Business Intelligence Software
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Table of Contents:
- What is business intelligence software?
- The top business intelligence software vendor reviews
- Business intelligence software comparison
- Key business intelligence software features and recommended vendors
- Choosing the right business intelligence software
What is business intelligence software?
Business intelligence software is a set of tools used by companies to retrieve, analyze, and transform data into useful business insights. Examples of business intelligence tools include data visualization, data warehousing, dashboards, and reporting. In contrast to competitive intelligence, business intelligence software pulls from internal data that the business produces, rather than from outside sources.
As Big 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. But all this data is nothing if we can’t make sense of it and use it to improve business outcomes.
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.
Business intelligence has traditionally taken the form of quarterly or yearly reports, but today’s software-backed business intelligence tools work continuously and at light speed. 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 business intelligence software for different sized businesses.
The top business intelligence software vendor reviews
- Tableau vs. Spotfire: Business Intelligence for the Non-IT Guru
- Tableau vs. Looker: A Business Intelligence Software Comparison
- Power BI vs Tableau: A Data Analytics Duel
- 16 Tableau Alternatives For Visualizing And Analyzing Data
- Domo vs. Tableau: Choosing the Right Business Intelligence Solution
- 5 Ways Embedded Analytics Can Bring Data Science to Your Customers
- Your IT Department Will Love These 6 Customer Intelligence BI Software Choices
- The Best Embedded Analytics Software Options for Small, Medium, and Enterprise Businesses
- The TechnologyAdvice 2019 Best Business Intelligence Software Awards
- Top 10 Predictive Analytics Tools, By Category
- Find the Canary in Your Data: Data Mining Techniques for Non-Analysts
Business intelligence software comparison
Best BI Software (By Category)
|Self-Service||Data Visualization||Data Warehousing||BI Platforms|
|SAP Crystal Reports||iDashboards||Sisense||Tableau|
Key business intelligence software features and recommended vendors
Data storage for business intelligence
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 (ERP) application. These separate programs may label and categorize data differently and will need to standardize the data before analysis.
Some business intelligence software programs 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 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 data storage. Alternately, they may use a data storage framework like Hadoop to manage their data.
Business intelligence combines disparate data sources into one database by building a data warehouse. Data warehouses act as a central repository for data to be queried and analyzed by other BI applications. Using the extract, transform, and load method, data warehouses aggregate data from across an organization and make it easier for other applications to quickly access them.
Analytics and reporting tools can still function without data warehouses, but running reports through CRM software, or even point of sale (POS) software not only limits the focus of the intelligence, it also negatively affects the performance of those applications. Also, the data in these systems exist in different formats, making it exceptionally difficult to draw conclusions and identify patterns without restructuring the data into a common format and housing it in a common area.
Data stored in a data warehouse takes the form of dimensions or facts, which are pulled from the systems that produce the data. Facts represent numbers for a specific action, like the sales of a widget. Dimensions give context to facts by adding dates and locations, and is also called metadata. For instance, dimensions could break apart the sales of a widget by months or years, making queries easier to perform.
For more information and recommended data warehouse vendors, visit our data warehouse overview page.
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Essentially simpler, narrower versions of data warehouses, data marts focus on a specific subset of data instead of storing data from across the entire company. They might store more frequently used data, or data that only one department uses. Companies will find it cheaper to implement data marts than data warehouses, and they can provide non-IT staff with a better user experience by limiting the complexity of the database.
Extract, transform, and load (ETL) software
Named for the process by which data is transferred into a data warehouse, ETL applications normalize data in a central location. Companies can purchase ETL software with data warehouse software or as an add-on application. Let’s examine each part of the ETL process:
- Extract: Data extraction is the process of retrieving data from its system of origin. Often the most difficult aspect of the process, the degree of success by which data are extracted from their source systems—ERP or CRM systems for example—influences the success of the rest of the process. Unstructured data aren’t formatted well for fitting into rows and columns, which makes it more difficult to analyze after storage in a data warehouse. Tagging unstructured data with metadata like information about the author, type of content, and other identifying factors can help teams find the right data when it’s stored in the data warehouse and eventually loaded into the BI software.
- Transform: After pulling data from its application of origin, that data must be normalized before it is stored in the data warehouse for future use. For analyses within the business intelligence system to work properly, data from different applications of origin must exist in the same format or else the queries won’t be accurate.
- Load: Now that the data have been extracted from their source systems and normalized through the transform phase, it’s ready to be loaded into the central database, most commonly the data warehouse. Load frequencies will vary by organization. Some businesses may enter new data on a weekly basis while others will do it every day.
A popular data storage framework, Hadoop is an infrastructure for storing and processing large sets of data. Though Hadoop stores data, it does so differently than a traditional data warehouse. Hadoop uses a cluster system – Hadoop Distributed File System or HDFS – that allows users to store files in multiple servers.
Analyzing big data with business intelligence software
Regardless of whether businesses choose to store their data in a data warehouse or run queries on the source system, data analysis and the resulting insights make the field appealing to business users. Analytics technologies vary in terms of complexity, but the general method of combining large amounts of normalized data to identify patterns remains consistent across platforms.
Also known as “data discovery,” data mining involves automated and semi-automated analyses of sets of data to uncover patterns and inconsistencies. 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.
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.
Data analytics with business intelligence software
Perhaps one of the most exciting aspects of BI, predictive analytics applications function as an advanced subset of data mining. As the name suggestions, 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 realm of machine learning, where software actually learns from past events to predict future consequences. For our purposes, let’s focus on the three main forms of predictive analysis:
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 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.
