Business intelligence (BI) is an organization’s secret weapon for managing large volumes of digital data. BI transforms raw data into intelligent decisions that provide meaningful information to employees and the organization.
Imagine an online fashion store needing to decide on holiday season t-shirt samples. Without accurate data, the store could make a decision based on guesswork and intuition. However, some people need more expertise to make correct decisions, so only some can make good decisions in complex situations. This is why business intelligence is helpful for all organizations.
That’s why we’ve compiled a detailed guide explaining how business intelligence works, how it can help your organization and much more.
Table of Content
- Business Intelligence: What Is It?
- How Business Intelligence Works
- The Importance of Business Intelligence
- Features Of Business Intelligence
- Phases Of Business Intelligence Systems
- Advantages And Disadvantages of BI Systems
- Modern BI vs Traditional BI?
- Artificial Intelligence vs Business Intelligence
- Business Analytics vs Business Intelligence
- Business Intelligence Tools and Software
- Business Intelligence in The Past
- Future of Business Intelligence?
- Trends In Business Intelligence
- Business Intelligence Examples
- Conclusion
Business Intelligence: What Is It?
Modern businesses need to react quickly to problems. That’s why business intelligence (BI) helps organizations make better decisions.
Business Intelligence has become a recognized industry term, but there still needs to be a consensus on how to use it. It is the process of using expertise to turn data into information and information into knowledge. This traditional approach, therefore, encompasses all processes and structures that systematically examine data about markets, competition and business.
Business intelligence is also defined as business-oriented information technology. BI is the collection, analysis and presentation of data to make business decisions. Data visualization is usually done using dashboards. Such a definition includes the following elements.
- It focuses on decision-making,
- Prepares data,
- Data mining techniques,
- Data visualization dashboards to present business information.
Business intelligence aims to provide practical information about a company’s present and possible future and its business environment.
How Business Intelligence Works
One of the main benefits of business intelligence is the ability to collect data, which leads to discovering new insights and developing real-world applications. This is useful for businesses in many industries.
But what about the business intelligence process itself?
It may seem like it, but it’s more complicated than it looks. It can be divided into four categories: sharing, collecting, analyzing and monitoring.
Here’s an overview of each of them.
Data Collection
During the data collection process, it is essential to determine what information is helpful to your business and what goals you can achieve. For example, you can analyze the data you collect on sales or production costs to determine how to increase profits.
At this stage, you can refine the database to identify the most exciting and relevant information for your research objectives. Equally important, you can refine your brand communications to align your company’s positioning with the public’s expectations.
It is also essential to collect data and record past customer interactions, whether customer service, comments on websites or social media, etc.
Analyze The Data
Next, you need to decide what metrics you will use to analyze the data you have collected. The data should also be categorized to make it easier to compare and visualize. Use the dashboard to view the reports and update them as necessary.
Communication
Some departments in the organization are interested in specific information. Reports should be shared with relevant departments so managers and employees can share their views and get the best scores.
Observation
Data processing leads to conclusions and results that must be continuously monitored and evaluated. Therefore, it is essential to find new data sources to respond to changes in society, the market and the organization.
The Importance of Business Intelligence
Business intelligence helps organizations make better decisions by putting past and present data in a business context. BI also allows analysts to make comparisons and contrasts. Ultimately, this helps to improve organizational efficiency and effectiveness.
In addition, analysts can better identify market trends, which can lead to increased profits and sales. Proper use of data can also help in workforce planning. Here are some examples of how business intelligence can help improve data-driven decision-making in your organization:
- Identifying business intelligence strategies to increase sales
- Analyzing customer behavior
- Analyze competitors
- Tracking progress
- Improving customer behavior
- Assessing the probability of success
- Identifying market trends
- Identifying problems and issues
Features Of Business Intelligence
Let’s look at the main features of a business intelligence strategy:
Data integration
Data integration combines information from different sources such as cloud applications, databases, spreadsheets and ERP systems.
Data warehouse
A data warehouse is a central repository for the organization, management and storage of data.
Analysis And Reporting
Interactive dashboards, customized reports and visualization of information.
Advanced Analytics
Using machine learning and data mining techniques to uncover hidden patterns and trends.
Collaboration
Enabling collaboration so users can share information and analyze data.
Flexibility For Mobile Devices
Access content on a range of devices, including tablets and smartphones.
Control And Security
Ensure secure access to data with robust security protocols.
Scalability
Scalability to cope with increasing data volumes and user numbers.
Real-Time Analytics
Decision-makers have access to up-to-date data.
Predictive Analytics
Predictive analytics uses models and historical data to forecast future trends.
Self-Service Business Analytics
You don’t need to be an expert to access and analyze data yourself.
