What is Business Intelligence?

Business intelligence (BI) refers to a set of processes and technologies 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. Business intelligence tools allow decision-makers to view reports and gain specific business insights from data, rather than asking an analyst to generate reports. Traditionally, business intelligence has focused on descriptive and diagnostic reporting of historical and current business activities. Modern business intelligence can incorporate techniques such as real-time predictive analytics, AI-assisted querying, and scenario planning.

Business intelligence allows you to gain data-based views of your business operations, its staffing, its customers, and wider market trends. If you can gather data, you can perform business analytics on that data with BI.

Faster data-driven decision-making

BI provides data-driven answers to complex business questions. The ability to quickly return answers from business data allows organizations to make decisions faster and with more confidence. This can give your business strategy a competitive advantage. For example, being able to see the combined cost of a product from near-real-time supply chain component costs would allow a company to dynamically adjust the selling price.

Improved accessibility

Presented in easy-to-understand dashboards, visuals, or reports from multiple data sources and data warehouses, BI allows business users to perform tasks such as analyzing corporate performance, discovering trends, and determining areas where performance is not acceptable. Before modern business intelligence tools became widespread, business users would ask analysts to produce static reports. Analysts would then structure queries to run on conventional relational databases and report back on the data.

Higher revenues and lower costs

The right data coming into business intelligence tools, combined with the right queries, can result in higher revenues and lower costs across the organization. For example, discovering a new product line is underperforming could mean investing more in marketing, reimagining the product, or taking the product off the market.

Improved customer intelligence

You can improve customer service and product offerings by examining customer behavior data points and analyzing patterns. For example, you can query your customer data to determine whether social media posts result in inquiries, sales, or other interactions.

Artificial intelligence (AI) and machine learning (ML) for business intelligence uses advanced algorithms and deep learning techniques to analyze big data and discover patterns hidden within the data.

ML allows data scientists and business analysts to perform more advanced analysis on data than traditional BI techniques. This can help speed decision-making in business processes and uncover further insights.

The benefits of AI in BI

The benefits of AI in BI include:

●      Enhanced BI capabilities: AI provides a greater ability to identify relationships within 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: Self-learning AI can improve BI performance thanks to its ability to incorporate new information to gain better insight quality

●      Better insights: AI-enabled BI solutions help users to better identify hidden trends and provide new insights not readily apparent with legacy BI tools

Natural language processing in BI

A key ML technology within modern BI solutions is natural language processing (NLP). With this technique, AI-powered BI can incorporate insights from sentiment and information from documents, emails, and transcripts from call centers. Natural language querying (NLQ) is a specific application of NLP. With NLQ, BI users explore data by using free text, without requiring analysts to create custom dashboards or reports.

There are four phases to the business intelligence pipeline.

1. Data ingestion

Business data comes from many sources, including SaaS applications, databases, files, emails, and streaming data. Ingesting this data is the first phase in the BI pipeline. Data may be ingested in batches, chunked by time or size, or consumed as a stream.

Raw data can come in as-is, ready for immediate storage. This is known as the Extract, Load, Transform (ELT) pipeline. Another option is to transform raw data before storage. This option is known as Extract, Transform, Load (ETL) and involves data preparation, restructuring, and cleaning the data before storage.

2. Data storage and modeling

The underlying data storage technology for your BI solution depends on your ELT/ETL choice and whether you are storing structured and unstructured data together. For example, data ingested and transformed into a standardized format can be stored in a data warehouse. A data warehouse, such as Amazon Redshift, contains multiple databases. Data ingested with ELT pipelines is commonly stored in a data lake, although modern data warehouses also support ETL. A data lakehouse combines both the data warehouse and the data lake for an all-in-one storage solution.

Data modeling helps with business intelligence systems performance. For example, modeling data with a star schema can reduce query time, whereas modeling data with a snowflake schema can reduce storage space, depending on the workload and infrastructure.

Data catalogs index information across the organization, so that users can find existing relevant data to include in their queries.

3. Analysis and querying

Data warehouses support native SQL querying, but data lakes and lakehouses typically require separate querying engines over the top. For example, you can pair Amazon S3 storage with the Amazon Athena query service.

Beyond SQL queries, you will need BI software to perform advanced querying and reporting. A common pattern is pre-built reports, which will contain fixed information. For example, you might receive a weekly sales report for a department. These reports are typically configured in advance by a business analyst. Other advanced analytical techniques, known as data mining, use methods from fields such as statistics, data science, and machine learning.

Modern self-service BI solutions allow business users to view reports without waiting for an analyst to perform data analytics. Analysts can set up dashboards based on user groups to show appropriate business information. In other solutions, business users can create queries within the software to access reports without any intervention by an analyst. Modern BI solutions are incorporating natural language querying (NLQ), powered by machine learning, for analyzing data without any specific technical skills.

4. Visualization and delivery

The fourth phase of the BI pipeline is the display of queries to the end user. You will typically have access to configurable dashboards, scorecards, and reports for print. In some cases, BI tools are embedded within existing software and can show in-application reporting, such as in tool tips or side panels.

You can configure BI to output alerts, emails, notifications, and other push actions for reporting. These push-based events can happen on triggered events, on a schedule, when data passes a threshold, or when a large analysis is complete.

Depending on the solution, BI can analyze historical data, determine the cause of anomalies, predict future events, and recommend actions based on predictions.

There are four main types of business intelligence that are often combined in a single solution:

Descriptive BI

BI reports and dashboards are often structured to provide business insights into historical performance, including current results. Descriptive BI shows users what has happened, and can include dashboard components such as Key Performance Indicators (KPIs) and summary tables.

Diagnostic BI

Diagnostic BI includes a layer over descriptive BI to analyze the root cause of anomalies in the data. Diagnostic BI solutions include tools for drilling down into the data.

Predictive BI

It is important to be able to predict what will happen next in your business. Predictive BI regression, classification, and time-series forecasting, and ML modeling to predict future outcomes.

Prescriptive BI

Prescriptive BI helps you to decide what to do with your predictions. This type of BI involves techniques such as scenario modelling and optimization recommendations.

AWS has a range of services to help you create and perfect your business intelligence pipeline, from data ingestion through to visualization tools. Here are some of the services to help in your BI journey:

Amazon Redshift is a cloud data warehouse that delivers unmatched price-performance for analytics and agentic AI. Redshift powers SQL analytics on unified data across your lakehouse in Amazon SageMaker. Zero-ETL data integrations enable near real-time analytics by connecting streaming services, operational databases, and third-party enterprise applications without complex data pipelines.

Amazon QuickSight delivers AI-powered BI capabilities and dashboards within Quick, transforming your scattered data into strategic insights for everyone, enabling you to make faster decisions and achieve better business outcomes. Amazon QuickSight allows you to perform advanced data analysis in natural language with scenarios and answer 'what-if' questions with step-by-step guidance.

Amazon SageMaker Canvas allows you to add ML to your BI pipelines by building highly accurate ML models using a visual interface, no code required. With SageMaker Canvas, you can transform data at petabyte-scale, and build, evaluate, and deploy production-ready machine learning (ML) models without coding.

AWS Glue helps you discover, prepare, and integrate all your data. You can discover and connect to more than 100 diverse data sources, manage your data in a centralized data catalog, and visually create, run, and monitor data pipelines to load data into your data lakes, data warehouses, and lakehouses.

Get started with business intelligence on AWS by creating a free account today.