Tools

 The most popular BI tools on the market include: 

• Tableau 

• Microsoft Power BI 

• QlikView 

• SAP BusinessObjects 

• IBM Cognos 

Common data analytics tools include: 

• R 

• Python (using libraries such as panda, scikit-learn, or TensorFlow) 

• SAS 

• Apache Hadoop 

• Apache Spark 

7. User Base 

BI targets business users, managers, and executives who may or may not be technically proficient. Data analytics is geared toward data scientists, analysts, and technical professionals. 

8. Implementation Time 

BI is faster to implement since it uses dashboards and reports. Data analytics can take longer since it needs data preprocessing, followed by model training and validation. 

Business Intelligence vs. Data Analytics Summarized

Should You Choose Business Intelligence or Data Analytics? 

It’s true that data analytics and business intelligence use different approaches and employ different methods to generate insights. However, it’s not an either/or situation; you don’t necessarily have to choose and forgo the other. You’ll find that despite their differences, BI and data analytics complement each other. Using a combination of the two delivers more comprehensive and valuable insights.

A comprehensive data solution streamlines access to analysis-ready data. Astera offers a unified data management platform that tackles every stage, from seamlessly integrating data from disparate sources to building robust data warehouses and API management.

With Astera, you can: 

• Effortlessly collect and unify data from various sources, ensuring the reliability and consistency needed for BI and analytics. 

• Cleanse, transform, and prepare your data for optimal analysis, saving valuable time and resources. 

• Build a secure and scalable data warehouse that is the foundation for all your data exploration needs. 

• Empower your BI and analytics tools with high-quality data, unlocking deeper insights and more informed decisions. 

All without writing a single line of code!

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