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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 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.
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 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 initiatives can only succeed if the organization is committed and executes it strategically. Critical factors include:
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.
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.
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.
One of the most essential ingredients to strong project management is opening crucial lines of communication between project staff, IT, and end users.
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 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 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 engineers, often provided by business intelligence providers, address technical issues within the software or service. Learn more about MicroStrategy’s support offerings.
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.
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.
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 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 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 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 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.
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.
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.
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.
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.
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.
Artificial Intelligence and Machine Learning
Artificial intelligence is a broad concept that refers to computers thinking like humans. They are built on complex algorithms. Machine learning takes AI a step further—the computer trains itself as more data is ingested and makes changes to the algorithm. The more data it consumes, the smarter the machine becomes, and the better its decisions are.
How is this tech changing the BI world? Traditional BI programs aim to provide a consolidated view of data. More recently, they moved towards a self-service model where users can explore data on their own. However, the seemingly infinite number of ways to view and segment data introduced its own set of problems.
User bias often leads users to see only the data that supports their initial view and can result in poor decision-making. This is where machine learning and predictive analytics come in to play. As more data is gathered and analyzed, complex algorithms can continuously learn to optimize it for a given goal, freeing the user to think more strategically.
Natural language processing (NLP) is a popular example of how AI is transforming how we interact with data. BI tools such as MicroStrategy can take unstructured data from written or spoken language, compute it, and answer the user’s questions even before they ask it.
The most successful companies will prioritize these tools and structure organizations to adopt these technologies with agility.
The internet of things (IoT) refers to interrelated physical devices that speak to each other and transfer data without human interaction. Almost anything can be turned into an IoT device with a sensor to collect and transfer data over networks. The decrease in the cost of sensors and technological advancements in ever-smaller devices have led to the explosion of such devices and the amount of data gathered.
While AI and machine learning are advancements in data processing, the IoT marks an advancement in data production and gathering. Big data is often stored and processed in a distributed file system such as Hadoop. The increase in data sources means BI has more inputs, and smarter decisions can be made when big data is combined with AI and machine learning.