Business intelligence
Analyze data to gain actionable insights and improve decision making for your business
Introduction
What is business intelligence?
Business intelligence (BI) is an umbrella term for the technology that enables data preparation, data mining, data management, and data visualization. Business intelligence tools and processes allow end users to identify actionable information from raw data, facilitating data-driven decision-making within organizations across various industries.
There are a number of BI tools in the marketplace, which aid business users in analyzing performance metrics and extracting insights in real time. These tools focus on self-service capabilities, reducing IT dependencies and enabling decision-makers to recognize performance gaps, market trends, or new revenue opportunities more quickly. BI applications are commonly used to make informed business decisions, advancing a company’s position within the marketplace.
User adoption of BI software continues to increase at a rapid pace, especially as customers migrate workloads to the cloud. Vendors are increasingly supportive of different cloud platform providers, leading to more SaaS-based BI solutions and subscription-based pricing models.
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Resources
BI versus business analytics
Business intelligence versus business analytics
The term business intelligence is commonly used in association with business analytics, and while there is significant overlap between the two areas, business intelligence focuses more narrowly on what is happening in your business and why, while business analytics more broadly includes solutions that help you leverage that insight to plan for the future. Business intelligence uses descriptive analytics to formulate conclusions about historical and current performance, providing context around changes in key performance indicators (KPIs).
Business analytics and business intelligence are inclusive of prescriptive and predictive analytics practices, which help advise decision-makers on potential future outcomes. Both BI and business analytics solutions enable stakeholders to make better decisions, and these should be viewed as complementary to one another.
Business analytics and data analytics tend to be used interchangeably. But business analytics is a merely a subset of data analytics, as the scope of data analytics can refer to any analysis of data. Business analytics focuses on discovering information which can improve business decision-making.
Key components of BI software
BI platforms are expected to have dashboarding, ad hoc reporting and data visualization capabilities. To stay competitive, business intelligence systems are integrating machine learning and AI. At the core, they rely on data warehouses, ETL, and OLAP.
Data warehouses and data marts
After data is pre-processed and aggregated, it is fed into one central repository, such as a data warehouse or data mart, which supports business analytics and reporting tools. For larger data sets, businesses typically use an open source data storage framework called Apache Hadoop.
ETL
BI solutions rely on a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system. ETL is short for three steps in this process, which are extract, transform and load.
OLAP
This technology extracts big data from relational tables and reorganizes it into a multidimensional format, enabling fast processing and insightful data analysis. OLAP is an acronym which stands for online analytical processing.
Emerging BI capabilities
Natural language processing
Natural language processing (NLP) refers to the branch of artificial intelligence that enables computers to understand text and spoken words in a similar way to human beings. BI vendors have started to incorporate this technology into their products, allowing users to access business information in new ways. Imagine typing a question into your self-service BI or asking it directly, “which product has created the most revenue this month?” versus searching through the data for that answer yourself.
AI-assisted data preparation
It is highly valuable for BI solutions to provide a one-stop-shop across the entire analytics journey — and that starts with data. Automatically identifying any problems in the data and suggesting ways to combine different data sources allows users to adapt and customize datasets and dashboards as needed. The process makes it faster and easier for a business user to cleanse, refine and combine data modules so that they end up with exactly the data they need to drive powerful visualizations and uncover new insights.
Smart reporting
Reporting and dashboarding are at the heart of a modern approach to analytics. Organizations rely on regular, structured reporting to run their business. These formal reports collect and disseminate the crucial details that support good decision-making, and they provide jumping-off points for further exploration of trends, threats and opportunities. AI features embedded in modern BI solutions learn from users to make it easier to identify visualizations that have the highest impact for discovering and communicating insights.
Use cases
Business intelligence use cases
Sales
BI software is used to view sales data in real time and quickly pivot to adapt to change. Specialized data visualizations and dashboards can easily be shared and standardized across the business. BI software is also beneficial for monitoring pipeline quality, managing compensation, profitability analysis, and territory assignment.
Supply chain
BI systems can be used to improve business operations, replacing Microsoft Excel and allowing companies to identify delays and problem areas in the shipping process. Supply chain use cases also extend to material sourcing, compliance, and injury reports.
Marketing
BI tools are commonly used to track impact of content and campaigns across various audience segments. Organizations use this data to understand the effect of marketing on business performance. Marketers also benefit from the ability of BI tools to help with monitoring customer preferences, customer lifetime value, and profitability.
BI and IBM
Business intelligence and IBM
IBM’s history with business intelligence can be dated back to 1958 in a paper published by IBM researcher, Hans Peter Luhn (PDF, 631 KB). His research helped establish methods for creating some of IBM’s early analytics platforms. While IBM has continued to evolve its portfolio of products to support business intelligence strategies, his work is undoubtedly foundational to our legacy in this space.
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