Intelligent Apps and Analytics
Intelligent Applications (Apps) and Analytics embeds Artificial Intelligence (AI) and analytic technologies directly into applications such that applications leverage and act upon analytical feedback. Simply put, Intelligent Apps are aware of their performance and their interaction with the user and other applications; they autonomously learn, adapt, and discover patterns of behavior. The more an Intelligent App is used, the more it learns and can leverage predictive analytics to adapt when it is most timely. The built-in machine learning algorithms leverage big data sources to process vast amounts of data to continuously improve.
- Immediate application of predictive analytics
- Business decisions immediately refined based on all of data available
- Applications continually increase business value as algorithms learn and immediately feed back into processes
- Big data sources are no longer isolated to separate platforms
- Actionable analytics
- Decision driving data visualizations
- Suggested business actions in near real time
- Non-obvious associations revealed in a timely manner such that appropriate actions can be taken
- Organizations with complex business processes with many decision factors
- Organizations with very large data sets that want to leverage the data they have
- Organizations where immediate business realizations can dramatically increase effectiveness
- Business buy-in can be slow when process decisions are challenged by software; need progressive deployment of features to gain confidence.
- Results of AI must be interpreted before actions are taken.
- Non-obvious associations may be difficult for analysts to visualize or correlate.
- Integration points generally cross technological and organizational boundaries which requires coordination and possibly reorganization.
- Identify how business leadership would like to leverage their analytic data
- Identify data sources available to business for decisions
- Engineer and architect big data solution
a. Assess current IT environment
b. Determine best products based on cost, approvals, etc.
- Identify application points where predictive analytics can inject information
a. Detail questions that the analytic engine can answer at that point in time
b. Determine how analytic delivery can assist business process
- Engineer application and analytic integration including integrated security and auditing model
- Define success criteria through measurable criteria
a. Key Performance Indicators (KPIs)
b. Return on Investment (ROI)
- Integrate, test, and implement intelligent apps and analytics solutions with existing IT infrastructures or Cloud services.
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