As with every other field it is applied to, Artificial Intelligence (AI) has a way of improving on the status quo, and it is not any different when it comes to Business Intelligence (BI). While they both sound similar, they are far apart in their actual meanings and applications. BI benefits from AI in areas like task automation and increased efficiency. The following highlight some BI processes impacted by AI: automation, data governance, pipeline documentation, writing DAX measures, and SQL query optimization.
Automating BI Processes
Some repetitive tasks in the BI workflow can be automated leveraging AI. Tasks such as file management, data extraction, report generation, and even more complex workflows like pipeline orchestration can be simplified with AI models and scripts. For instance, a Python script can be used to automate a File Renaming process. Such scripts as this can be applied to various business tasks such as batch file processing, data cleansing, or even data extraction from APIs.
Writing DAX Measures for BI Dashboards
DAX (Data Analysis Expressions) Measures are an essential part of BI Dashboards as most metrics visualized for stakeholders are built on these Measures. Using proper descriptions in generative AI prompts, Report Developers are able to leverage the code generation capabilities of AI to compute different types of Measures, from the very basic examples like a DAX Measure that sums up a Sales to complex ones like an Hierarchical ranking of Sales for local governments within states.
Documenting Data Pipelines
Documentation is easily a strong point for AI, especially because it is context based text generation and not nearly as complex to achieve as code generation. For data pipeline documentation, once the flow of the ETL (Extraction, Transformation and Loading) process has been determined, and the AI has been fed with a prompt of a good description of the process, the result is a well structured documentation of the process that can then be tweaked to the Data Engineer and Business’ taste.
Data Governance with AI
Similar to the pipeline documentation instance, guidelines and policies that regulate how data is used, accessed, stored, secured and managed in an organization can be generated using AI.
This important aspect of BI ensures that data used in decision-making is accurate and compliant with regulations.Key areas of data governance like Data Quality Management, Data Privacy and Security and Master Data Management (MDM) are being enhanced in today’s BI workflow by the use of AI.
Clean Up SQL Query
Writing clean and efficient SQL queries is non-negotiable in the BI workflow. Queries just like DAX Measures also vary from basic to complex and the code generation capabilities of AI allow it to handle various scenarios seamlessly. From generating queries based on user descriptions, to correcting runtime errors and fine tuning existing queries to speed up their execution, AI finds its use in different ways in ensuring that SQL queries are written efficiently. A common use case can be that of restructuring a query to enhance readability and improving the query for better performance.
Evidently, Artificial Intelligence is significantly impacting the way Business Intelligence is done by improving on areas like automating tasks, enhancing decision-making, and improving the overall efficiency of data workflows. Writing DAX Measure and automated Python scripts, pipeline documentation, SQL query optimization are just a few of the many other ways in which AI is bettering BI. In all, AI saves time, helps reduce errors, increases efficiency, all of which at the end of the day make for better reporting and improved data-driven decision-making. I look forward to a future where fully functional and interactive BI dashboards would be created using AI with no human interference.
Conclusively, with all the many advantages posed by AI it is always important to follow your organization’s guidelines while using it.
Adebiyi Adebayo, Data Scientist, writes from Lagos.
