Business Intelligence (BI): The Definitive Guide

Business Intelligence Applications in the Enterprise

  • Measurement
    Many business intelligence tools are used in measurement applications. They can take input data from sensors, CRM systems, web traffic, and more to measure KPIs. For example, solutions for a facilities team at a large manufacturing company might include sensors to measure the temperature of key equipment to optimize maintenance schedules.

  • Analytics
    Analytics is the study of data to find meaningful trends and insights. This is a very popular application of business intelligence tools since it allows businesses to deeply understand their data and drive value with data-driven decisions. For example, a marketing organization could use analytics to determine the customer segments most likely to convert to a new customer.

  • Reporting
    Report generation is a standard application of business intelligence software. BI products can now seamlessly generate regular reports for internal stakeholders, automate critical tasks for analysts, and replace the need for spreadsheets and word-processing programs.
    For example, a sales operations analyst might use the tool to produce a weekly report for her manager detailing last week’s sales by geographical region—a task that took far more effort to do manually. With an advanced reporting tool, the effort required to create such a report decreases significantly. In some cases, business intelligence tools can automate the reporting process entirely.

  • Collaboration
    Collaboration features allow users to work across the same data and same files together in real-time and are now very common in modern business intelligence platforms. Cross-device collaboration will continue to drive development of new and improved business intelligence tools. Collaboration in BI platforms can be important when creating new reports or dashboards.

  • For example, the CEO of a technology company might want a personalized report or dashboard of focus group data on a new product within 24 hours. Product managers, data analysts, and QA testers could all simultaneously build their respective sections of the report or dashboard to complete it on time with a collaborative BI tool.

Business Intelligence Best Practices

Business intelligence initiatives can only succeed if the organization is committed and executes it strategically. Critical factors include:

  • Business sponsorship
    Business sponsorship is the most important success factor because even the most optimal system cannot overcome a lack of business commitment. If the organization cannot come up with the budget for the project or executives are busy with non-BI initiatives, the project cannot be successful.

  • Business Needs
    It’s important to understand the needs of the business to properly implement a business intelligence system. This understanding is twofold—both end users and IT departments have important needs, and they often differ. To gain this critical understanding of BI requirements, the organization must analyze all the various needs of its constituents.

  • Amount and Quality of the Data
    A business intelligence initiative will only be successful if it incorporates high-quality data at scale. Common data sources include customer relationship management (CRM) software, sensors, advertising platforms, and enterprise resource planning (ERP) tools. Poor data will lead to poor decisions, so data quality is important.

    A common technique to manage the quality of data is data profiling, where data is examined and statistics are collected for improved data governance. It helps to maintain consistency, reduce risk, and optimize search through metadata.

  • User Experience
    Seamless user experience is critical when it comes to business intelligence because it can promote user adoption and ultimately drive more value from BI products and initiatives. End user adoption will be a struggle without a logical and usable interface.

  • Data Gathering and Cleansing
    Data can be gathered from an infinite number of sources and can easily overwhelm an organization. To prevent this and create value with business intelligence projects, organizations must identify critical data. Business intelligence data often includes CRM data, competitor data, industry data, and more.

  • Project Management
    One of the most essential ingredients to strong project management is opening crucial lines of communication between project staff, IT, and end users.

  • Getting Buy-in
    There are numerous types of buy-in, and it’s crucial from top decision-makers when purchasing a new business intelligence product. Professionals can get buy-in from IT by communicating about IT preferences and needs. End users have needs and preferences as well, with different requirements.

  • Requirements Gathering
    Requirements gathering is arguably the most important best practice to follow, as it allows for more transparency when several BI tools are up for comparison. Requirements come from several constituent groups, including IT and business users.

  • Training
    Training drives end user adoption. If end users aren’t properly trained, adoption and value creation become much slower and difficult to achieve. Many business intelligence providers, including MicroStrategy, provide education services, which can consist of training and certifications for all associated users. Training can be provided for any key group associated with a business intelligence project.

  • Support
    Support engineers, often provided by business intelligence providers, address technical issues within the software or service. Learn more about MicroStrategy’s support offerings.

  • Others
    Companies should ensure traditional BI capabilities are in place before the implementation of advanced analytics, which requires several key precursors before it can add value. For example, data cleansing must already be excellent and system architectures must be set up.
    BI tools can also be a black-box to many users, so it’s important to continually validate their outputs. Setting up a feedback system for requesting and implementing user-requested changes is important for driving continuous improvement in business intelligence.

Functions of Business Intelligence

  • Enterprise Reporting
    One of the key functions of business intelligence is enterprise reporting, the regular or ad-hoc provision of relevant business data to key internal stakeholders. Reports can take many forms and can be produced using several methods. However, business intelligence products can automate this process or ease pain points in report generation, and BI products can enable enterprise-level scalability in report production.

  • OLAP
    Online analytical processing (OLAP) is an approach to solving analytical problems with multiple dimensions. It is an offshoot of online transaction processing (OLTP). The key value in OLAP is this multidimensional aspect, which allows users to look at problems from a variety of perspectives. OLAP can be used to complete tasks such as CRM data analysis, financial forecasting, budgeting, and others.

  • Analytics
    Analytics is the process of examining data and drawing out patterns or trends to make key decisions. It can help uncover hidden patterns in data. Analytics can be descriptive, prescriptive, or predictive. Descriptive analytics describe a dataset through measures of central tendency (mean, median, mode) and spread (range, standard deviation, etc.).
    Prescriptive analytics is a subset of business intelligence that prescribes specific actions to optimize outcomes. It determines a prudent course of action based on data. Therefore, prescriptive analytics is situation-dependent, and solutions or models should not be generalized to different use cases.
    Predictive analytics, also known as predictive analysis or predictive modeling, is the use of statistical techniques to create models that can predict future or unknown events. Predictive analytics is a powerful tool to forecast trends within a business, industry, or on a more macro level.

