While a database can potentially hold a wealth of information and trends, users can only harness that potential through the data query. A query is a request for data written in a special syntax, often Structured Query Language (SQL), from a database that extracts information and formats it for consumption and analysis. Data querying can perform calculations, automate tasks or dig deeper through data mining, which uncovers hidden trends and relationships between data points. Though more specialized for the fields of data science and big data than business intelligence specifically, it is certainly a feature you can consider depending on your business needs.
Query Multiple Data Sources
Readable and Modifiable SQL
Data analysis turns raw information into actionable insights, helping businesses maximize the value of their data to make better business decisions. Data analysis assists users in extracting value from operational information and empowers them to take a deeper look into their business. There are four main types of analytics: descriptive, predictive, prescriptive and diagnostic. Each is useful and important in its own way and not all BI solutions can perform every type of analysis, so it’s important to identify your data analysis needs and make sure that prospective solutions can address them.
Segmentation and Cohort Analysis
Scenario and What-if Analysis
Statistical and Regression Analysis
Time-Series Analysis and Forecasting
Predictive Analytics and Predictive Modeling Markup Language (PMML) Support
Text Mining (Text Analytics) and Sentiment Analysis
Social Media and Web Analytics
Advanced Data Analysis using Python and R
Internet of Things (IoT) and Streaming Analytics
Augmented analytics play a huge role in enabling organization-wide data literacy, empowering all users with the power of self-service BI, while freeing up IT and data scientists professionals for more specialized projects. Augmented analytics is the use of technologies such as machine learning, artificial intelligence and automated algorithms to analyze data, accelerating the work done by human data analysts and data scientists. Through augmented analytics, modern BI solutions can now also process data faster and return deeper, more valuable insights with minimal human bias. They can provide automated predictions and prescriptions, helping users prepare for what-ifs. Through natural language generation and natural language processing, they can simplify data analysis and make actionable insights accessible to users without coding knowledge.
No longer just a buzzword, augmented analytics is often referred to as the future of business analytics and Gartner predicts that by the end of 2020, more than 40% of data science tasks will be augmented. If you want a BI solution that’s fully equipped for the future, augmented analytics may be a feature you want to consider for your requirements list.
Example of augmented analytics, featuring a system automatically turning data into written sales reports
Source: Oracle Analytics Cloud, a vendor on our BI augmented analytics leaderboard.
Augmented Data Preparation
Automated Descriptive Insights
Key Driver Analysis
Automated Anomaly Alerting
Autogenerated and Analyzed Segments or Clusters
Auto generated Forecasts or Predictions
Contextualized or Relevant Insights
Automated Feature Generation or Selection
Automated Algorithm Selection and Model Tuning
Automated Model Packaging and Monitoring
Text-Based and Voice-Based Natural Language Search
You may think that reporting and analysis are the same thing, but they are very different in terms of their purpose and delivery. Reporting organizes data into information that displays how different areas of a business are performing, while analysis transforms that data into insights. Reporting shows what is happening in a business, while analysis explains why it’s happening and what can be done about it. While reporting without BI tools is often IT-centric, many BI solutions break down this barrier and allow for self-service reporting so that users can generate their own reports. This helps users get reports in minutes, not days, while alleviating the burden on a company’s IT department to deliver reports. The productivity and efficiency that reporting can provide make it important to consider in your business intelligence report requirements template.
Screenshot example from Board, a vendor on our BI leaderboard for Reporting
Text-Based Natural Language Reports
Voice-Based Natural Language Reports
Embedded analytics refers to a BI solution that can be embedded into other programs to perform in-application analysis, offering features like reporting, data processing, drill-down and more from directly within the system. It delivers the benefits of business intelligence and data-informed decision making to users of an existing platform without requiring that they access a separate BI application. If you’re looking to add embedded analytics to an existing platform and don’t want to have to build an in-house analytics solution within your chosen application, you can purchase an embedded BI tool that supports out-of-the-box integration.
Here are some embedded BI requirements:
Mobile App Embeddability
Integrated Workflow Actions
Get our BI Tools Requirements Template