What is Business Intelligence (BI)?

Today’s decision makers are faced with the necessity of making big business decisions faster and more accurately than ever. Fortunately, the right strategies and tools can help you make data-driven decisions quickly and accurately.

Mục Lục

Business intelligence (BI) is the collection of processes, technologies, skills, and applications used to make informed, data-driven business decisions. BI includes data collection, data aggregation, analysis, and meaningful presentation that facilitates decision-making.

Data-driven organizations use a variety of BI tools to access historical and real-time data in a data repository to perform queries, generate customized reports, and predict future trends. These tools include advanced analytics performed by trained data scientists as well as insights generated autonomously by machine learning algorithms.

Data repositories for BI applications include: data warehouses (centralized or decentralized), production databases, operational data stores, and data marts.

Additional resources:

Unlocking Data Preparation for Business Intelligence (BI)
The Role of Statistics in Business Decision Making
How to Structure Your Business to Make Better Use of Data

Business Intelligence vs Analytics

BI is often confused with business analytics. Business analytics (BA) refers to statistical methods used to measure performance and optimize business processes.

Data analytics is the process of analyzing sets of data to gain insights. Two types of data analytics are:

Predictive analytics — Analyzing historical data to determine the most likely outcome.
Prescriptive analytics — Running hypothetical scenarios to determine most likely outcome of a certain action.

Data analytics is a primary component of BI and BA, but only one part of the overall system.

BA is a similar yet separate process with a different function. BA mines historical data for trends and insights to drive business change. BI uses historical and real-time data to enable decision-making in the present: i.e. evaluate what works, what doesn’t, then decide how to best move forward. BI primarily helps run a business today, BA is primarily used to predict what will happen in the future.

Why is Business Intelligence Important?

The growing accessibility of big data is making modern business decisions more crucial but also more difficult to obtain. An enterprise data warehouse often contains a terabyte or more of raw data that needs to processed and made ready for analysis. BI systems allow for comprehensive analysis of data — often in minutes — to respond to specific business requests.

For example, SKF is a global manufacturing enterprise, so they need to be able to accurately forecast the size of the market for its products and the demand for specific product types.

“What products the company should produce and in what volumes? Where to invest or divest and how to react to emerging industry trends? Talend is helping us do that.” — Fritz Ulrich Dettmer, Manager of Business Intelligence, SKF

F+W is a content and ecommerce company dedicated to innovation and creativity. That means their entire team needs to be able to access the data necessary to evaluate success and drive progress.

“With Talend, our cloud and on-premises systems are now speaking together. This has empowered the organization as a whole and is moving us from making decisions based on gut feel to making them based on consistent data.” — Greg Sitzman, VP of Business Intelligence, F+W

Other key benefits of BI include:

Accelerated time-to-answer — In-memory analytics with cloud-based data warehouse solutions can analyze data in real-time, providing fact-based information in minutes.
Better business decisions — BI extracts facts and transforms data into actionable information that can be trusted.
Improved operational efficiency — BI makes the interconnections between different components of the business more visible, so problems and inefficiencies can be identified and dealt with more quickly.
Increased ROI — BI helps identify resources needed to reach goals, increases productivity by making data analysis quicker, and aids in the discovery of new revenue streams.
Faster reporting — BI provides real-time reporting of up-to-the-minute, accurate data sets giving organizations a competitive edge in solving complex business problems.
Accurate strategies — BI helps identify important trends and patterns in data that can be utilized to set priorities and allocate resources to meet desired goals.
Satisfied customers — BI provides data on KPIs that improve core business functions (improved product or service, decrease in time to market) resulting in higher customer satisfaction scores (CSATs).

Business Intelligence Tools

BI can be separated into two main categories: traditional BI and self-service BI. Traditional BI is handled by an IT team or data specialists who run queries, provide guided analysis, and create reports. The downfall with this approach is that it can takes weeks or longer to prepare a report.

The main push today is for, “self-service business intelligence (SSBI).” Self-service BI is when business professionals, without any training in statistical analysis, make queries and generate reports — ad hoc analysis — often by using interactive dashboards installed on a PC. These tools are intuitive, user-friendly, and provide access to data in real time.

Learn more at, “What is Data Preparation?” →

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Business Intelligence Tools: Seven Key Features and Functions

Efficient business intelligence requires the right tools. Different types of BI tools perform various pieces of the overall BI process, and function according to different standards. They operate as standalone tools or as part of an integrated suite of products.

