Step into the world of Business Intelligence (BI)! In this blog, we address typical beginner issues such as learning data jargon and selecting proper tools to get into the realm of Business Intelligence. We will make Business Intelligence easy to understand with straightforward, uncomplicated explanations and actionable steps, breaking complicated things down into simpler ones. Want to learn more? Explore our detailed Business Intelligence Course Syllabus!
Introduction to Business Intelligence Tutorial
Business Intelligence (BI) is the superpower of doing business. It’s all about taking raw, disjointed data and turning it into easily understood, actionable information that informs you to make better decisions. Think about operating a lemonade stand without knowing which flavor to sell or when people are busiest. That’s what businesses without BI do!
This business intelligence tutorial will guide you from the fundamentals to more sophisticated ideas in BI, such as trending tools like Power BI software, the BI market, and how BI business intelligence can be a game-changer. We will provide real-time examples to make it easy to comprehend.
What is Business Intelligence (BI)?
Fundamentally, business intelligence (BI intelligence) is a collection of techniques, processes, and technologies utilised by an enterprise to analyze data and provide actionable information to support decision-making. Consider it as a process:
- Collect Data: Collecting data from different sources (sales, marketing, operations, customer feedback, etc.).
- Process and Store Data: Cleaning, transforming, and storing the data in a structured fashion, usually in a data warehouse.
- Analyze Data: Utilizing analytical methods to discover patterns, trends, and insights.
- Visualize and Report: Displaying these insights in an understandable format, typically in the form of dashboards and reports.
- Act on Insights: Employing these insights to make better business decisions.
The primary aim of BI is to deliver a picture of business operations in total, assisting organizations with knowing “what happened” and “why it happened” and how this will guide “what will happen” and “what do we do.”
Why is Business Intelligence Important?
In the era of big data, the BI sector is booming because it provides massive value:
- Better Decision-Making: From instinct to data-driven decisions.
- Discover Trends and Patterns: Discovering opportunities and threats in advance.
- Better Operational Efficiency: Detecting bottlenecks and maximizing processes.
- Greater Customer Insight: Catering products, services, and marketing to customer requirements.
- Competitive Edge: Remaining ahead by reacting fast to market developments.
- Cost Savings: Finding areas of wastage and restructuring resource usage.
- Real-time Analysis: Getting timely information to act promptly.
The business intelligence market is growing rapidly with estimated growth continuing over the years. This development is fueled by the amount of data growing exponentially and the need for organizations, both large and small, like Small and Medium-sized Enterprises (SMEs), to use data for strategic decision-making.
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Key Components of a BI System
A strong BI system normally consists of a number of connected pieces:
Data Sources: This piece provides all your raw information.
- Transactional databases (sales, inventory)
- CRM (Customer Relationship Management) systems
- ERP (Enterprise Resource Planning) systems
- Marketing platforms (Google Analytics, social media)
- Flat files (Excel, CSV)
- External data (market research, demographic information)
ETL (Extract, Transform, Load) Processes: The cornerstone of data preparation.
- Extract: Retrieving data from multiple sources.
- Transform: Cleaning, standardizing, and converting the data into a working state. This includes dealing with missing values, error corrections, and making it consistent.
- Load: Loading transformed data into a data warehouse or data mart.
Data Warehouse/Data Mart:
- A data warehouse is a single, centralized repository of integrated data from one or more diverse sources. It is intended for reporting and analysis purposes, not transactional processing.
- A data mart is a part of a data warehouse, generally aimed at a specific business unit or department (e.g., sales data mart, marketing data mart).
OLAP (Online Analytical Processing):
- OLAP tools provide rapid, multi-dimensional analysis of data in data warehouses. They enable users to “slice and dice” data, drill down into details, and pivot data to see different angles.
Data Mining:
- This entails the application of sophisticated statistical methods and algorithms in machine learning to uncover concealed patterns, correlations, and anomalies within massive datasets. This aids predictive modelling and forecasting.
Reporting and Data Visualization:
This is where data insights are made tangible. BI software (business intelligence software, BI tools, or business analytics software) is employed to develop interactive dashboards and reports. These graphics, such as graphs, maps, and charts, simplify complicated information at a glance.
Popular Business Intelligence Software
The BI market has a broad range of robust BI software solutions. Below are some of the most popular business intelligence software options, with an emphasis on Microsoft Power BI:
Microsoft Power BI
Power BI is a popular interactive data visualization BI product from Microsoft. It allows you to connect to hundreds of data sources, shape data, and create engaging reports and dashboards. Its ease of use and good integration with other Microsoft products make it extremely popular.
Major Features of Power BI:
- Power Query: To connect to different data sources and for ETL operations.
- Power Pivot: For modeling data and creating tables’ relationships.
- Power View: For developing interactive data visualizations.
- Power Map: For displaying geographical data.
- Power Q&A: A natural language query engine where users can question their data using plain English.
