Online Business Intelligence Software Tableau – We researched the best business intelligence software based on user popularity and key features. Compare the best BI tools in the table below and read on to learn more about how these data analytics tools can improve your business results. Try our Product Selector at the top of the page for personalized recommendations of the best BI software for your company.
Business intelligence (BI) software is a set of business analytics solutions used by companies to capture, analyze, and transform data into useful business information, typically in easy-to-read visualizations such as charts, graphs, and dashboards. Examples of top BI tools include data visualization, data warehousing, interactive dashboards, and BI reporting tools. Unlike competitive intelligence, which analyzes data from external sources, a BI solution pulls internal business-generated data into an analytics platform to gain deeper insights into how different parts of the business interact.
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As big data—the trend for companies to collect, store, and master their business data—has become increasingly important, BI software has also grown in popularity. Companies are creating, tracking and compiling business data on an unprecedented scale. The ability to integrate cloud software directly with proprietary systems has further increased the need to integrate multiple data sources and leverage data preparation tools. But all this data is nothing if we can’t understand it and use it to improve business results.
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In order to make an informed choice, businesses must base their decisions on evidence. The mountains of data generated by businesses and customers contain evidence of buying patterns and market trends. By collecting, standardizing, and analyzing this data, businesses can better understand their customers, better predict revenue growth, and better protect themselves from business pitfalls.
Business intelligence has traditionally taken the form of quarterly or annual reports that report on a set of key performance indicators (KPIs), but today’s BI reporting software is supported by lightning-fast continuous data analysis tools. These can help a company choose a course of action within minutes.
BI software interprets large numbers of customer and business actions and returns queries based on patterns in the data. BI comes in many forms and involves many different types of technology. This comparison of business intelligence tools from software vendors breaks down the three key data steps that must be taken to provide business intelligence and provides considerations for purchasing BI tools for businesses of various sizes.
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Business intelligence tools and platforms come in different forms for different business needs. Companies that provide information services to business users will find that self-service BI software will meet the needs of most of their users. Data visualization tools are useful for teams that are dipping their toes into data analysis but don’t have a lot of additional development resources. Data warehousing tools provide the underlying infrastructure that can host and clean data before serving it through visualization. And BI tools provide end-to-end dashboard tools to store, clean, visualize and publish data.
Data resides in a number of systems within an organization. For more accurate analysis, companies should provide standardized formats by data type from each of these systems. For example, large enterprises may have customer information in customer relationship management (CRM) applications, financial data in enterprise resource planning (ERP) applications, and various other key revenue data sets in various cloud software applications. These separate applications may label and categorize data differently, and the company must standardize the data before analyzing it.
Some business intelligence platforms pull data directly from source applications for analysis via a native API connection or web hook. Other business intelligence tools require the use of a cloud data storage system to collect various data sets in a common location. Small businesses, single departments, or individual users may find that a local connection works well, but large corporations, enterprise companies, and companies that create large data sets will need a more comprehensive business intelligence setup.
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If they choose a centralized storage solution, enterprises can use a data warehouse or data mart to store business data and program extract, transform, and load (ETL) to facilitate big data storage. Alternatively, a data warehouse framework such as Hadoop can be used to manage the data.
Whether enterprises choose to store their data in a data warehouse, cloud database, on-premises server, or search the source system, data analysis and results make this area attractive to business users. Data analysis tools vary in complexity, but the general method of combining large volumes of normalized data to identify patterns remains consistent across business intelligence platforms.
Data mining, also known as “data discovery,” involves automated and semi-automated data analysis to discover patterns and inconsistencies. Common correlations drawn in data mining include grouping specific data sets, finding outliers in data, and drawing relationships or dependencies in separate data sets.
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Data mining often uncovers patterns that are used in more complex analyses, such as predictive models, making it an essential part of the BI process, whose growth is directly related to the rise of big data in businesses of all sizes.
In the standard process performed by data mining, learning association rules offers the greatest benefit. By exploring data to draw dependencies and build correlations, association rules can help businesses better understand how customers interact with their websites and even what factors influence their purchasing behavior.
A study of association rules is presented to detect relationships between purchase data recorded in supermarket point-of-sale systems. For example, if a customer bought ketchup and cheese, the association rules will detect that the customer also bought hamburger meat. While this is a simple example, it now works to illustrate a type of analysis that connects incredibly complex chains of events across industries and helps users find correlations that would otherwise remain hidden.
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Perhaps one of the most interesting aspects of BI is that advanced analytics features such as predictive and prescriptive analytics function as part of data acquisition. The tools use existing datasets and algorithmic models to help companies make better business decisions.
As the name suggests, predictive analytics predicts future events based on current and historical data. By making connections between data sets, these software programs predict the likelihood of future events that can lead to a significant competitive advantage for businesses.
Predictive analytics involves detailed modeling and even forays into the fields of artificial intelligence (AI) and machine learning (ML), where software actually learns from past events to predict future outcomes. The three main forms of predictive analytics are predictive modeling, descriptive modeling, and decision analysis.
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The most popular segment of predictive analytics, this type of software does exactly what its name implies: it predicts with reference to one item in particular. Predictive models use algorithms to find correlations between a given unit of measure and at least one or more properties associated with that unit. The goal is to find the same correlation between different data sets.
Predictive models look for uniform correlations between a unit and its characteristics—for example, to predict the likelihood that a customer will switch insurance providers—while descriptive models try to reduce data to manageable dimensions and groupings. Descriptive analytics work well for summarizing data such as unique page views or social media mentions.
Decision analysis considers all the factors involved in a discrete decision. Decision analysis predicts the cascading effect of an action on all the variables involved in making that decision. In other words, decision analysis provides businesses with the concrete information they need to predict outcomes and take action.
Business Intelligence Tools For Data Visualization And Analysis
Data comes in three basic forms: structured, semi-structured and unstructured. Unstructured data is most common and includes text documents and other file types that exist in formats that computers cannot easily read.
Unstructured data cannot be stored in well-sorted sets of rows or columns of similarly formatted data, making it impossible for traditional data analysis software to analyze. However, this information is often critical to understanding business outcomes. With so much data in unstructured form, text analytics should be a key consideration when researching the best business intelligence tools.
Natural language processing (NLP), also known as text analysis software, combines large sets of unstructured data to find hidden patterns. NLP is especially interesting for businesses that work with social media. Using the right mix of data ingestion software and AI, a business can set up rules to track keywords or phrases (such as a business name) to find patterns in how customers use that language. Natural language processing tools measure customer sentiment, provide actionable insight into customer lifetime value, and learn customer trends that can inform future product lines.
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Two previous applications of business intelligence software dealt with the mechanics of a business intelligence system: how business data is stored and how software refines that data into meaningful intelligence. Focuses on the presentation of business intelligence reports
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