Application intelligence refers to an application's inherent understanding of blending systems together. We say that software has application intelligence only if it understands how to connect to different systems and knows how data is stored, structured, and relates to other data. It also includes knowledge of undocumented idiosyncrasies or special "features" that only domain experts understand, even if you don't.
Companies have more options than ever to invest in a growing collection of best-of-breed systems. It's no longer practical for companies to maintain experts in systems, such as on-premise ERPs and cloud CRMs. By using application intelligence, companies can substantially decrease system complexity and increase usability.
Most solutions without application intelligence are singularly obsessed with connecting to systems. We think it's essential to go beyond connecting to systems by helping end-users prepare, clean, and enrich data for blending and streaming.
Working directly with source data instead of exporting it to spreadsheets is mandatory because modern enterprise applications can generate significant volumes of data. Managing and organizing this data manually in spreadsheets is no longer possible because it no longer fits within the constraints of spreadsheets. The only way to efficiently work with millions of records is by connecting directly to source systems and leveraging application intelligence to facilitate an end-user experience.
Application intelligence helps ready data for end-users by leveraging inherent system knowledge and supplementing it with artificial intelligence and machine learning. Application intelligence introduces essential requirements for total data integration.
The basic functionality of replacing system fields with recognizable names and adding contextual information like tags and descriptions makes understanding data easier. For example, JDE ERP tables coded for system organization are not end-user-friendly; (e.g., the Accounts Receivable table is called F03B11). Overwriting table and field names and adding intelligent tags like "Financial Customer Info" can help restructure and categorize data.
Application intelligence augments system data with time intelligence by transforming text fields into dates allowing users to report by specific periods. Drill-down functionality is enabled by automatically linking relevant summary data to transactions.
New dimensions of analysis can be introduced with smart groups. Smart groups automatically associate attributes like geographic information, state, for example, to regional summary groups so data can be aggregated for new and unexplored perspectives.
Data output preparation is as vital as data input preparation. Application intelligence facilitates shaping data for specific output purposes. For example, suppose one system holds period values in rows, and another system holds period values in columns. Application Intelligence simplifies pivoting data into a usable structure and streams it to other systems that require a particular shape. Planning applications often require data shaping. Application intelligence fully automates the process of shaping data.
Application intelligence and artificial intelligence are capable of working with massive datasets using algorithms and machine learning to assist in cleaning, grouping, and driving insights. AI removes the need for an IT skillset and frees up people to add the type of value a machine could never deliver. With application intelligence, the process of preparing, cleaning, and streaming data ensures the delivery of high-quality experiences for end-users.