Data Center Analytics Dashboard for Storage Cost Reduction
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Solution Overview
Problem
Organizations face challenges with data duplication across disparate systems, leading to increased costs and inefficiencies in storage and maintenance, as well as a lack of ability to discover collaborative opportunities within their data.
Innovation Solution
A data center analytics and dashboard system that utilizes semantic analysis and graphical representations to evaluate the relative value of data across multiple sources, enabling efficient storage management, detection of duplicate content, and identification of collaborative opportunities through analytics such as document originality, corpus storage volume, and data source ingest timeline.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If organizations maintain multiple disparate systems to store and process data, then data can be preserved across different platforms, but storage costs and maintenance costs increase significantly
Solution Approach 1:
The patent consolidates data from multiple disparate systems into a unified data center environment. The system integrates structured data, semi-structured data, and unstructured data from various sources into a single centralized location, eliminating the need to maintain separate storage systems while preserving all data assets.
Solution Approach 2:
The unified data center is designed to handle multiple types of data (structured, semi-structured, unstructured) from various sources using a single platform. This multi-functional system can store, process, and analyze different data types simultaneously, replacing the need for multiple specialized systems.
2Reliability
If duplicate data is stored across multiple systems, then data availability is maintained, but data synchronization issues occur and outdated information persists
Solution Approach 1:
The system merges duplicate data from multiple sources into a single unified repository. By consolidating data that exists in multiple systems into one central location, the patent eliminates synchronization conflicts and ensures that all users access the same current version of the data.
Solution Approach 2:
The unified data center implements data quality monitoring and validation mechanisms that provide feedback on data consistency and accuracy. This feedback loop ensures data integrity by detecting and preventing synchronization issues before they propagate across the organization.
3Quantity of substance
If hardware is purchased to support duplicate data storage, then data capacity is increased, but unnecessary hardware costs are incurred
Solution Approach 1:
The patent consolidates storage capacity from multiple distributed systems into a single unified data center. This consolidation achieves the same or greater total storage capacity while eliminating redundant hardware investments, as the unified system serves all data storage needs centrally.
Solution Approach 2:
The system identifies and eliminates redundant hardware resources that were previously necessary to support duplicate data storage. By recovering these unused or underutilized hardware assets, the organization reduces capital expenditure and operational costs while maintaining adequate storage capacity through efficient resource allocation.
4Ease of operation
If users store multiple copies of the same documents in enterprise storage systems, then data access flexibility is improved, but storage requirements increase
Solution Approach 1:
The unified data center consolidates multiple copies of the same documents into a single centralized repository. The system maintains data access flexibility by providing multiple access points and interfaces to the unified storage system, while eliminating the need to store duplicate physical copies across distributed user systems.
5Device complexity
If organizations lack centralized data analytics, then data remains siloed in disparate systems, but the ability to discover collaborative opportunities is reduced
Solution Approach 1:
The patent implements centralized data analytics capabilities within the unified data center, merging previously siloed analytical functions into a single comprehensive system. This consolidation enables cross-functional data analysis that reveals collaborative opportunities across different departments and business units.
Solution Approach 2:
The unified data center acts as an intermediary platform that connects previously isolated data systems and enables data-driven collaboration. By providing a central hub for data integration and analytics, the system mediates between different organizational units and facilitates discovery of collaborative opportunities through shared insights.
Data Source
AI summary
A method and system to evaluate data efficacy across an enterprise is disclosed. The method includes the step of indexing a set of data sources that include at least one of structured and unstructured data artifacts. The method further includes accessing the indexing on the one or more data sources with a computer. The method further includes the step of generating a plurality of analytics about the data sources based on the indexing, wherein the analytics include a plurality of: a document originality analytic, a corpus storage volume analytic, a data source ingest analytic, a document type analytic, and an analysis analytic. The method further includes displaying, on a display device, an interactive visualization of results based on the analytics, wherein the visualization comprises at least one of: a histogram, a graph, a timeline, a panel, a list, a chart, a popup, and a table.