Invented by Todd Cotton, Karen M. Gardner, Robert Konitzer, Mary Jane VanSant, Agilent Technologies Inc

In today’s digital age, data is the lifeblood of businesses. The ability to collect, store, and analyze data is critical for organizations to make informed decisions and stay competitive. However, with the increasing amount of data being generated, it has become challenging for businesses to manage and track their data effectively. This is where enterprise frameworks and applications supporting meta-data and data traceability requirements come into play.

An enterprise framework is a set of guidelines, best practices, and tools that provide a structured approach to building and managing applications. These frameworks are designed to simplify the development process, improve application quality, and reduce development time and costs. Enterprise frameworks supporting meta-data and data traceability requirements are specifically designed to help businesses manage their data effectively.

Meta-data is data that describes other data. It provides information about the structure, content, and context of data. Meta-data is critical for data management as it helps businesses understand their data and how it is used. Data traceability, on the other hand, is the ability to track data from its origin to its current state. This is important for businesses to ensure data accuracy, compliance, and security.

Enterprise frameworks and applications supporting meta-data and data traceability requirements provide businesses with the tools they need to manage their data effectively. These frameworks and applications offer features such as data modeling, data mapping, data lineage, and data quality management. They also provide data governance capabilities, which help businesses ensure data compliance and security.

The market for enterprise frameworks and applications supporting meta-data and data traceability requirements is growing rapidly. According to a report by MarketsandMarkets, the global metadata management market is expected to grow from $3.07 billion in 2018 to $7.85 billion by 2023, at a compound annual growth rate (CAGR) of 20.2%. The report also states that the data governance market is expected to grow from $1.31 billion in 2018 to $4.37 billion by 2023, at a CAGR of 27.2%.

The growth of this market can be attributed to the increasing amount of data being generated, the need for businesses to manage their data effectively, and the growing importance of data compliance and security. As businesses continue to rely on data to make informed decisions, the demand for enterprise frameworks and applications supporting meta-data and data traceability requirements will continue to grow.

In conclusion, the market for enterprise frameworks and applications supporting meta-data and data traceability requirements is growing rapidly. These frameworks and applications provide businesses with the tools they need to manage their data effectively, ensure data compliance and security, and make informed decisions. As businesses continue to rely on data, the demand for these frameworks and applications will continue to grow, making it an exciting time for the data management industry.

The Agilent Technologies Inc invention works as follows

An application that runs within a web-based enterprise-wide framework, which provides reusable facilities and services such as security and meta-data management, and data traceability, and a framework to support the same. The framework is designed to support decision-making across the entire value chain with a focus on meta-data. The framework supports the activities of an internal or external virtual organization and the inevitable differences in data formats, file types etc. “without requiring massive integration of the different sources of data involved.

Background for Enterprise Framework and Applications Supporting Meta-Data and Data Traceability Requirements

The biopharmaceutical value chain (from disease discovery to post-market surveillance of patients) is supported with a large number of disparate, heterogeneous, and incompatible software and instrumentation. The result is significant inefficiencies and high costs, as well as a lack of control over information contained in these systems. Solving the problems with scientific data management is critical for biopharmaceutical firms to reach their growth and revenue goals. Biopharmaceutical firms have relied on the integration of at least some systems to help solve the problem of scientific data management. These data integration efforts resulted into the creation of large databases (often federated), and/or integrated platforms for scientists.

Document Management Systems

Document management systems are helping to improve scientific data management. They address the content of documents and not just their management. As a rule, however, these systems tend to be more concerned with the syntactic aspect of meta-data, (e.g. section 2 being placed at a specific position), and the management itself of documents.

Documentum has the highest market share of document management systems for managing scientific data. It also provides the most comprehensive document and content management capabilities. Documentum, on the other hand, does not support backwards and forwards traceability, does not seem to offer finely-grained entitlements and does not provide semantic content management.

GMPharma, a joint-product of Documentum & PWC, was developed for the pharmaceutical industry. It meets GMP requirements and manages controlled documents. It doesn’t appear to support fine-grained entitlements or semantic content management.

PharMatrix (Opentext) is a system that pharmaceutical companies can use to manage and coordinate information. It stores, captures and distributes information during the phase of drug discovery. It does not provide backwards and forwards traceability, finely-grained entitlements or semantic content management. “It is only available on Windows.

Astoria is a content management system based on XML. It addresses the syntactic aspect of organizing a file (e.g. which parts are videos, which are graphs and the like), and does not appear to support backwards and forwards traceability, nor does it offer fine-grained rights.

CyberLab (Scientific Software) is a 21 CFR-11 compliant system which manages laboratory data through cataloging, indexing and keyword retrieval. This includes raw data, compliance records, as well as storing, archiving and storing keywords. The software is only available for Windows. “Auditability (who, when, and where did what) is supported but fine-grained tracability and fine entitlements aren’t supported.

CoreDossier [ESPS] and Liquent are document management software for regulated documents. They do not support semantic metadata, entitlements or fine-grained traceability.

Integrated Research Platforms

An integrated research platform (also called an integrated database) is a system of software that allows a scientific user to manage, analyze and access integrated scientific data gathered from external and internal databases. A solution that includes several products (usually proprietary) is an integrated research platform.

The SRS [Lion] System provides access to external databases, and allows for rudimentary management of the information resulting from this. These products are a collection of software packages for data analysis and visualization. The SRS [Lion] does not support entitlements or traceability at any level. Meta-data are used minimally.

Synergy” (NetGenics), a data management tool, allows users to access normalized integrated data on gene expression from multiple sources. It also includes tools for data analysis. Synergy does not support traceability or semantic meta-data, entitlements, or regulatory requirements.

UNIFY VISION ARCHIVE [NuGenesis] is a data management/document repository which captures data from different sources and allows viewing,?cutting and pasteing? “UNIFY, VISION and ARCHIVE [NuGenesis] make up a data management system/document repository that captures data from disparate sources, allows viewing and?cutting-and-pasting? guidelines. This document repository/data management system does not support finely-grained traceability or entitlements.

GenoMax [InforMax]” is a data-mining platform that integrates the results of genomic data analyses. It can process massive amounts of data. It does not seem to support meta data, entitlements or tracability.

Knowledge Management Systems/Decision Support Systems”.

Knowledge Management Systems/Decision Support Systems are designed to enhance a certain type of decision-making process. These systems are mostly simulation software systems that target very specific areas in the pharmaceutical industry (e.g. modeling of organic molecules). The present invention could provide services for these types of systems.

Insight II (MSI) provides a variety of simulation and modelling systems that target Molecular Modeling.” It doesn’t appear to support metadata, entitlements, or traceability.

MineSet (SGI) provides data visualization in 3D format, and the capability to subject data to ‘what if? analyses.

PhysioLab (Entelos) predicts the results from experimental studies but does not appear support meta-data entitlements or traceability.

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