A Comprehensive Guide to Business Intelligence and Data Visualization 
Data & Analytics
A Comprehensive Guide to Business Intelligence and Data Visualization 
A Comprehensive Guide to Business Intelligence and Data Visualization 
Data & Analytics

A Comprehensive Guide to Business Intelligence and Data Visualization 

The business intelligence (BI) and data visualization sectors are growing at an unprecedent rate. According to statistics, they could reach market values of $54.27 billion and $22.13 billion, respectively, by 2030. For businesses, this growth means interpreting complex datasets and uncovering essential insights will only become much easier. By using BI and data visualization tools, companies will be able to make more informed decisions to stay ahead of competition. 

Global business intelligence market

In this article, we’ll reveal the specific roles of enterprise business intelligence and data visualization. We will also highlight the reasons and methods for integrating them into your business strategy to maintain competitiveness. For those new to the field, familiarize yourself with essential terminology by clicking here for a glossary of terms. 

Let’s begin. 

Introduction to Business Intelligence 

Let’s start by defining business intelligence. 

Business intelligence (BI) is using technology and tools to analyze, process, and present raw data to support informed decision-making within an organization. 

benefits of bi data visualization

The origin of BI can be traced to the 1960s and 70s with Decision Support Systems (DSS) and Executive Information Systems (EIS). Then came data warehousing and online analytical processing (OLAP) in the 80s and 90s. These laid the foundation for modern BI. The 2000s introduced self-service BI data visualization tools and cloud-based analytics, democratizing access to data insights. 

Now, let’s break down why BI matters for today’s businesses: 

  1. Data Integration. BI pulls data from different sources across a company—like databases, spreadsheets, and applications—and brings it together in one place for analysis. 
  2. Data Analysis. It makes it easier for organizations to sift through large amounts of data, and find trends, patterns, correlations, and anomalies that can guide decision-making. 
  3. Reporting and Visualization. BI platforms create reports, dashboards, and visualizations that make complex data easy to understand. This helps everyone involved quickly grasp insights and make informed choices. 
  4. Predictive Analytics. Organizations can harness the power of BI to use advanced analytics techniques, such as predictive modeling and data mining, forecasting future trends based on past data. 
  5. Performance Management. BI keeps track of key performance indicators (KPIs) and metrics, helping monitor progress toward company goals and objectives. 
  6. Decision Support. Decision-makers can leverage BI for timely, accurate, and relevant information to support strategic, tactical, and day-to-day decisions.

Overall, BI is a game-changer for modern businesses. It’s all about using data to innovate, make better decisions, and stay ahead in today’s fast-paced business world. 

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The Role of Data in Business Intelligence 

Data is possibly the most important component of any Business Intelligence (BI), serving as its foundation. It’s the raw material from which businesses derive insights for informed decision-making. Collected from sources like internal databases and real-time feeds, data undergoes processing, cleaning, and transformation before analytical tools uncover valuable insights. 

There are two main types of data used in business intelligence. 

structured vs unstructured data

Structured Data 

Structured data follows specific patterns and resides in databases or warehouses. Examples include customer records and financial metrics. Analyzing structured data involves traditional techniques like SQL queries. The distinction between databases vs data warehouses lies in their functionality and scope. The former is for transactional use while the later is for analytical purposes. 

Unstructured Data 

Unstructured data can be complex and need more organization. Examples include text documents, social media feeds, and multimedia files. Despite the complexity, it is increasingly popular, accounting for approximately 90% of global data generated. 

Integration of Structured and Unstructured Data 

These days, data extends beyond structured and unstructured. Combining both structured and unstructured in semi-structured data offers a comprehensive view of operations. This integration enables data-driven decisions across departments and functions. 

