What is advanced analytics?
Advanced data analytics can help drive innovative business decision making. Advanced analytics and reporting use sophisticated tools for data mining, big data and predictive analytics to mine data for important trends, patterns, and performance. As the amount of valuable data your company gathers increases, so will the need to use that data for insights that provide a competitive advantage.
Advanced analytics techniques
Advanced analytics tools dive deep into data to help you better understand why something is happening, identify trends, generate predictive insights, or optimize for a desired outcome. Employing these techniques will help build a solid foundation for advanced analytics to mature. Some advanced analytics methods include:
Data mining is the process of identifying sequences, relationships, or anomalies in large amounts of raw data. Connections within data sets can then be created and analyzed to produce information about opportunities and risks.
This advanced analytics technique uses computational methods to find patterns or inferences in data, and automatically create statistical models to produce reliable results with minimal human intervention.
Cohort analysis is an advanced data analytics technique that develops broadly applicable insights by examining the behavior of a group of people. Read Making Friends with Cohort Analysis to get five ways to deep dive into your customer data using cohort analysis.
Cluster analysis is a way to recognize differences or similarities in sets of data and visually present that data to make comparisons easier. Box plot visualizations are a standard way of showing the distributions of data in a cluster.
Retention analysis uses advanced data analytics to understand cohorts of users or customers. These insights can help you determine what factors influence retention and inform customer growth strategies. Download our whitepaper on Retention Analysis and the Data that Drives It to learn more.
Complex event analysis
Complex event analysis, also called complex event processing (CEP), is an advanced data analytics process that aggregates and analyzes event data coming from multiple sources as an event happens. Complex event analysis identifies cause-and-effect relationships in data to provide real-time insight that can influence business decision-making.
Predictive analysis, in conjunction with data mining, statistical methods, and machine learning, studies data to predict the likelihood of a future outcome and inform business forecasting appropriately.
What are the benefits of advanced analytics?
Modern advanced data analytics tools allow you to analyze and operationalize more data, faster. The insights generated also empower better and more accurate decision-making that drives enhanced ROI.
More time to focus on strategy
The right selection of advanced analytics tools can reduce the resources needed to streamline the process of sorting and analyzing data. Enterprises can input large amounts of data and operationalize the insights faster. This frees up analysts to take on more valuable projects and gives leaders more time to focus on strategy for business improvement.
More accurate decision-making
Advanced analytics gives deeper insight into data, turning that data into actionable information. This information allows businesses to make fact-based decisions quickly and accurately.
Gain a better ROI
Advanced analytics tools can help you effectively decide where and when to employ resources, which increases operational efficiency and reduces costs. Advanced analytics also uncover customer needs so you can develop and evolve products and services and deliver innovation ahead of your competition.
Data sharing and user empowerment
Advanced analytics tools can support the integration of data from disparate sources and enable secure data sharing across your organization. When data is accessible to employees, they are empowered to identify and act on opportunities—further growing your enterprise.