Evolution of analytics technology

Edgar F. Codd, an IBM computer scientist, publishes ‘A Relational Model of Data for Large Shared Banks’. The paper introduced a new way to model data, cross-linking tables and allowing for querying of any content within a database to efficiently utilize disk space. This served as a pivotal moment in computer science that revolutionized how data is structured, accessed and managed, leading to the widespread adoption of the relational databases that underpin much of today's digital infrastructure.

A research analyst at Gartner, Dresner described BI as an ‘umbrella term’ for data-driven decision making and analytics concepts, to differentiate from earlier IT-centric terms. It became synonymous with attempts to use data as a strategic asset within the enterprise and ultimately, empower data consumers with increased access while improving the accuracy and speed of business decisions.

The release of QlikView 7 marked a key milestone in the company’s evolution, evolving from a single-user desktop tool to a server-based web platform. This allowed it to support larger datasets often associated with Big Data, as well as simultaneous access by multiple users. By expanding access to those in various locations and using different devices, this significant change enhanced user collaboration throughout the organization, one of the most pervasive challenges of enterprise analytics at the time.

The evolution of cloud-based computing took the benefits of the web platform further. It reduced the toll of data analytics on business infrastructure and the IT teams that manage it, while removing geographical and accessibility restrictions. This drove the rise of a much more mobile, global workforce. Amazon Web Services helped propel organizations’ ability to harness data analytics by cutting out the needs to make significant investments in physical IT infrastructure.

Watson showcased the potential of AI in complex natural language processing and understanding capabilities when it defeated Ken Jennings and Brad Rutter in the gameshow Jeopardy!. The result kickstarted the AI Renaissance, sparking widespread curiosity about machine learning and opening up possibilities for AI applications in business.

In their annual “Magic Quadrant for Business Intelligence and Analytics Platforms" report, Gartner formally recognized data discovery as a key trend in business intelligence. The introduction of data discovery by Gartner marked a significant turning point in the BI and analytics industry, shifting focus towards more user-friendly, self-service tools and paving the way for the modern, data-driven business landscape we see today.

The launch of Qlik Sense set a new standard for modern analytics platforms, combining self-service capabilities with robust governance. It build on the AQL foundation with other advanced technologies such as associative indexing, intuitive design and collaborative functionalities, with the focal aim of empowering users across various roles to make informed decisions based on real-time data. Two years later, Qlik Sense Cloud was released, designed to further increase accessibility for individual users and enable virtual collaboration within small teams.

A team at Google Brain introduced the Transformer architecture in their paper: "Attention is All You Need." This architecture revolutionized natural language processing (NLP) by allowing models to process sequences of data in parallel rather than sequentially. The Transformer became foundational for subsequent models like Google’s BERT and Open AI’s GPT, leading to significant improvements in tasks such as translation, summarization and text generation. This evolution has not only transformed how organizations leverage data but also set new standards for the user experience in BI tools.