Business intelegence software

Business intelligence BI software is a set of tools used by companies to retrieve, analyze, and transform data into useful business insights, usually within easy-to-read visualization like charts, graphs, and dashboards. Examples of business intelligence tools include data visualization, data warehouses, interactive dashboards, and reporting tools. In contrast to competitive intelligence that analyzes data from outside sources, a BI application pulls internal data that the business produces into an analytics platform for deep insights into how different parts of the business affect one another.

As big data—the tendency for companies to collect, store, and mine their business data—has gained in prominence, so has the popularity of BI software. Companies generate, track, and compile business data at a scale never before seen.

And the ability to integrate cloud software directly with proprietary systems has further driven the need to combine multiple data sources and take advantage of data preparation tools.

To make informed choices, businesses need to base their decisions on evidence. The mountains of data that businesses and their customers produce contain evidence of purchasing patterns and market trends. By aggregating, standardizing, and analyzing that data, businesses can better understand their customers, better forecast revenue growth, and better protect themselves against business pitfalls. These insights can help a company choose a course of action in a matter of minutes.

BI software interprets a sea of quantifiable customer and business actions and returns queries based on patterns in the data. BI comes in many forms and spans many different types of technology.

This guide compares the top business intelligence software vendors, breaks down the three major stages data must go through to provide business intelligence, and provides considerations for purchasing BI for different sized businesses. Business intelligence platforms come in several forms for varying business needs. Companies looking to provide data services to business users will find self service BI software will meet the needs of most of their users.

Data visualization tools are helpful for teams that are dipping their toes into data analytics but may not have lots of extra development resources available.

Data warehousing tools provide the underlying infrastructure that can house and cleanse data before serving it up through visualizations. And BI platforms provide end-to-end tools to store, cleanse, visualize, and publish data. Data lives in a number of systems throughout an organization.

For the most accurate analysis, companies should ensure standardized formatting across data types from each of these systems. For example, large enterprises could have information about their customers in their customer relationship management CRM application, and have financial data in their enterprise resource planning application, and several other key revenue data sets in various cloud software applications.

These separate programs may label and categorize data differently and the company will need to standardize the data before analysis. Some BI platforms pull data for analysis directly from the source applications via a native API connection or webhook. Other business intelligence systems require the use of a cloud data storage system to aggregate diverse data sets in a common location. Small businesses, single departments, or individual users may find that a native connection works well, but large corporations, enterprise companies, and companies that generate large data sets will need a more comprehensive business intelligence setup.

If they choose a centralized storage solution, businesses may use a data warehouse or data mart to store their business information and purchase an extract, transform, and load ETL software to facilitate their big data storage. Alternatively, they may use a data storage framework like Hadoop to manage their data. Regardless of whether businesses choose to store their data in a data warehouse, a cloud database, an on-premise server, or run queries on the source system, data analysis and the resulting insights make the field appealing to business users.

Analytics platforms vary in terms of complexity, but the general method of combining large amounts of normalized data to identify patterns remains consistent across platforms. Common correlations drawn from data mining include grouping specific sets of data, finding outliers in data, and drawing connections or dependencies from disparate data sets. Data mining often uncovers the patterns used in more complex analyses, like predictive modeling, which makes it an essential part of the BI process whose growth is correlated directly with the rise of big data in businesses of all sizes.

Of the standard processes performed by data mining, association rule learning presents the greatest benefit. By examining data to draw dependencies and construct correlations, the association rule can help businesses better understand the way customers interact with their website or even what factors influence their purchasing behavior. Association rule learning was originally introduced to uncover connections between purchase data recorded in point of sale systems at supermarkets.

For example, if a customer bought ketchup and cheese, association rules would likely uncover that the customer purchased hamburger meat as well. While this is a simplistic example, it works to illustrate a type of analysis that now connects incredibly complex chains of events in all sorts of industries, and helps users find correlations that would have stayed hidden otherwise. Perhaps one of the most exciting aspects of BI, advanced analytics features like predictive and prescriptive analytics function as a subset of data mining.

The tools use existing data sets and algorithmic models to help companies make better business decisions. As the name suggests, predictive analytics forecast future events based on current and historical data. By drawing connections between data sets, these software applications predict the likelihood of future events, which can lead to a huge competitive advantage for businesses.

Predictive analysis involves detailed modeling, and even ventures into the realms of artificial intelligence AI and machine learning ML , where software actually learns from past events to predict future consequences. The three main forms of predictive analysis are predictive modeling, descriptive modeling, and decision analytics. The most well-known segment of predictive analytics, this type of software does what its name implies: it predicts, particularly in reference to a single element.