Data Visualization
Data visualization uses graphs and charts to understand data and make quick decisions.
Phases Of Business Intelligence Systems
If there is one thing that we know for sure about business intelligence or BI, it’s that it is a concept that has been around and is being extensively worked on for years now and that it is no longer just an option but a necessity for the successful operations of any given business. If you dare to not utilize business intelligence, you are essentially pushing your business towards failure with each passing day. Failure to adopt business intelligence is like deliberately losing your competitive edge which doesn’t make any sense for a veteran or for a newbie businessman. Therefore, we have taken the liberty of highlighting the process and phases of business intelligence systems development which will help a great deal in successfully integrating it into your business for streamlining its operations and activities.
The development of a business intelligence system is primarily categorized into 4 different stages namely:
Identification And Analysis
As the name suggests, this is the preliminary step where you are required to make a thorough analysis of the need for such a system in the context of your particular business. It is never recommended to rely solely on your own judgment and the available information while determining your requirement for a Business Intelligence Systems. In order to make sure that you have a broader perspective at your disposal and as much information as possible, it is a commendable idea to spare some time and perform a series of interviews with different members of the organization. Involve and engage the stakeholders, managers, employees, or even customers, get their opinions, and learn more about their expectations – this will help a great deal in building a business intelligence system that will cater to all individual requirements.
Design
Once the information has been made available and analyzed thoroughly, it is now time to move on to the next stage, that is, of designing the business intelligence system. One of the most important components of designing such a system is realizing the need for updates and evolution. The design team is not only responsible for developing a tool that caters to the current requirements, but it also has to leave room for upgrading the BI solution at a later stage so that once the requirement changes, the tool can be modified accordingly without going through the hassle of building a whole new solution from scratch. In simple words, in the designing phase of business intelligence systems development, you must ensure that the current design, its maintenance, and the opportunity for upgrades and evolution over time are not ignored.
Planning
All business intelligence solutions are not equal. There is a myriad of features and functionalities that such tools are capable of offering. However, all of the features are not applicable, or at least not valuable, for each and every single business. And this is what the planning phase is all about. It is during this phase that your team is required to put their heads together and identify the features and functionalities that are relevant to your business. It won’t be wrong to state that successful planning can eventually lead to making the tool remarkably cost-effective since the more the features, the higher will be the cost. By confining the features to those which are useful for your business, you can conveniently minimize the cost while avoiding a hit to the overall performance or productivity of the business.
Implementation
Once you have gone through the above-mentioned stages and have developed a business intelligence system, it is now time to implement it for your business and let it work its magic. Before you do that, however, it is always a fine decision to pre-test the tool and see if there are any loopholes left. This ensures that the shortcomings can be identified and addressed in time so that your business won’t have to take a hit at a later stage when fixing the error would become more complicated as well. Once implemented, the BI systems will automate several processes, analyze the available data, and generate reports, among other things, helping a great deal in improving the efficiency of your business by enabling the authorities to make informed decisions.
Now that you are familiar with the phases of business intelligence systems development, you are in a much better position to get the most out of such tools. Regardless of whether you have an in-house team of professionals working on developing a BI tool for your business or are hiring third-party experts for the same purpose, the above-mentioned information will ensure that you are not left clueless throughout the entire process. Having the right knowledge will ensure the development and implementation of an impeccable tool in the context of your particular business.
Advantages And Disadvantages of BI Systems
The advantages and disadvantages of BI systems depend on the type of organization, the type of business and the area of use.
Advantages
The advantages of business intelligence systems are
Increased Productivity
Web-based business intelligence applications make reporting more accessible, whether your business is large or small. This can save companies money, workforce and time. Employees can focus on more productive tasks, as they don’t have to spend hours manually processing data.
Greater Transparency
Companies can easily access and use large amounts of data with business intelligence. This gives them visibility into all processes and allows them to identify problems as they arise.
Task Sharing
You can use different methods to assign responsibilities within your organization to give all the activities related to a data source to a group or a person.
Managing The Overall Situation
Business intelligence solutions often include features such as dashboards and spreadsheets. Managers and stakeholders can use these to get an overview of activities.
Optimize Business Processes
Predictive analytics and benchmarking are two business processes that can be automated and optimized with these solutions. This ensures proper compliance and saves companies time and money.
No Specialist Knowledge Is Required
Business intelligence solutions are simple and easy to use, so even non-technical users can perform analysis. So, there is no need for IT experts or analysts.
Disadvantages
The disadvantages of business intelligence systems include.
High Implementation Costs
For many organizations, purchasing a BI solution can be prohibitive.
Complex Implementation
Business intelligence is complex and may require the work of a whole team of experts.
