Business intelligence (BI) is the collection of strategies and tools used to analyze business information. Business intelligence projects are significantly more effective when they combine external data sources with internal data sources for actionable insight.
Business analytics, also known as advanced analytics, is a term often used interchangeably with business intelligence. However, business analytics is a subset of business intelligence since business intelligence deals with strategies and tools while business analytics focuses more on methods. Business intelligence is descriptive while business analytics is more prescriptive, addressing a problem or business question.
Competitive intelligence is a subset of business intelligence. Competitive intelligence is the collection of data, tools, and processes for collecting, accessing, and analyzing business data on competitors. Competitive intelligence is often used to monitor differences in products.
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.
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.
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