BI 1.0: The Rise of Online Tools and Democratization

The groundwork for BI 1.0 was laid by three key developments that took place in the 1980s —? data warehousing, RDBMS, and personal computing.

Before data warehouses, data was stored in silos scattered across different departments. This made it difficult to get a holistic view of organizational information.?

Then data warehouses emerged as centralized repositories, consolidating data from different sources into a single, unified platform.?

RDBMS? offered a structured approach to organizing and manipulating vast amounts of data and enabled efficient storage and retrieval. This paved the way for more sophisticated data analysis techniques.

As mentioned in the previous article in our series, the increasing affordability and accessibility of personal computers in the 1980s empowered individual users to interact with data directly, fostering a culture of data exploration and analysis beyond the confines of specialized IT departments.

These technologies allowed organizational data to be consolidated, organized, and accessed.

Businesses were now ready to unlock the hidden potential of their information using powerful BI tools that emerged during the 1990s and 2000s.

Let’s dive right into it!

Business Objects Takes BI Online

Business Objects, a pioneer in the BI space, developed a BI platform that could be accessed via the Internet. They created web-based interfaces that allowed users to interact with data, generate reports, and visualize insights directly through their web browsers.?

This transition from traditional, on-premises BI systems to online platforms revolutionized how businesses accessed, used, and shared their data, making BI more accessible and flexible for users across various roles and locations.

This paved the way for more user-friendly interfaces and remote data exploration.

However, scalability remained a major challenge due to limitations in traditional databases and computing resources. These systems struggled to handle large volumes of data and complex queries because of hardware constraints, rigid data models, and concurrency issues.?

The introduction of OLAP or Online Analytical Processing in the 90’s was instrumental in solving the issue of scalability.

Online Analytical Processing (OLAP)

OLAP introduced features such as multidimensional data modeling, and pre-aggregation. It also optimized query processing, parallel processing, and incremental data loading to support efficient analysis of large and growing datasets.

OLAP organized data into multidimensional structures called cubes. These cubes allowed users to analyze data from different perspectives, known as dimensions, such as time, geography, and product categories.

It allowed users to? "slice and dice" in multiple dimensions, bringing real-time insights to dashboards.

OLAP made performing complex queries more efficient, enabled quick data retrieval, and facilitated flexible data exploration.

Additionally, the emergence of open-source database management systems like MySQL in 1995 led to increased competition and innovation in the BI industry.?

MySQL provided a robust and reliable tool for managing and organizing structured data without the expensive price tag of proprietary RDBMS offerings.?

The increased competition spurred established BI vendors to lower prices and develop more user-friendly tools. This created a shift towards democratizing access to business intelligence solutions.

Speeding Up Analytics and Data Visualization

In 2003, Microsoft's SQL Server Analysis Services (SSAS) entered the BI scene, introducing in-memory analytics.?

By storing data in RAM instead of disk drives, SSAS enabled faster processing and real-time insights, enhancing the speed and agility of BI systems.

In the same year, Tableau launched, revolutionizing data visualization with its intuitive drag-and-drop interface and interactive features like filtering and drilling down. This made data exploration more accessible to non-technical users, empowering them to derive insights from data without specialized training.

The trend of BI democratization continued as Business Objects created a unified platform for querying and reporting data.?

Business Object’s new platform allowed users to generate ad-hoc queries and create custom reports without extensive technical knowledge. This expanded the user base for BI tools, allowing more individuals across organizations to leverage data for decision-making.

From Niche to Mainstream

Google Analytics, as a free and no-code web analytics platform, emerged in 2007, making web analytics available to a wider range of businesses.

Google Analytics lowered the barrier to entry for organizations of all sizes, empowering them to leverage data analytics to understand user behavior and make informed decisions without significant financial investment or technical expertise.

This also shifted the focus of BI from traditional metrics like traffic numbers to user behavior.?

By providing insights into how users interact with websites, Google Analytics allowed businesses to adopt a more customer-centric approach and tailor their strategies to better meet the needs and preferences of their target audience.

This impressive growth highlights the transformation of BI from a niche technology to a crucial driver of business success.

The widespread adoption of BI underscored the growing recognition of the value of data-driven decision-making across industries, fueling further investment and innovation in the BI sector.

Cloud Adoption and the Road to BI 2.0:

Zoho Analytics’s free, cloud-based platform, further broadened BI adoption across various sectors by offering industry-specific editions. Cloud technology also enabled users to access their data from anywhere, paving the way for mobile BI.

These advancements set the stage for BI 2.0, characterized by increased mobility, the rise of self-service BI, and the integration of advanced analytics capabilities.

While BI 1.0 tools may not feel too impressive compared to today's sophisticated solutions, their impact on the evolution of business intelligence is undeniable. This era laid the foundation for the development of diverse BI tools catering to diverse needs and making data analysis accessible to a wider range of users

In the fourth and final installment of our History of BI series, we will delve into the quest for self-service BI in the 2.0 and 3.0 eras. We will also discuss the concerns over data privacy and ethical use that emerged with the advancements in technology.