Time Is Needed
Business intelligence technology can optimize many business processes, but implementing a data warehouse system can take up to a year and a half.
How Does Modern Business Intelligence Differ from Traditional Business Intelligence?
Organizations have been using business intelligence to support strategic decision-making for decades. Today, organizations live in a dynamic, data-driven and rapidly evolving world. This rapidly changing environment requires an increasingly agile data collection, analysis and dissemination approach.
Key Changes Include
The Potential of Artificial Intelligence
In the past, data analysis was done manually by small teams of data experts. Today, business intelligence is automated and uses Artificial Intelligence and Machine Learning to analyze and process large amounts of data. AI enables real-time data mining.
Ease Of Use
In modern BI, data can be accessed and modified by any group in the organization. Previously, only the data team accessed, updated and shared data with all stakeholders.
Automated Reporting
Modern business intelligence provides predictive reporting and historical information. Traditional business analytics had few on-demand reporting capabilities, but there are many more today.
Data Structuring
While traditional BI requires formatting data before use, modern BI combines large amounts of data from disparate sources and analyzes it in real-time.
Artificial Intelligence vs Business Intelligence
Artificial Intelligence (AI) and Business Intelligence (BI) are powerful and influential technologies in today’s business world. BI analyzes historical data to provide the information needed to make informed decisions, while AI aims to mimic human cognition to make autonomous decisions. Both technologies are essential for business growth, productivity and competitive advantage. By understanding the different roles and behaviours of AI and BI, companies can use them to streamline operations, make data-driven decisions and prevail in a changing marketplace.
Here Are the Key Differences
Purpose
The primary purpose of AI is to mimic human intelligence and perform functions that usually require human thinking. In contrast, business intelligence (BI) aims to analyze historical data to provide insights that support business decision-making.
Data Analysis
By analyzing large amounts of complex statistical data, artificial intelligence (AI) can identify patterns and trends humans cannot recognize. On the other hand, BI specializes in analyzing historical data and processing structured data.
Automation
Artificial intelligence refers to automation tools that can act autonomously after analyzing data. Although AI can provide helpful information, decisions require human understanding and participation.
Capabilities
AI can make decisions and adapt to changing situations in real time, making it ideal for applications such as fraud detection and driverless cars. In contrast, business intelligence works with historical data and is typically used for non-real-time decision-making.
Business Analytics Vs Business Intelligence
The distinction between business analytics (BA) and business intelligence (BI) sometimes must be clarified.
Different people see them differently. In general, however, business analytics can be considered part of BI. Business analytics uses business intelligence, while business intelligence does not use analytics.
Two Further Differences
- Business analytics generally focuses on forecasting,
- Business intelligence (BI) focuses on visualizing data.
- BA usually does not.
Business Intelligence Tools and Software
BI tools and software are used to create tables and graphs, organize data and perform calculations. They enable users to create reports, including charts, graphs and other visualizations, and to develop and run queries on data sources.
Business intelligence tools also enable the visualization of data and the creation of interactive dashboards, infographics, maps and other graphical representations based on the data. These tools also facilitate data mining. Data mining refers to algorithms or techniques that discover relationships or patterns in large amounts of data.
Online analytical processing (OLAP) is another aspect of business intelligence software that allows data to be broken down into multiple dimensions and analyzed using multidimensional data cubes.
The Best Tools for Business Intelligence
The market for business intelligence solutions is flooded with buyers. The boom in BI software has increased the need for investment, but the selection process can be daunting and logistically challenging.
Here are five of the best business intelligence (BI) tools and software analytics platforms available.
Business Intelligence in The Past
The word “business intelligence” was first used in 1958 by Hans Peter Luhn, an IBM researcher, in his article “A Business Intelligence System.” Luhn described the potential of computing to process data for business purposes.
However, the term “business intelligence” in its current form was popularized by Howard Dresner, a former analyst at the Gartner Group, who now runs his own consultancy, Dresner Advisory Services. Dresner (1989) defines business intelligence (BI) as “concepts and techniques for improving business decisions through fact-based decision support systems.” Dresner has expanded the definition of the term to include a variety of tools, systems, and software that support the collection, storage, analysis, and access to data, enabling organizations to make informed business decisions.
Summary: The term “business intelligence” was coined by Hans Peter Luhn, and Howard Dresner developed the modern definition. Both used the term in different contexts, but both made significant contributions. Dresner’s concept has endured and significantly contributed to how organizations use data. Over time, business intelligence has evolved from simple data collection to advanced data processing. Today, the future of decision-making is shaped by advances in artificial intelligence and machine learning, which provide predictive insights.
What Is the Future of Business Intelligence?