Predictive models use algorithms to search for correlations between a particular unit of measurement and at least one or more features pertaining to that unit. The goal is to find the same correlation across different data sets. Whereas predictive modeling searches for a single correlation between a unit and its features—in order to predict the likelihood of a customer switching insurance providers, for example—descriptive modeling seeks to reduce data into manageable sizes and groupings.

Descriptive analytics works well for summarizing information such as unique page views or social media mentions. Decision analytics take into account all the factors related to a discrete decision. Decision analytics predict the cascading effect an action will have across all the variables involved in making that decision. In other words, decision analytics gives businesses the concrete info they need to predict outcomes and take action.

Data comes in three main forms: structured, semistructured, and unstructured. However, this data is often crucial to understanding business outcomes. With so much data in unstructured form, text analytics should be a key consideration when trying to find the best business intelligence software. Natural language processing NLP software, also known as text analytics software, combs large sets of unstructured data to find hidden patterns. NLP is particularly interesting for businesses that work with social media.

Natural language processing tools also measure customer sentiment, provide actionable insight into lifetime customer value, and learn customer trends that can inform future product lines. The previous two applications of business intelligence software dealt with the mechanics of a business intelligence system: how business data is stored, and how software refines this data into meaningful intelligence.

Business intelligence reporting focuses on the presentation of these findings. First, explore the data dashboards the tool offers and see how they can help your organization uncover new insights. Since these dashboards will be used daily by a wide range of users, they should enable users to visually explore data in ways that help identify and solve problems quickly.

Next, take a look at how findings are presented through data visualization and data storytelling. These features typically use charts, graphs, and maps to surface insights that are hard to see on a spreadsheet. The best data visualization tools have an intuitive interface that lets users explore and present data in multiple ways, regardless of their technical skills.

Sharing insights to drive action is one of the main benefits of business intelligence. As part of your evaluation, find out how users can collaborate to update, customize, and share reports. For example, some BI tools let users make annotations right in the software, quickly embed reports into messaging and collaboration tools, and set permissions to distribute findings inside and outside of your organization. Mobile features provide access to real-time data and allow you to make data-informed decisions on the fly when working remotely.

Talk to your employees who work in the field or distributed locations to find out what level of mobile access they need. It could range from basic viewing of dashboards and reports to creating and editing analytics with mobile tools.

Software pricing structures can range from per-user plans to subscription models. As you look at your budget, consider how many users you currently have and how your business will grow in the future. Keep in mind that additional capabilities and updates could cost more—make sure you know which features your organization must have and which ones are negotiable. The goal is to find a cost-effective solution that provides business intelligence tools to your organization at the scale you need and has flexibility to grow with your organization.

Any business intelligence tool requires training and support, initially and as new features are added. Explore what training options are available. Is training included or do you need to manage it in-house? If you need to deliver training to a distributed workforce at multiple locations, are online classes available? What kind of training is offered for software updates?

In addition to traditional training and education, user communities can provide tips and advice from peers and product experts. Before you select a business intelligence tool, look for forums, blogs, and user groups that help provide training and support. Finally, make sure you understand how product support is provided. Evaluating your business intelligence vendor is equally as important as reviewing features of the tool. It should also have features that make BI insights accessible to your entire organization—such as data visualization, shared dashboards, artificial intelligence, and machine learning.

Flexibility, interactivity … just jump in. Business intelligence is applied differently from business to business and across a range of sectors—finance, retail and consumer goods, energy, technology, government, education, healthcare, manufacturing, and professional services. Metro Bank uses business intelligence to connect all their data sources and enable business users as well as IT staff to develop reports and BI solutions, making the business more agile and responsive.

Customer service, online banking, and branch staffing are just a few of the teams that use BI tools to improve efficiency.

Employees are better able to perform their jobs, spending time innovating and deploying code instead of simply maintaining assets. Cummins needed a solution that balanced governance and autonomy in report creation, while including advanced analytics. They also wanted a cost-effective solution that would keep the spirit of self-service alive, while giving IT more control over how and where data was stored, accessed, and maintained. Explore how your organization can use business intelligence tools, such as Microsoft Power BI, to work more efficiently and enable everyone across your company to make data-driven decisions.

Power BI. What is business intelligence? How business intelligence works. Step 1: Collect and transform data from multiple sources Business intelligence tools typically use the extract, transform, and load ETL method to aggregate structured and unstructured data from multiple sources.

Step 2: Uncover trends and inconsistencies Data mining, or data discovery, typically uses automation to quickly analyze data to find patterns and outliers which provide insight into the current state of business.

Step 3: Use data visualization to present findings Business intelligence reporting uses data visualizations to make findings easier to understand and share.

Step 4: Take action on insights in real time Viewing current and historical data in context with business activities gives companies the ability to quickly move from insights to action. Why companies benefit from using business intelligence tools. Some of the specific benefits that businesses experience when using BI include:.