Business intelligence (BI) can unlock the value of data for your enterprise by providing data analytics reporting and visualization capabilities. But businesses seeking to modernize with the best data technologies and methods may find themselves wading in a sea of jargon. This guide introduces the concepts business leaders need to know to take advantage of business intelligence.
What is business intelligence?
BI is a collection of software tools, supporting infrastructure, and data practices designed to leverage enterprise data to improve business decision-making. BI helps managers and stakeholders see trends in their data and derive insights, both visually and through summarization, often using statistical methods. BI comprises many functions, including data discovery, data mining, reporting, visualization, and dashboarding.
The term “business intelligence” has existed for more than a century. It originally referred to the employment of information to increase business profits and improve enterprise performance. BI as it exists today is an offshoot of decision support systems (DSS), which originated in the 1960s.
BI involves using and analyzing data throughout an organization. It’s a necessary part of any modern enterprise’s data infrastructure, as it provides operational, tactical, and strategic insights.
Understanding business intelligence
BI covers an array of software and ideas, and understanding its scope involves becoming familiar with other data-related terms, such as data analytics, data warehousing, data lake, data modeling, ETL, and data integration.
Data analytics and business intelligence
Data analytics consists of the processes used to examine and obtain insights from data. While BI evolved out of DSS, data analytics has more in common with data mining and statistical modeling.
BI and data analytics are related but distinct concepts. BI is focused on data visualization and reporting, and some BI tools can be used effectively by any authorized manager or stakeholder in an enterprise. Data analytics is more often the purview of data scientists, who instead work with programming languages like Python or R, and take advantage of their computational, statistical, and subject matter expertise to answer difficult business questions.
For example, BI could graph statistics about customer support calls for any timespan, aggregated along arbitrary dimensions (customer demographics, time of day, etc.). A production anomaly detection system that automatically sends alerts to technicians based on tests performed in real time on this same data would fall within the realm of data analytics.
A data warehouse is a centralized repository for clean data. The information contained in a data warehouse is intended for consumption by applications and systems that perform analysis. BI tools use data after it’s loaded into the data warehouse to generate insights that help managers improve decision-making.
Within a data warehouse, raw sales data might be replicated to a staging area, joined with historical data, merged with customer information from another database, then used for reports and visualizations on management dashboards.
Data lakes are centralized repositories like data warehouses, but they are designed to store unstructured data in its native, raw format. Many modern BI tools can act on raw data, and keeping the data unstructured allows for the flexibility to run a range of analytics, as well as the freedom to transform and structure the data to suit an enterprise’s other needs.
Data modeling is used to standardize data into consistent, governable, and reusable formats and structures. Data stored in a data warehouse is stored in modeled form, while data in a data lake is only modeled after it’s retrieved, based on requirements specific to a particular project or analysis.
Data modeling is important for business intelligence because it creates a clear picture of where data is located, what it means, and how it can be used by various systems and tools.
ETL and data integration
ETL and data integration are closely related to the terms we’ve already discussed, and to each other. ETL is short for “extract, transform, load” and describes the process for ingesting data into data warehouses. Information is extracted from sources, transformed to fit a data model, then loaded into the warehouse. ETL’s modern cousin, ELT, loads data before transforming it, and it is particularly useful when the data’s destination is a cloud data warehouse. Data integration describes the process of combining many disparate sources of data into a processing and storage system useful for generating business intelligence and data analytics.
How BI works
BI tools are united by a common purpose: to provide decision support and analytical functionality. Analysts who want improved reporting or decision-makers and managers who need better, faster information about their business or team benefit the most from BI.
BI supports many use cases:
- Performing evaluatory, temporary, or ad-hoc analyses
- Optimizing KPIs without interrupting the existing systems evaluating business performance
- Real-time reporting, such as in dashboards or graphs based on live data
- Simple statistical inference and modeling, to validate ideas and test hypotheses
- Generating visualizations useful for exploratory data analysis (EDA)
- Optimizing internal tasks such as auditing and quality testing by exposing company data to more stakeholders for summarization and visualization
The importance of BI in business
By generating insights and putting analytical tools in more hands, BI improves enterprise performance and can lead to resource savings across an organization in cost, employee time, and error reduction. Users empowered with BI tools can also complete tasks that were once the sole domain of specialists, while gaining a more comprehensive perspective on their enterprise.
Businesses need BI to unlock the value of the increasing volumes of data they accumulate. An enterprise’s decisions must be informed by data insights for the organization to remain competitive.
Self-service BI tools
BI tools are an integral part of the analysis process. They allow decision-makers to display relevant data, organize it to suit their needs, and quickly reach the information they need. An enterprise should select a BI tool carefully to suit its needs. For example, an organization that involves non-technical personnel in data analysis should focus on BI tools that support self-service analytics.
Self-service BI tools allow end users to delve into enterprise data and come to conclusions just as data analysts or data scientists might, but without the need for programming expertise. They enable more users to view and understand enterprise data. And more eyes on business problems leads to improved solutions.
These benefits have led to the rise of self-service BI tools, bringing sophisticated data processing and analysis to anyone who wants to reach into data and answer business questions.
Stitch makes it easy to start business intelligence today
BI works best when used on accessible, clean data stored in a data warehouse. Stitch lets you quickly and easily replicate data from more than 100 SaaS and database platforms to your data warehouse.
Stitch makes data ingestion straightforward, democratizing data access and usage. Start building smarter business intelligence with Stitch today.