Core Components of Business Intelligence Systems 

Understanding the important components of BI systems is important for organizations that want to leverage data effectively. Let’s break them down. 

core components of business intelligence system
  • Data Sources. This includes internal databases, spreadsheets, social media, and CRM platforms. BI systems collect and verify information from these sources. 
  • Data Warehouses. Data is stored and organized in a key repository for easy access and integration. Ensuring consistency across datasets is one of the benefits of data warehouses
  • Data Integration. Combining information from multiple sources directly into a single source of truth ensures compatibility and consistency. This is where data extraction, transformation, and loading (ETL) or real-time data integration happens. The good thing is that there are dozens of ETL tools to help. 
  • Data Analysis. This involves using statistical strategies and algorithms to examine records and uncover patterns and trends for informed decisions. This component covers descriptive, diagnostic, predictive, and prescriptive analytics. 
  • Reporting. This is a visual representation of analyzed data. They allow users to monitor key performance indicators (KPIs) and track progress towards business objectives. 
  • Data Governance. This component encompasses policies and procedures that govern the use of data to ensure security, compliance, and privacy. 
  • Data Mining. Here is where hidden patterns from datasets are extracted to reveal valuable insights using statistical techniques and predictive modeling. 
  • Data Visualization. Visualization helps translate complex data into easy-to-understand visual representations using charts, graphs, and infographics that enhance data comprehension. 
  • Data Security. This component involves implementing measures such as encryption, access controls, and audits to protect data from unauthorized access and breaches. 

Data Visualization: Turning Numbers into Knowledge 

Data visualization, an essential part of enterprise business intelligence, simplifies data and presents it visually to help businesses make informed decisions. Patterns and trends that might be overlooked in raw data will be revealed by converting data into charts, tables, graphs, and infographics. 

Science is there to back it up. The brain processes visuals faster – about 60,000 times faster than textual content! We also remember 65% of what we see compared to just 10% of what we hear. So, presenting data visually engages the brain’s visual processing power, making information less difficult to recognize and recall. In this way, decision-making is faster and more certain. 

Here are some key benefits. 

  1. Clear Insight. Visuals help spot patterns and trends easily, making data more understandable. 
  2. Interactivity. Data visualization allows users to play with the data, like filtering or sorting, improving analysis. 
  3. Sharing Insights. It enables easy sharing of insights, promoting collaboration and informed decisions. 
  4. User-Friendly. Visuals use colors, shapes, and sizes to convey information intuitively, making data more accessible. 

Principles of Effective Data Visualization 

Data visualization can be either effective or ineffective. 

  • Effective Visualization. A clear bar chart showing monthly sales trends, with labeled axes and a concise title, facilitating quick interpretation. 
  • Ineffective Visualization. A cluttered pie chart displaying multiple categories with similar colors, lacking clear labels and a legend. 

It is crucial to ensure that your visualizations are effective to facilitate better decision-making. Here are 6 design principles that can help. 

  • Clarity. Keep things crystal clear. Cut out unnecessary clutter, use concise titles and labels, and arrange your data logically. This ensures your audience gets the point without any confusion. 
  • Simplicity. Make complex data easy to digest. Trim down the info, skip the jargon, and use straightforward visuals. The simpler, the better. 
  • Accuracy. Ensure that your data is represented truthfully and without distortion. Double-check your sources, validate your findings, and avoid misleading visualizations. 
  • Efficiency. Get to the point fast. Use suitable chart types, trim out unnecessary details, and offer quick summaries. Save time and get the message across ASAP. 
  • Aesthetics. Looks matter. Choose pleasing colors, clean typography, and maintain visual harmony. Good aesthetics make data more engaging and memorable. 
  • Accessibility. Make sure everyone can access and use the data. Use alt text, offer keyboard navigation, and consider color schemes for those with visual impairments. 

Tools and Technologies in BI and Data Visualization 

When looking to leverage BI and data visualization, there are dozens of tools to choose from. Here, we review some of the top options on the market. 

Power BI 

Microsoft Power BI is a top tool for business intelligence and data visualization. Easily connect, transform, and visualize data with its user-friendly interface. Generate charts, graphs, and dashboards effortlessly. 

