Evolution of Business Intelligence. Strategic approach.
The essay is written for the project DataAcademy.kz
In recent years, Business Intelligence has become an essential concept for any modern organization. Companies have begun to view enterprise data as an asset and have started treating it accordingly. The idea of becoming a data-driven organization has gained significant momentum. But how far are we from becoming truly data-driven? What issues must we resolve today to create strategic innovations that will drive business growth tomorrow?
The latest developments in the field of AI are set to significantly impact the landscape of Business Intelligence in the near future. Business Intelligence systems are expected to evolve into AI-powered platforms capable of autonomously making informed decisions based on data. However, today, numerous issues hinder the implementation of AI-driven Business Intelligence. For instance, decentralized and ungoverned data without a semantic layer can lead to conflicting metrics in various reports. The use of various systems can, in some instances, hinder proper data integration, especially when not all sources provide API access. The lack of proper data models and data quality checks prevents companies from enabling self-service analytics capabilities. As a result, companies are not harnessing the full potential of the data they collect.
For organizations aiming to become data-driven, it is crucial to address the aforementioned issues. This implies that the first step in becoming data-driven involves incorporating AI into the data governance process, once the fundamental groundwork has been established. For instance, AI can assist with data quality management by automatically detecting and correcting inconsistencies and errors in data. This would greatly improve the reliability and accuracy of the data, which is essential for making informed decisions.
Once the high-quality data is in place, the data is properly modeled, and a semantic layer has been created, the second step of the transformation is the implementation of AI-powered automated business insights that will drive data-informed decision-making. Moreover, strategically, AI-powered Business Intelligence systems must use not only first-party data but also third-party data to evaluate the market conditions for decision propositions. This can greatly enhance the company’s speed, leading to more autonomous business decisions made by AI.
It is worth highlighting the role of a semantic layer in AI-powered Business Intelligence platforms. A semantic layer would greatly enhance the implementation of Large Language Models (LLM) into Business Intelligence systems. In contrast, LLM by itself will be at the forefront of self-service analytics, helping users without technical expertise to analyze data and make business decisions.
The third and critical step in developing an AI-powered Business Intelligence platform is embracing Explainable AI and Ethical AI. For the business leaders of tomorrow, understanding the inner workings of the models within the platform will be crucial. The Explainable AI concept not only helps build trust among stakeholders by making AI decision-making processes transparent and understandable but also ensures that AI models do not, even unintentionally, reproduce real-world biases and discrimination.
In conclusion, the strategic development of AI-powered Business Intelligence platforms with self-service analytics capabilities is a multi-step process that hinges on addressing key data governance issues, implementing AI-powered automated business insights, and embracing the principles of Explainable and Ethical AI.