Natural language processing
Data comes in three main forms: structured, semistructured, and unstructured. Unstructured data is the most common, and includes text documents and other types of files that exist in formats that computers can’t read easily.
Unstructured data can’t be stored in rows or columns, which makes it impossible for traditional data mining software to analyze. 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. Using the right software, a business can set up a rule to track keywords or phrases—a business’s name, for example—to find patterns in how customers use that language. Natural language processing tools also measure customer sentiment, provide insight into lifetime customer value, and learn customer trends that can inform future product lines.
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Business intelligence software for corporate reporting
The previous two applications of business intelligence software dealt with the mechanics of business intelligence systems: how business data is stored, and how software refines this data into meaningful intelligence. Business intelligence reporting focuses on the presentation of these findings.
Online analytical processing (OLAP)
Online analytical processing (OLAP) uses multidimensional databases to enable users to query data warehouses and create reports that view data from multiple perspectives. OLAP gives business intelligence software the ability to combine data, drill down into single metrics, and view data for combinations of single metrics that are unobtainable in a traditional spreadsheet setup.
For example, a supply chain’s data metrics can include location, SKU, date of purchase, salesperson, and expiration date. OLAP tools can provide the analysts with a clear picture of any combination of these metrics. That provides analysts with the power to surface insights that would otherwise be hidden within two or three-dimensional spreadsheets.
One of the more popular trends in BI, data visualization allows companies to graphically display the results of data mining or other analytics. Presenting findings in a visual format like a graph, chart, or on a map, provides immediate insight into the most important metrics—insights that do not surface within the context of a spreadsheet. As part of a broader shift towards better BI usability, the data visualization UX may become a larger factor in the software purchasing decision.
Not every business user needs full access to everything available in the dashboard. Most employees only need access to a dashboard of their most important metrics. It gives at-a-glance access to a range of predefined visualizations. While each company can define its own dashboards based on custom business needs, some possible dashboard setups are
- Sales dashboard that includes the total number of leads and prospects in each stage of the sales funnel, KPI metrics of the total number of meetings scheduled per salesperson, a total revenue leaderboard, gas gauge tool that shows total revenue toward monthly goal
- Marketing dashboard that shows a line chart with the total number of marketing qualified leads per day, top performing blog posts per month, latest social posts.
- Customer success dashboard with visualizations for the total number of open tickets, number of closed tickets per day, average time to close, ticket totals leaderboard
- IT support dashboard with key metrics regarding sprint progress, total number of open bug tickets, current on-call developers, feature request leaderboard
Alerts and notifications
While dashboards and reports greatly extend the usability of business intelligence software for non-IT users, alerts and notifications can provide even further practical applications for all business users. Alerts notify users who don’t spend most of their time in the tool to data changes that need immediate attention.
When companies set alerts for thresholds of high and low performance, they can track when they need to mobilize a response or investigate an issue before it becomes an emergency. Even better, companies that set alerts for goal metrics can celebrate and recognize their team efforts early and often.
State of the business intelligence market
- A 2018 Dresner Advisory report showed that nearly 50 percent of business intelligence users find “making better decisions” a critical objective for their projects, followed by 35 percent of BI users who rate cost savings and revenue growth as critical business objectives covered by BI.
- Raconteur estimates that 90 percent of large global companies will have a chief data officer (CDO) in place by 2019 to drive revenue growth, cost savings, and decision making.
- BI-Survey.com found that data quality management, data visualization, and self-service BI are the three most important trends in business intelligence. The same survey found the most growth in interest for data preparation for business users between 2016 and 2019.
These statistics show the growing use of business intelligence outside of the IT environment. As business users see the value of data analytics within diverse departments, the demand for business intelligence has risen. Departments see how data visualizations can provide instant answers to questions that have long been answered via gut feeling or guessing, and they want to know how they can also tap into these tools to make better decisions and drive revenue.
In-memory database processing utilizes RAM instead of disk or hard drive processing in order to read information. Accessing information in this manner increases the application performance exponentially. The increasing power of RAM in our computing environments coupled with the demand for more agile systems means this software has a large stake in the future of BI. Dramatic drops in memory prices are making it a more popular option to running analysis through multidimensional databases and cubes.
Use of business intelligence software across business departments
More and more, BI users aren’t IT staff; they’re employees with a standard amount of technological savvy that want to harness the power of BI to get a competitive advantage.
Consequently, the design of reporting mechanisms and ease of use of analytics functions are being driven toward a lower barrier of access. No longer is it enough to have excellent analysis or data warehousing features; they must be usable by both IT experts and business users with no analytical experience..
Many of the major BI vendors—SAP, IBM, Microsoft, and SAS—all responded to the uprising of new, smaller companies that offered easy to use visual function by totally redesigning their interfaces. A 2018 Dresner Advisory Services report found that the major motivation for BI adoption comes from business executives, operations, and sales divisions. Several vendors are specializing in the ‘self-service’ BI space, including Tableau and TIBCO Spotfire, which we compare in our post Tableau vs Spotfire.
Business intelligence software promises to clarify business analytics for the most non-technical of employees, which has driven the demand for embedded analytics tools. These tools let companies build data visualizations within their BI software, and dynamically serve those visualizations to internal and external customers within company apps.
Embedded analytics save companies thousands of hours and hundreds of thousands of dollars they would otherwise use to build reporting and analytics dashboards and tools from scratch. These tools now give business users access to custom, plug-and-play visualizations, greatly speeding the time to market.
Choosing the right business intelligence software
Comparing all the features these tools offer side by side can be a daunting task, but we can help you shave hours off your software search. Contact us today or fill out the form at the top of the page to start the process. We’ll send you a set of recommendations that fit your feature requirements and data needs.
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