Key Features: 

  • Insights from Large Datasets 
  • Custom Visualizations with R and Python 
  • Data Transformation with Power Query 
  • Mobile App Access 
  • Integration with Microsoft Products 


Tableau is a powerful BI tool known for interactive analysis and stunning visualizations. It’s flexible with options for desktop, cloud, or on-site deployment. Tableau offers visualization options like Bar, Line, Area, Pie Charts, Treemaps, Scatter Plots, and more. 

Key Features: 

  • Real-time Analytics 
  • Intuitive Dashboard Creation 
  • Versatile Data Source Connectivity 
  • Role-based Permissions 
  • Simple Sharing and Collaboration 
  • Mobile Accessibility 

Zoho Analytics 

Zoho Analytics is a comprehensive BI and data visualization tool. It offers drag-and-drop report generation functionality and AI-driven insights with smooth integration. Choose from a variety of data integration and robust collaborative features visualization options, such as pie charts, line charts, and bar graphs. 

Key Features: 

  • Visual Analysis 
  • Augmented Analytics 
  • Collaborative Analytics 
  • Data Storytelling 
  • Mobility 

Looker Studio 

Looker Studio is an advanced analytics, customization, and real-time collaboration BI tool. Formerly Google Data Studio, Looker Studio provides a wide range of visualization options including Cartesian, Pie/Donut, Progression Charts, Text/Tables, Maps, and custom visualizations. 

Key Features: 

  • Advanced Filtering 
  • LookML Modeling 
  • Google Services Integration 
  • Automated Scheduling 
  • Support for 50+ SQLs and Databases 
  • Looker Studio API 
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Integrating BI and Data Visualization in Business Strategy 

Integrating business intelligence (BI) and data visualization into your enterprise strategy is essential. Here’s how to do it successfully. 

  • Align Objectives. Start by identifying your key strategic goals. Then, see how BI and data visualization can support these objectives. This ensures that your analytics efforts are targeted at addressing important business challenges and opportunities. 
  • Engage Stakeholders. Get various departments involved in the BI implementation process. Understanding their needs and requirements allows you to avoid unexpected issues and find practical solutions. According to a recent poll of almost 80% of CEOs, involving stakeholders in digital transformation helped them recognize issues and come up with workable solutions. Collaboration and communication are key. 
  • Promote User Adoption. Make sure your employees are comfortable using enterprise business intelligence solutions. Offer training programs, user-friendly interfaces, and ongoing support. Highlighting success stories and demonstrating the value of BI through tangible outcomes can encourage broader adoption throughout the organization. 
Power BI Tableu Zoho Analytics Looker Studio 
Cost Affordable with free and premium plans. Higher cost with various licensing options. Competitive pricing with free and premium tiers. Free to use, with Google Cloud storage costs. 
Ease of Use User-friendly interface suitable for beginners and professionals. Steep learning curve but powerful for data analysts. Intuitive for users with drag-and-drop functionality. User-friendly with seamless integration into Google ecosystem. 
Feature Set -Insights from large datasets 
– Custom visualizations 
– Data transformation 
– Mobile app access 
-Integration with Microsoft products 
– Real-time analytics 
– Intuitive dashboard creation 
– Versatile data source connectivity 
– Role-based permissions 
– Mobile accessibility 
-Visual analysis 
– Augmented analytics 
– Collaborative analytics 
– Data storytelling 
– Mobility 
– Advanced filtering 
– LookML modeling 
– Google services integration 
– Automated scheduling 
– Support for 50+ SQLs and databases 
Typical Use Cases Best for businesses embedded in the Microsoft ecosystem looking for a comprehensive BI solution. Ideal for data-heavy organizations requiring deep analytics and complex visualizations. Suitable for small to medium businesses needing affordable, easy-to-use analytics solutions. Perfect for users heavily invested in Google’s ecosystem needing straightforward, collaborative data analysis. 