  • Data Mining
    Data mining is the process of discovering patterns in large datasets and often incorporates machine learning, statistics, and database systems to find these patterns. Data mining is a key process for data management and pre-processing of data because it ensures proper data structuring.
    End users might also use data mining to construct models to reveal these hidden patterns. For example, users could mine CRM data to predict which leads are most likely to purchase a certain product or solution.

  • Process Mining
    Process mining is a system of database management in which advanced algorithms are applied to datasets to reveal patterns in the data. Process mining can be applied to many different types of data, including structured and unstructured data.

  • Benchmarking
    Benchmarking is the use of industry KPIs to measure the success of a business, a project, or process. It is a key activity in the BI ecosystem, and widely used in the business world to make incremental improvements to a business.

  • Intelligent Enterprise
    The above are all distinct goals or functions of business intelligence, but BI is most valuable when its applications move beyond traditional decision support systems (DSS). The advent of cloud computing and the explosion of mobile devices means that business users demand analytics anytime and anywhere—so mobile BI has now become essential to business success.
    When a business intelligence solution reaches far and wide in an organization’s strategy and operations, it can use its data, people, and enterprise assets in ways that weren’t possible in the past—it can become an Intelligent Enterprise. Learn more about how MicroStrategy can help your organization become an Intelligent Enterprise.

Business Intelligence Jobs

Entry-level business intelligence workers are in high demand and make an average of $80,000 per year, which is 33% higher than the national median income level. Despite the demand for business intelligence professionals, salaries vary significantly. Some influential factors include education level, work experience, and technical skills.

How Much Do BI Analysts Make?

How Much Do BI Managers Make?

The History of Business Intelligence

Richard Miller Devens first used the term “business intelligence” in 1865 to describe how a successful banker named Sir Henry Furnese created a network of information to beat competitors. Furnese maintained a network of informant merchants throughout Western, Central, and Northern Europe, and they passed information to him faster than it could reach any of Furnese’s competitors, allowing him to act on the intelligence and turn major profits.

Until the middle of the 20th century, the term was not significantly used. In 1958, researcher Hans Peter Luhn used the term to describe the ability to understand relationships based on facts that spur action toward some kind of business goal. Luhn wrote about business intelligence as an automated system of disseminating information between the different sections or divisions of an organization, a definition fairly close to the usage we see today.
In 1989, Howard Dresner used the term business intelligence in a much larger scope: “the concepts and methods to improve business decision-making by using fact-based support systems.”
Despite these notable uses, the term was not widely used across industries until the 1990s. More recently, the term has evolved from describing enterprise reporting only to describing powerful and easy-to-use data analysis tools. Today, the term business intelligence refers to the entire collection of tools, systems, and methods of analyzing business information.

Key Challenges of Business Intelligence

  • Unstructured Data
    To solve problems with searchability and data assessment, it’s necessary to know something about the content. At present, business intelligence systems and technologies require data to be adequately structured to preserve searchability and data assessment. This structuring can be done by adding context with metadata.
    Many organizations also struggle with data quality issues. Even with pristine BI architecture and systems, companies that have questionable or incomplete data will struggle to get buy-in from users who don’t trust the numbers in front of them.

  • Poor Adoption
    Many BI projects attempt to entirely replace old tools and mechanisms, but this often results in poor user adoption, with users reverting to the tools and processes they’re comfortable with. Many experts suggest that BI projects fail because of the time it takes to create or run reports, which makes users less likely to adopt new technologies and more likely to revert to legacy tools.
    Another reason for business intelligence project failure is inadequate user or IT training. Inadequate training can lead to frustration and overwhelm, dooming the project.

  • Lack of Stakeholder Communication
    Internal communication is another key factor that can spell failure for business intelligence projects. One potential pitfall is giving false hope to users during implementation. BI projects are sometimes billed as quick fixes, but they often turn into large and stressful projects for everyone involved.
    Lack of communication between end users and IT departments can detract from project success. Requirements from IT and purchasers should align with the needs of the team of end users. If they don’t collaborate, the final product may not align with expectations and needs, which can cause frustration from all parties and a failed project. Successful projects provide business users with valuable tools that also meet internal IT requirements.

  • Improper Planning
    The research and advisory firm Gartner warns against one-stop shopping for business intelligence products. Business intelligence products are highly differentiated, and it’s important that customers find the product that suits their organization’s needs for capabilities and pricing.
    Organizations sometimes treat business intelligence as a series of projects instead of a fluid process. Users typically request changes on an ongoing basis, so having a process for reviewing and implementing improvements is critical.
    Some organizations also try a “roll with the punches” approach to business intelligence rather than articulating a specific strategy that incorporates corporate objectives and the needs of IT and end users. Gartner suggests forming a team specifically to create or revise a business intelligence strategy with members pulled from these constituent groups.
    Companies may try to avoid buying an expensive business intelligence product by asking for surface-level custom dashboards. This type of project tends to fail because of its specificity. A single, siloed custom dashboard might not be relevant to overarching corporate objectives or business intelligence strategy.
    In preparation for new business intelligence systems and software, many companies struggle to create a single version of the truth. This requires standard definitions for KPIs from the most general to the most specific. If proper documentation is not me and there are multiple definitions floating around, users can struggle and valuable time can be lost to properly address these inconsistencies.

Frequently Asked Questions