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The chief aim of data analytics is to apply statistical analysis and technologies on data to find trends and solve problems. Data analytics has become increasingly important in the enterprise as a means for analyzing and shaping business processes and improving decision-making and business results.
Descriptive analytics uses historical and current data to describe the organization’s present state by identifying trends and patterns. Predictiveanalytics: What is likely to happen in the future? Prescriptive analytics: What do we need to do? Examples of business analytics. This is the purview of BI.
BRIDGEi2i is recognized as cool for its advanced analytics solution, BRIDGEfunnel, an AI-based diagnosticanalytics tool, supported by predictiveanalytics functions, that can be used in lieu of enterprise-grade advanced analytics platforms. Email: venkat.subramanian@bridgei2i.com.
Having the right data strategy and data architecture is especially important for an organization that plans to use automation and AI for its data analytics. The types of data analyticsPredictiveanalytics: Predictiveanalytics helps to identify trends, correlations and causation within one or more datasets.
Constellation Research predicts that by 2020, 60 percent of mission-critical data will be accessed, rather than owned by enterprises – with external sources including SaaS, social networks, third-party enrichment data and partner information. Data-management capabilities, including data integration and self-service data preparation.
Enterprise Artificial Intelligence. Enterprise Artificial intelligence (AI) is a common jargon used to refer to how an organization integrates artificial intelligence (AI) into its infrastructure to drive digital transformation. The aim of predictiveanalytics is, as the name suggests, to predict and forecast outcomes.
Originating with Gartner, this chart includes the analytic features needed for a full analytics strategy, and what our AI team believe to be the absolute future of analytics – Cognitive Analytics. . In order to know where to go, you must first find yourself on this chart. A Centralized Approach.
Data analysts leverage four key types of analytics in their work: Prescriptive analytics: Advising on optimal actions in specific scenarios. Diagnosticanalytics: Uncovering the reasons behind specific occurrences through pattern analysis. Descriptive analytics: Assessing historical trends, such as sales and revenue.
There are other dimensions of analytics that tend to focus on hindsight for business reporting and causal analysis – these are descriptive and diagnosticanalytics, respectively, which are primarily reactive applications, mostly explanatory and investigatory, not necessarily actionable. This is predictive power discovery.
Traditional BI Platforms Traditional BI platforms are centrally managed, enterprise-class platforms. Leading research and consultancy company, Gartner describes the path that businesses take as they move to higher levels: Descriptive Analytics: Describe what happened (e.g., DiagnosticAnalytics: No longer just describing.
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