- DAX (Data Analysis Expressions): A formula language for developing custom calculations (measures and calculated columns).
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Getting Started with Power BI:
Step 1: Installation and Interface (Beginner)
Download Power BI Desktop from the Microsoft website. It is free!
Once installed, you’ll find three views in the left pane:
- Report View: Where you build visualizations and lay out your reports.
- Data View: Where you can view your raw data, review tables, and sort/filter.
- Model View: Where you work with relationships between various tables.
Step 2: Connecting to Data (Beginner)
Power BI can connect to literally tons of data sources. Let’s try connecting to an Excel file, which is a good starting point for beginners.
Example: Linking to an Excel File
- Launch Power BI Desktop.
- On the Home tab, click “Get Data.”
- Choose “Excel Workbook” and click “Connect.”
- Navigate to your Excel file and click “Open.”
- In the Navigator window, choose the sheets or tables you wish to import. You’ll see a preview of the data.
- You have two choices:
- Load: Load the data directly into Power BI.
- Transform Data: Open the Power Query Editor to clean and transform your data. As a beginner, it is always good to transform data.
Power Query Data Transformation
Power Query Editor is where you shape and clean your data. It is an important step to ensure data quality.
Typical Transformation Steps:
- Delete Columns: Right-click the column header and choose “Remove.”
- Rename Columns: Double-click the column header to rename.
- Change Data Type: Click on the icon beside the column title (e.g., “ABC” for text, “123” for numbers) and select the right data type.
- Remove Rows: Utilize “Remove Rows” under the Home tab (e.g., remove empty rows, duplicate rows).
- Filter Rows: Employ the filter icon on the column title to eliminate unnecessary data.
- Split Column: Divide a column into two columns using a delimiter (e.g., divide “City, State” into “City” and “State”).
- Merge Queries: Merge two tables on a shared column (similar to a VLOOKUP in Excel).
- Append Queries: Stack one table on top of another (if they have comparable columns).
Example: Cleaning Sales Data
Say you have a sales dataset with a “Product_Category” column that has inconsistent spellings such as “Electronics”, “electronics”, “Eletronics”.
- In Power Query Editor, select the “Product_Category” column.
- Go to the “Transform” tab.
- Click “Format” and then “Capitalize Each Word” or “Lowercase” to standardize the text.
- You can also use “Replace Values” to correct specific misspellings.
To load the data into Power BI Desktop after transformations are complete, select “Close & Apply” from the Home tab.
Data Modeling
Data modeling consists of establishing relationships among various tables in your dataset. This enables you to perform multiple-table analysis.
Example: Sales Data with Product and Customer Tables
Suppose you have three tables:
- Sales: OrderID, ProductID, CustomerID, SaleAmount, Date
- Products: ProductID, ProductName, Category
- Customers: CustomerID, CustomerName, Region
Steps for Data Modeling:
- Switch to the Model View in Power BI Desktop.
- Power BI will generally auto-detect relationships, but you can also do them manually.
- Drag ProductID from the Sales table to ProductID in the Products table to establish a relationship. Do the same for CustomerID between Customers and Sales.
- Make sure the cardinality (one-to-many, one-to-one) and filter direction are in order. By default, sales tables usually reside on the “many” side of a one-to-many relationship with dimension tables (Products, Customers).
Creating Visualizations
This is where you make your data come alive!
Steps to Create a Bar Chart:
- Switch to Report View.
- Click the Stacked Bar Chart icon in the “Visualizations” pane on the right.
- Drag Category from the table for Products to the “Axis” field.
- Drag SaleAmount from the table for Sales to the “Values” field.
- You will see at once a bar chart of sales by product category.
Example: Sales Trend over Time (Line Chart)
- Choose the Line Chart icon.
- Drag Date from the Sales table into the “X-axis” field. Power BI will create an automatic date hierarchy (Year, Quarter, Month, Day).
- Drag SaleAmount from the Sales table into the “Y-axis” field.
- You may click the “Expand All Down One Level in the Hierarchy” icon to drill down from Year to Quarter, then Month.
Creating Dashboards
A dashboard is one page that tells a story using a set of visuals, key performance indicators (KPIs), and interactive controls. Reports are built in Power BI Desktop, and dashboards are usually developed in the Power BI service (the cloud version).
How to Make a Basic Dashboard in Power BI Service:
- Publish your report from Power BI Desktop to the Power BI service (File > Publish > Publish to Power BI).
- In the Power BI service (app.powerbi.com), you open your published report.
- Hover over a visual you would like to add to your dashboard. You will see a “Pin visual” icon (a pushpin). Click on it.
- Select to pin it to a “New dashboard” or an “Existing dashboard.” Name your new dashboard.
- Repeat for other key visuals.
- On the dashboard, tiles can be resized and rearranged.