The concept of business intelligence is not new, but it is evolving yearly. Today, businesses use automation, artificial intelligence (AI) and machine learning (ML) to improve their analytical capabilities. The combination of ML and AI in business intelligence makes analytics more accurate and easier to understand. Machine learning can analyze data in real-time, and anomalies can be identified immediately. This is very effective in fraud analysis, which requires a proactive rather than reactive approach.
The first “intelligent” business intelligence processes used natural language and high-level queries. This approach uses natural language (questions, statements, etc.) to analyze data and extract information. The system can provide relevant information without human interpretation using machine learning to understand the question.
Today, most business intelligence solutions offer some form of predictive analytics. Predictive analytics is a tool that helps to influence future strategies and decision-making by assessing the likelihood of certain future events based on past data and real-time trends. For example, predictive analytics can help optimize inventory management by forecasting when a specific product type will run out. If decisions are made early enough, more products can be ordered to balance supply and demand. This is particularly effective in the clothing sector, where demand for different collections varies from season to season.
The new technology will undoubtedly change the way businesses operate in the future, but many need to be convinced about its reliability. In machine learning, human error is always a problem, at least in the early stages of development, as human judgment determines the algorithm’s evolution. In addition, machine learning requires a lot of accurate data, and any inaccuracy in this area can cause the algorithm to perform poorly.
Trends In Business Intelligence
Business intelligence (BI) is undoubtedly the hottest topic for most organizations today. Business intelligence is taking the lead, changing how decisions are made and becoming a de facto leader. Here are five trends that are expected to impact business intelligence in 2024.
Advanced Data Analysis
Advanced analytics, powered by AI and ML, allows you to create advanced models to analyze your data and quickly extract actionable insights from your data without the need for technology. By making it easier to collect, refine and create insights using these technologies, organizations can better manage the volume and complexity of their data. Data analytics enables more organizations to use their data, ask the right questions and access information clearly and understandably. Depending on the context, analytics can be extended to provide the correct information.
Adapting To Artificial Intelligence
Web 2.0, web 3.0, collaboration software, business intelligence tools, and many other technologies that facilitate data-driven decision-making are coming together to create collaborative business intelligence. Collective business intelligence facilitates the exchange of information and reports and accelerates decision-making. Domo, QlikView, Tableau and Power BI are among enterprises’ most popular BI applications.
Managing Data Quality
Data quality is one of the biggest challenges that analysts face every day. Intelligent insights can only be derived from existing data. To maintain data integrity, data managers follow a series of processes called data quality management. These protocols are adhered to throughout the data process, from data collection to development, dissemination and analysis.
Data Security
Effective business intelligence depends on reliable and timely data. Many of the world’s largest IT companies have experienced security breaches that have prevented customers from accessing their data, putting them at risk of unauthorized access and exploitation. This has left a bad taste in the mouths of many, causing the market value of many companies to fall and many users to go. Cyber-attacks can affect businesses of all sizes. Therefore, it is essential to put security that closes as many vulnerabilities as possible. The importance of data security will remain the same in the 23rd century and the future.
Networking In the Cloud
In today’s changing world, every tool is moving to the cloud. Every element of business intelligence is moving to the cloud: computing power, analytical models, data sources, data warehouses and data modelling. As a result, many companies are benefiting from analytics in the cloud. Using many different systems in the cloud presents several challenges, such as complexity, risk and cost. There is no one-size-fits-all solution for business analytics, so an intelligent strategy is to use the connected cloud.
Business Intelligence Examples
To contribute to positive thinking, we conclude with some examples of successful business intelligence applications.
Walmart
Well-known retailers such as Walmart use business intelligence techniques to analyze how customers’ online and in-store behaviors affect their online and in-store experience. Walmart uses analytics to model its customers’ shopping habits. For example, Walmart can search for mobile phones in the app and on the Walmart website and see how many people buy a mobile phone that day. This allows Walmart to understand which days are the busiest and which days shoppers are abandoning purchases.
Lufthansa
Lufthansa, Europe’s largest airline, has automated and standardized its reporting process with Tableau. This has reduced data preparation time by 30%. Self-service BI provides valuable analysis for business users and decision-makers.
Uber
Uber uses business intelligence to understand critical aspects of its business. It uses BI algorithms to track real-time traffic conditions, travel times, driver utilization and user demand. Hotels and airlines also use dynamic pricing to meet customer demand.
Conclusion
Businesses need a reliable and up-to-date business intelligence system to stay ahead of the competition. Access to real-time data can help new or existing companies make smarter business decisions. This also applies to non-business decisions.
Business intelligence systems also enable companies to understand customer needs and expectations better. This helps to increase customer loyalty and retention, increasing turnover for the business as a whole.
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