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Business Intelligence Software Market Share
Business intelligence statistics: Business intelligence (BI) refers to the technologies and strategies that businesses use to manage business information and analyze data. It is a set of processes, technologies and architectures that transform raw data into meaningful information. This data can be used to mobilize fruitful business actions. Business analytics directly affects an organization’s strategic, tactical, and operational business decisions.
Business Intelligence Statistics: Unleashing The Advantage
Business intelligence (BI) plays a critical role in enabling organizations to use data to make informed decisions. With the development of technology and the increase in the volume of data, business intelligence has evolved into artificial intelligence, machine learning and advanced analytics. These trends are shaping the future of BI, offering organizations opportunities for automation, self-service capabilities, real-time analytics, and improved data management. Addressing these trends and the power of BI enable companies to gain deeper insights, drive innovation, and stay competitive in today’s data-driven environment. By using BI effectively, organizations can unlock the full potential of their data and make strategic decisions that will drive their success.
Business intelligence refers to the process of collecting, analyzing, and presenting data to obtain information and support informed decision-making within an organization. It includes technologies, tools and methodologies to transform raw data into actionable information.
The global business intelligence and analytics market was valued at USD 23.9 billion in 2020 and is projected to reach USD 55.48 billion by 2028, growing at a CAGR of 10.1%.
As of 2021, Microsoft Power BI has over 30% market share, followed by Tableau with approximately 19% market share and Qlik with approximately 10% market share.
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In 2020, the world generated 64.2 zettabytes (1 zettabyte = 1 trillion gigabytes) of data, and this number is projected to reach 181 zettabytes by 2025.
Tajammul Pangarkar is the Marketing Director at Prudour Pvt Ltd. Tajammul’s years of experience in mobile technology and industry research are often reflected in his insightful work. His interest lies in understanding technology trends, analyzing mobile applications and raising general awareness of technical know-how. He is a frequent contributor to numerous industry journals and forums. When he’s not pondering various happenings in the tech world, he can usually be found pursuing his favorite hobby, table tennis. Business Intelligence emerged as a response to the growing need for data analysis in decision-making. Initially relying on simple spreadsheets, it evolved into more sophisticated software that collected and analyzed data for better strategic understanding. Over time, business intelligence tools have expanded to include advanced analytics, data visualization, and real-time reporting.
Business intelligence has reached an advanced stage of evolution where it is more than just data analysis and reporting. It now incorporates elements of artificial intelligence, machine learning and predictive analytics to provide companies with deeper insights and proactive decision-making capabilities.
Business intelligence (BI) refers to the technologies, processes, and tools that enable teams to collect, analyze, and transform raw data into actionable information. In today’s data-driven business landscape, BI is extremely important as it empowers companies to make informed decisions based on evidence rather than intuition. BI aids strategic planning, improves operational efficiency, and helps organizations gain competitive advantage by proactively identifying opportunities and solving problems.
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Data analysis is key to making informed business decisions by turning raw data into meaningful insights. It uncovers patterns, trends, and correlations in data, enabling insights into customer preferences, market dynamics, and operational efficiencies. This information guides decision makers in formulating strategies, mitigating risks and capitalizing on opportunities, ultimately leading to more accurate and successful business choices.
Business intelligence (BI) dates back to the mid-20th century, when companies began using the first computer systems to store and retrieve data for basic reporting. However, this concept took shape in the 1980s and 1990s with the advent of more sophisticated database management systems.
The 2000s saw the rise of data warehouses and online analytical processing (OLAP) tools that allowed companies to perform multidimensional analysis and explore data from multiple perspectives. The 2010s brought the integration of data visualization, which made it possible to more clearly interpret complex data sets.
Today, business intelligence has reached a level where it is not only about data analysis, but also about predictive analytics, artificial intelligence and real-time reporting.
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Business intelligence tools are a set of software and technologies that collect, analyze and present data to help companies make smarter decisions. These tools take raw data from a variety of sources, such as databases and spreadsheets, and transform it into easy-to-understand visualizations, reports, and dashboards. BI tools empower teams to gain insights, identify trends, and take strategic actions for growth.
Business Intelligence tools simplify the collection, integration and transformation of data, simplifying the process of gathering information from various sources. These tools connect to various data stores such as databases, spreadsheets, and even cloud sources. They then combine this diverse data into a unified format, eliminating inconsistencies and redundancies.
Once integrated, Business Intelligence tools transform data into a structured and organized state suitable for analysis. This means cleaning up messy data, applying computation, and enriching it with additional context. This transformed data becomes a reliable basis for creating reports, visualizations and dashboards that provide insight into business performance.
These features enable companies to turn data into actionable insights, helping them make informed decisions and gain a deeper understanding of their operations and strategies.
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Business intelligence tools use visual elements such as charts, graphs, and maps to present complex data in a more understandable and engaging way. Visualizations help users quickly understand patterns, trends, and relationships in data.
These customizable displays combine different visualizations and summary data into a single view. Users interact with the dashboard, drilling down on specific data points or changing parameters to gain deeper insights.
This feature allows end users to create customized reports without relying on predefined templates. It allows users to tailor messages to their specific needs, helping them answer specific questions using real-time data.
Business Intelligence tools offer self-service capabilities that allow non-technical users to explore and analyze data independently. This reduces reliance on IT teams and empowers people across the organization to make data-driven decisions.
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Types of business intelligence tools have emerged based on different functionalities and capabilities required to analyze, visualize and interpret data in different ways to suit different business needs.
Data mining tools extract valuable patterns and insights from large data sets. They uncover hidden connections and correlations, helping companies uncover valuable insights that may not be apparent through traditional analytics.
These tools focus on creating structured reports that present data in a tabular format. They summarize information and produce regular reports, often with charts and graphs. Business Intelligence reporting tools are valuable for sharing information between organizations and maintaining a consistent view of data.
Visualization tools transform complex data into easy-to-understand visuals such as charts, graphs, and maps. They help identify patterns, trends, and relationships in data that may take time to emerge in raw numbers. These tools enable users to make faster and more informed decisions.
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Self-service BI tools enable non-technical users to analyze their data without relying on IT teams. They provide a user-friendly interface for data exploration, querying, and creating reports or dashboards. This category democratizes access to data and encourages a more data-driven culture in organizations.
OLAP tools specialize in multidimensional analysis, allowing users to explore data from different hierarchies. They provide interactive and dynamic capabilities for drilling, slicing, slicing, and summarizing data to provide a deeper understanding of business performance.
Predictive Business Intelligence and analytics tools go beyond historical data analysis by using statistical algorithms and machine learning techniques to predict future trends and outcomes. These tools help companies anticipate customer behavior, market changes, and potential opportunities or risks.
Each type of BI tool offers unique features and serves specific use cases, contributing to a comprehensive set of tools for extracting insights from data.
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Here are three of the most popular cloud-based business intelligence tools and their advantages in terms of scalability and availability:
Critical considerations for choosing business intelligence tools are based on team needs, data complexity, user experience, scalability features, and desired functionality.
This refers to the BI tool’s ability to handle growing volumes of data, growing user demands, and changing business needs. Scalable BI tools can scale data without sacrificing performance. As your organization accumulates more data or experiences more user engagement, the scalable tool ensures that data analysis and reporting processes remain efficient and operational.
It asks how well the BI tool integrates with your organization’s systems, databases, and applications. The ideal BI tool easily integrates with your current technology stack, allowing data to flow seamlessly between different systems. This integration optimizes data processing processes, prevents data placement and ensures that analytics are available
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