Cross-Silo Enterprise Data Acquisition System
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Solution Overview
Problem
Existing data acquisition and reporting tools lack the ability to gather, report, and analyze data across multiple silos within an enterprise, limiting business executives' access to cross-silo information for decision-making.
Innovation Solution
A cross-silo enterprise data acquisition, reporting, and analysis system that automatically gathers and consolidates machine-generated and human-generated data from various silos, computes key performance indicators (KPIs), and provides a graphical user interface for data entry, visualization, and role-based access control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data is pre-processed and only pre-specified data items are extracted and stored, then data retrieval and analysis efficiency is improved, but data flexibility and the ability to investigate different aspects of machine data are lost
Solution Approach 1:
The system performs preliminary indexing of all machine data at ingest time, creating a searchable structure that enables efficient retrieval without pre-specifying analysis needs. This preliminary action prepares the data in advance while maintaining full flexibility for future analysis queries.
Solution Approach 2:
The system creates a universal data index that serves multiple analysis purposes simultaneously. A single indexed data structure supports various types of analysis queries across different business units and data aspects, eliminating the need for separate pre-processed data sets for each analysis scenario.
2Adaptability or versatility
If massive quantities of minimally processed machine data are stored for later retrieval, then data analysis flexibility is improved, but data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary indexing of all machine data at ingest time, creating a searchable structure that enables efficient retrieval without pre-specifying analysis needs. This preliminary action prepares the data in advance while maintaining full flexibility for future analysis queries.
Solution Approach 2:
The system replaces traditional mechanical data processing approaches with a software-based indexing and search architecture. This substitution enables rapid querying of massive data sets through efficient data structures and algorithms rather than sequential processing.
3Measurement precision
If existing CRM software applications are used to gather and report sales data, then sales group data analysis is improved, but cross-silo data gathering and analysis capability is lost
Solution Approach 1:
The system merges data gathering and analysis capabilities across multiple business units and silos into a unified platform. It combines machine data, human-generated data, and data from various departments (sales, marketing, engineering, HR) into a single cross-silo data acquisition system that enables enterprise-wide analysis.
Solution Approach 2:
The system creates a universal data platform that serves multiple business units and analysis needs simultaneously. It provides a common architecture that can gather, store, and analyze data from any enterprise silo, replacing the need for separate departmental tools while maintaining specialized analysis capabilities.
Data Source
AI summary
Disclosed is a system and method for cross-silo acquisition, reporting and analysis of enterprise data. A computer system receives enterprise data related to various vertical units of an enterprise, including machine-generated data and human-generated data. The computer system stores the machine-generated data with associations to at least some of the human generated data, and associates persona data representing a plurality of personas with the plurality of vertical units of the enterprise, such that at least one persona is associated with each of the vertical units. The computer system further associates a plurality of user-defined key performance indicators (KPIs) with the personas, and associates each of a plurality of users with at least one of the personas. The computer system computes the KPIs based on the enterprise data, and controls access by the users to the computed KPIs, based on personas to which the users are assigned.