Online analytical processing (OLAP) — BI tools that are used to analyze large volumes of historical data with drill-down functionality. Information is stored in OLAP cubes, and provides a multidimensional view of data.
Ad hoc analysis — BI tools that allow any user to make queries and generate a report to answer a specific question, often by using an OLAP “point and click” dashboard.
Reporting — BI tools that provide a visual representation of data that is extracted in a query such as charts, maps and graphs. Benefits of using BI reporting tools include increased speed, efficiency, and accuracy of reports used for analysis.
Advanced analytics — BI tools that are used by data scientists when constructing predictive and prescriptive analytical models. These autonomous or semi-autonomous tools have sophisticated capacities to predict future outcomes and make recommendations.
Operational BI — BI tools that process incoming data in real-time, giving visibility and faster access to information for decision-making. With real-time data and insights, a company can respond rapidly to market trends and events.
Open source BI — BI tools developed with open source code that can be modified as needed. These tools typically come as a suite of products with reporting and analysis capabilities included.
Self-service BI — BI tools that do not need any training in statistical analysis or data mining to use. Self-service systems are configured to allow any user to make queries, design reports, and gain insights using interactive dashboards.

How to Find the Best BI Tools

The first step in choosing a BI tool is to understand the type of data sources (schema and definitions) the organization accesses, and how they will need to be analyzed. Most data sources can be easily accessed by a BI tool, but there can be specific types of data that make it prohibitive. A cloud-native tool should be able to support data in various data repositories or data warehouses.

The next step is to define business goals and the desired outcome:

Identify KPIs you want the BI system to measure.
Assess costs and evaluate technical skills necessary to manage the tool.
Decide if you need a standalone BI tool, open source BI tool, or BI suite of tools.

The right BI tool—or tools—should allow you to drill down to the finest detail and get precise answers filtered by source, time, and any other factor needed to fulfill a request. It should have ‘suggestive intelligence’ capabilities (automated with machine learning) that can find patterns in the data relevant to the question being asked and suggest solutions.

Other important features of a modern, robust BI tool include:

Can generate visual reports
Tracks progress and individual KPIs
Creates presentation-ready graphics
Has an easy-to-use, intuitive interface
Has robust security
Includes mobile applications
Automatically prioritizes works tasks
Identifies problems early
Has a natural language interface

Business Intelligence Examples

More than just theory, when BI is implemented correctly it can transform an organization. Here are a few examples:

1. Lenovo: The Power of Real-Time BI

Lenovo is the world’s largest PC vendor and a U.S. $46 billion personal technology company. They have built an elastic hybrid-cloud platform supporting real-time BI that annually analyzes 11 billion+ transactions of structured and unstructured data.

Measured results of their new platform include: 18% increased attach rate for ThinkPad laptop series, 11% increase in revenue per retail unit with conjoint analysis, and $1 million reduced operating costs in six months.

View Case Study →

2. McDonald’s: Data-Driven BI for Better Customer Service

McDonald’s used data-driven BI to improve customer service with a new approach to ETL, big data, and data quality.

Measured results include: reduced need to buy new hardware and the ability to generate business-critical reports in a timely manner to forecast sales, ensure right staffing levels, and recruit new employees.

3. MoneySuperMarket: Aggregating Data for Improved BI

MoneySuperMarket (MSM) is the UK’s leading price-comparison website. MSM leveraged the capacity of Amazon Web Services with Talend Data Management to get data from several web services into data warehouses to provide marketing BI.

Measured results include: improved path to purchase for customers, channel performance forecasts enabled with daily metrics, and data science applied to 11 terabytes of data supporting ad hoc analysis

The Future of Business Intelligence: A.I. and the Cloud

The future of big data BI is the cloud and artificial intelligence. Pulling data from a production database and dumping it into a spreadsheet for BI reporting is becoming less common. As enterprises move to the cloud, the BI system is automated using cloud-native BI applications that extract insights, make suggestions, and create visual representations of the data.

Today, the needs to be data-driven and to address informational complexities and data modernization, are the driving forces behind businesses cloud strategies. The top three reasons CIOs give for adopting cloud computing information technologies are to:

Improve agility and responsiveness.
Accelerate product development and innovation.
Save money.

Cloud computing offers new solutions for BI and big data management—with automated, cloud-native BI tools. It is estimated that by 2020, 40% of tasks performed by data scientists will be automated.

Getting Started with Business Intelligence

Business analysts rely on factual, trusted information from a company’s massive data stores, as needed, to meet business demands. BI leverages various processes and technologies to access the vast cache of data contained in data warehouses or data marts, and convert it into actionable information.

More organizations rely on Talend to integrate big data into business decisions because our unified tools develop and deploy data integration jobs ten times faster than hand coding, at 1/5th the cost of competitors.

Learn more about how our free, open source integration software can get all of your data quickly connected, transformed, and ready for analysis faster than you ever thought possible.