What Is Business Intelligence?
Business Intelligence (BI) refers to a set of software capabilities that allows businesses to access, analyze and develop actionable insights from data to make business decisions. Typically, BI tools present information on user-friendly dashboards and data visualizations that graph and chart key metrics. While previously a function of tech or IT teams that required specialized expertise, modern business intelligence tools bring data and predictive analytics capabilities into the hands of decision-makers, allowing them to develop reports and gain specific business insights. Traditionally, business intelligence has focused on descriptive and diagnostic reporting of historical and current business activities.
Why Is Business Intelligence important?
Modern BI provides real time data driven answers to complex business questions. Presented in easy-to-understand dashboards, visuals, or reports from multiple data sources and data warehouses, BI allows users to analyze corporate performance, discover trends and determine areas where performance is not acceptable. Typically, it is structured to provide business insights into historical performance, including current results. Depending on the solution, users can pose questions using natural languages without the need for programmatic input. Some areas where companies use BI can include:
- ROI: An intelligent business understanding derived from BI helps organizations optimize performance and return on investment through business analytics.
- Customer experience: To better understand customer preferences, buying trends and behavior to improve customer service and facilitate targeted marketing.
- Monitor business performance: The use of data analysis to develop insights into company performance to continually improve operations
Traditional business intelligence techniques focus on historical data, providing answers to questions such as what happened and why did it happen. To achieve this, analysts structure queries that run on conventional relational databases to produce static reports.
Artificial intelligence (AI) and machine learning (ML) for business intelligence uses algorithms and deep learning techniques to analyze big data and discover patterns hidden within the data. AI allows data scientists and business analysts to automate manual processes to extract data, better understand trends, to forecast, and generate new BI reports. It is also useful for providing new insights that traditional BI techniques cannot uncover. Another area where AI comes into play within BI is for natural language processing, where AI-powered BI can extract sentiment and information from documents, emails and transcripts from call centers. BI users can dig deeper into data without requiring analysts to create custom dashboards or reports.
How is Artificial Intelligence powering Business Intelligence?
Using AI-driven business intelligence can enhance outcomes and provide deeper insights. More specifically, AI allows users to effectively analyze large amounts of data, including structured and unstructured data types. AI-driven applications can highlight priority areas more effectively than standard BI. Benefits include:
- Enhanced BI capabilities: AI provides a greater ability to understand relationships between data, nuances, outliers, and hidden trends.
- More informed decision making: The predictive capabilities of AI-driven BI allow users to more easily identify trends and make more informed decisions.
- Proactive decisions: AI can quickly highlight trends contained within current data, allowing analysts to identify these trends early on and make real-time proactive decisions
- Smart adaptive BI: The machine learning capabilities of AI can improve BI performance thanks to AI's ability to discover analyses and recommendations that give the best results.
- Better insights: AI-enabled BI solutions help users to better identify hidden trends and provide new insights not readily apparent with legacy BI tools.
What are the benefits of Artificial Intelligence in Business Intelligence?
Using AI-driven business intelligence can enhance outcomes and provide deeper insights. More specifically, AI allows users to effectively analyze large amounts of data, including structured and unstructured data types. AI-driven applications can highlight priority areas more effectively than standard BI. Benefits include:
- Enhanced BI capabilities: AI provides a greater ability to understand relationships between data, nuances, outliers, and hidden trends.
- More informed decision making: The predictive capabilities of AI-driven BI allow users to more easily identify trends and make more informed decisions.
- Proactive decisions: AI can quickly highlight trends contained within current data, allowing analysts to identify these trends early on and make real-time proactive decisions
- Smart adaptive BI: The machine learning capabilities of AI can improve BI performance thanks to AI's ability to discover analyses and recommendations that give the best results.
- Better insights: AI-enabled BI solutions help users to better identify hidden trends and provide new insights not readily apparent with legacy BI tools.
How can AWS Help with AI in Business Intelligence?
Amazon SageMaker Canvas expands access to machine learning by providing business analysts with a visual interface that allows them to generate accurate ML predictions on their own—without requiring any ML experience or having to write a single line of code. With Amazon SageMaker Canvas, you can access ready-to-use models or create custom models to extract information and generate predictions from thousands of documents, images, and lines of text in minutes.
In addition, business analysts can leverage ML predictions generated in SageMaker Canvas, enrich them with interactive dashboards in Amazon QuickSight, which provides unified BI at hyperscale, and use the insights from these dashboards in their day-to-day business decisions. With QuickSight, all users can meet varying analytic needs from the same source of truth through modern interactive dashboards, paginated reports, embedded analytics, natural language queries, and ML insights.
To get started with SageMaker Canvas and QuickSight, see the workshop.
Next Steps on AWS
