What Is Business Intelligence (BI)?
Understanding what’s happening across all areas of the business is critically important if you expect to remain competitive. However, when data is stored in silos, decision-makers struggle to even track key performance indicators (KPIs). Forget advanced predictive analysis.
Getting that valuable information into the hands of key decisions-makers is the promise of today’s business intelligence (BI) systems. These projects aren’t necessarily simple, but the payoff is more effective, real-time decision-making and performance optimization across the enterprise.
Business intelligence (BI) refers to the systems an organization uses to drive strategy, analyze data and extract insights to inform decision-makers. An effective BI practice enables all members of the organization — from leaders and managers to front-line support and operations personnel — to act based on shared intelligence derived from a single, reliable source of data.
BI overlaps a number of other data-driven disciplines. It’s important to understand the differences among them as well as how they work together to deliver greater value.
Business intelligence vs. data science
BI and data science are closely linked but distinct disciplines.
Data science is an interdisciplinary field that extracts meaning and insight from increasingly large, varied and complex data sets. It is both predictive, forecasting future outcomes, and prescriptive, determining the best actions to prepare for those outcomes.
BI refers to the analysis of business data in order to understand company performance and provide actionable insight. It analyzes what has already happened.
Business intelligence vs. data analytics vs. business analytics
Data analytics refers to the examination of datasets or creation of analytical models to uncover patterns and draw conclusions about information. Business analytics is a more specific application of data analytics, referring to the analysis of business data.
Business analytics includes data mining, machine learning and statistical analysis to make predictions about the future and guide decision-making.
BI paints a picture of what has and hasn’t worked to inform what a business might want to do next; business analytics offers visibility into what is likely to happen.
Increasingly, companies are combining business analytics and BI to drive both day-to-day and forward-looking planning and decisions.
Key Takeaways
Business intelligence comprises the data analysis strategies and technologies used to deliver insights that power better decision-making.
While business intelligence has been around for decades, it is now an indispensable, strategic, technology-enabled practice.
Digitization of information and advances in technology have democratized and amplified the power of business intelligence.
Use cases for business intelligence span most corporate functions and, increasingly, most business roles.
Effective business intelligence can deliver a number of business benefits, from increased revenue and agility to improved efficiency and productivity.
How Does Business Intelligence Work?
Business intelligence systems provide detailed analyses of business operations and performance. The process begins with collecting data that exists in multiple internal enterprise software applications and from external sources. The data may be structured or unstructured, historical or real-time. Often, this data is gathered into a central data warehouse or smaller data marts. It may also exist in data lakes, where raw data, like log files, typically reside. Data integration and management tools can be used to extract, transform and load the raw data into a warehouse.
Where Data Resides
Data lakes. Data lakes are large — sometimes huge — storage repositories containing a wide variety of raw, structured, semi-structured and unstructured data. Each data type stays in its native format while in the data lake. Data may be dumped into the lake from many internal and external sources. The storage medium used tends to be inexpensive, and extracting, transforming and loading the data so it can be used for analysis often requires specialized expertise.
Data warehouses. These contain data that may have been extracted from a data lake or deposited directly. Data in a data warehouse is in an assigned format and uses a defined schema. It may be structured or semi-structured, such as video files that contain metadata describing the contents. Data warehouses are usually large, containing data from all corners of a company, though not the size of a typical data lake. The data is readily accessible to authorized business users and applications, but companies often prefer to slice sections off into data marts for security and speed of access.
Data marts. These are collections of data relating to one subject or department, like finance, sales or marketing, and may be standalone or partitioned off from a data warehouse. Data marts structured and accessible to authorized business users and applications.
Databases. These are the organizing elements of data warehouses and data marts.
Next, BI software provides a variety of data management, reporting, analytics and communication capabilities, including data preparation, querying capabilities and advanced analytics including data mining, predictive analytics, text mining and statistical analysis. It also distributes the resulting KPIs and other intelligence to business users, conveying insights to help guide tactical and strategic action.
Source: https://www.netsuite.com/portal/resource/articles/erp/business-intelligence.shtml