Data Visualization Best Practices 

Creating effective data visualizations is key in business intelligence (BI) platforms. These visuals help users understand complex data and make smart decisions. To make your visualizations impactful, follow these tips: 

data visualization best practices
  • Choose the Right Chart. Pick the best chart type for your data. Line charts for trends, bar charts for comparisons, etc. 
  • Use Color Wisely. Make sure colors make sense and are clear, especially for color-blind users. 
  • Add Data Labels. Include labels for clarity, like names, values, or dates. 
  • Optimize Font. Use readable font sizes and styles, avoiding extremes. 
  • Be Consistent. Stick to the same formatting style for a cohesive look. 
  • Test Thoroughly. Make sure your visualization works well across different devices and accurately represents your data. 

It is also important to consider your target audience when doing data visualization. 

  • Expert Users: Use advanced chart types for complex data. 
  • Non-Experts: Keep it simple with clear labels and familiar charts. 
  • Mobile Users: Optimize for smaller screens with larger fonts and simplified designs. 

By following these tips and considering your audience, you’ll create data visualizations that drive informed decisions in BI platforms. 


Business Intelligence (BI) and data visualization are vital in today’s data-focused world. They assist in uncovering valuable insights from data, improving efficiency, and customer experiences, and gaining a competitive advantage. 

At Symphony Solutions, we know firsthand how powerful enterprise business intelligence and data visualization can be. Our data analytics services have empowered numerous clients to leverage their data for growth. Partner with us to unlock your data’s potential and make smart decisions for success.  

Glossary of Key Technical Terms 

BI (Business Intelligence) 

BI is like a detective tool for businesses, analyzing data from sources like sales reports to answer questions such as “Why are sales dropping?” Example: A coffee shop uses BI to decide to add oat milk based on customer preference trends. 

OLAP (Online Analytical Processing) 

OLAP lets you analyze data from many perspectives quickly. Example: A principal uses OLAP to examine test scores by subject and teacher to identify areas for improvement. 

SQL (Structured Query Language) 

SQL is the language used to query databases. Example: A pizza shop owner uses SQL to find out which pizza toppings are most popular. 

ETL (Extract, Transform, Load) 

ETL moves and refines data from multiple sources into a single database. Example: A business combines online and in-store sales data to analyze total sales figures. 

Data Warehouse 

A data warehouse stores a company’s historical data in an organized manner for analysis. Example: A retailer stores years of purchase data to predict future trends. 

Data Mining 

Data mining finds hidden patterns in large data sets. Example: Netflix uses data mining to recommend shows based on viewing patterns. 

Data Visualization 

Data visualization makes complex data easy to understand through visuals like charts. Example: A fitness app uses graphs to show your exercise patterns. 

Predictive Analytics 

Predictive analytics uses past data to forecast future events. Example: A clothing brand predicts increased sweater sales in November and stocks up accordingly. 

KPI (Key Performance Indicator) 

KPIs measure how well a business is achieving its goals. Example: A blogger tracks monthly website visitors to gauge readership growth. 

CRM (Customer Relationship Management) 

CRM systems manage all customer interactions and data. Example: A real estate agent uses CRM to track client preferences and viewed properties. 


Enterprise business intelligence refers to the use of business intelligence (BI) tools, techniques, and processes on an organization-wide scale to support decision-making and strategic planning across various departments and functions.

The four main concepts of business intelligence are data analysis, data mining, data visualization, and decision-making. 

Some data visualization best practices include: 

  • Understand your audience 
  • Choose the right type of visualization 
  • Simplify complex data 
  • Ensure clarity and readability 
  • Maintain consistency 
  • Provide context and annotations 
  • Test and iterate 

The three rules of data visualization are: 

  1. Simplicity: Keep visualizations easy to understand by avoiding unnecessary clutter. 
  2. Clarity: Ensure visuals represent data patterns and relationships. 
  3. Accuracy: Maintain accurate representation of data to avoid misinterpretation. 
Yevhen Tanchik
About the Author

Yevhen Tanchik

Lead Data Engineer
Yevhen Tanchik excels in delivering valuable insights to businesses through advanced analysis and visualization techniques. With expertise in utilizing visualization software, Yevhen guides analysis, extracts meaningful implications, and synthesizes complex data into clear and impactful communications. His role as a lead data engineer allows him to leverage his skills to provide data-driven solutions that drive business success and empower organizations to make informed decisions.
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