Real-time Example: Sales Performance Dashboard (Monitoring)
A sales manager might have a dashboard with:
- A card displaying Total Sales YTD (Year-to-Date).
- A line chart displaying Sales Trend by Month.
- A bar chart displaying Sales by Region.
- A table displaying Top 10 Products by Sales.
- A gauge displaying Sales vs. Target.
This dashboard would be refreshed frequently, providing the manager with a real-time barometer of sales performance and enabling them to immediately spot areas of concern.
DAX (Data Analysis Expressions)
DAX is a formula language that can be used to develop custom calculations (measures and calculated columns) in Power BI. It’s akin to Excel formulas but optimized for analytical data.
Example: Calculating Sales Growth Percentage
To determine the percentage growth compared to last month, you may require a measure.
Code snippet
Sales Growth % =
DIVIDE (
ALL ( Sales[SaleAmount] )
– CALCULATE ( ALL ( Sales[SaleAmount] ), DATEADD ( ‘Date'[Date], -1, MONTH ) ),
CALCULATE ( ALL ( Sales[SaleAmount] ), DATEADD ( ‘Date'[Date], -1, MONTH ) )
)
Explanation:
- SUM.C(Sales[SaleAmount]): Computes total sales for the current context (e.g., today’s month).
- CALCULATE.C(SUM.C(Sales[SaleAmount]), DATEADD(‘Date'[Date], -1, MONTH)): Calculates total sales for last month by using the DATEADD function to move back the date context.
- DIVIDE: Divides safely the difference by last month’s sales to obtain the percentage, avoiding division by zero.
You would then add this Sales Growth % measure to your visualizations to observe trends.
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Advanced BI Concepts
Real-time Business Intelligence (RTBI)
Legacy BI typically depends on batch processing, updating data periodically (daily, weekly). Real-time business intelligence (RTBI) means accessing, analyzing, and reacting to data as it is created.
Examples:
- E-commerce: One of the biggest meal delivery services uses RTBI to track digital marketing campaign activity, customer engagement, and churn in real-time. This enables them to A/B test offers and personalize messaging in real-time, resulting in higher conversion rates.
- Logistics: DHL employs RTBI with IoT sensors to monitor temperature-controlled shipment of pharmaceuticals. Container temperature and route real-time updates are achieved through dashboards, enabling them to anticipate potential problems instead of reacting.
- Financial Trading: Real-time dashboards are utilized by stock traders to view market movement and stock prices to determine real-time investment opportunities.
Predictive and Prescriptive Analytics
While BI is historically oriented towards descriptive (what occurred) and diagnostic (why did it occur) analytics, it’s more and more being combined with advanced analytics:1
- Predictive Analytics: Leverages past data and statistical models to predict future consequences. For instance, forecasting customer churn or future sales.
- Prescriptive Analytics: Suggests specific actions to meet desired results. For instance, prescriptively suggesting the optimal pricing strategy to yield maximum profit.
Embedded BI
Embedded BI couples BI functionality directly with business applications or processes. That is, users don’t have to exit their native application to gain insight; the data is available there in context.
Example: A CRM application may include embedded dashboards with sales performance per customer, or an HR application could include employee productivity statistics in the platform itself.
Cloud-Based BI Solutions
Numerous organizations are moving toward cloud-based BI solutions since they are scalable, flexible, and have lower infrastructure costs. Power BI, for example, has cloud services that accompany its desktop version.
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The Business Intelligence Market Landscape
The business intelligence market is competitive and dynamic, and there are different BI tools and vendors. Some of the key trends include:
- AI and Machine Learning Integration: BI solutions are embracing AI to provide automated insights, NLP to support conversational analytics (such as Power BI Q&A), and stronger predictive analytics.
- Self-Service BI: Enabling business users (not only data analysts) to retrieve, analyze, and create reports with minimal IT intervention. This makes data accessible to everyone and accelerates decision-making.
- Data Storytelling: Breaking away from merely showcasing charts to revealing the “why” of the data, enabling users to comprehend the context and meaning.
- Data Governance and Security: As data volumes grow and regulation increases, maintaining data quality, privacy, and security is the top priority in the BI domain.
- Mobile BI: Accessing dashboards and reports via mobile devices for on-the-go decision-making.
Major vendors in the BI market are Microsoft (Power BI), Tableau, Qlik, SAP, Oracle, and IBM, among others. All have special strengths and serve various organizational demands and budgets.
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Conclusion
Business intelligence is no longer an indulgence but a requirement for any organization that seeks to succeed in today’s economy. Through insight into the basics of data gathering, changing, analyzing, and visualizing, and with the help of robust BI tools such as Power BI, new users can rapidly get to work on pulling useful insights from data.
The development of the BI market toward real-time analytics, AI, and self-service enables the future of data-driven decision-making to be both more accessible and powerful than ever. Welcome to the world of BI business intelligence and empower yourself to make better, more informed decisions through our Business Intelligence Training in Chennai.