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TopQuadrant Whitepapers

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Maturing Reference Data Management

Abstract:This paper presents a practitioner informed roadmap intended to assist enterprises in maturing their Enterprise Information Management (EIM) practices, with a specific focus on improving Reference Data Management (RDM). Reference data is found in every application used by an enterprise including back-end systems, front-end commerce applications, data exchange formats, and in outsourced, hosted systems, big data platforms, and data warehouses. How well it is managed has a major impact on every aspect of an organization’s use of data. In this paper, we describe a RDM maturity model and one likely path an enterprise can take to mature RDM. We also describe how RDM intersects with and depends on data governance practices at each maturity level. We invite you to also download the companion white paper to this one: Maturing Information Governance with TopBraid EDG

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Maturing Information Governance with TopBraid EDG

Abstract:This paper provides a brief overview of the importance of information governance — and data governance as a key component of it. It outlines Enterprise Information Management (EIM) capabilities as a foundation to align data governance practices to in order to mature and perform them well. TopBraid Enterprise Data Governance (EDG) is described in terms of how it supports EIM capabilities, based on semantic standard-based approach that enables EDG to manage the entire range of enterprise information assets and their connections. The paper then focuses on Reference Data Management (RDM) as an integral part of EIM. It discusses how TopBraid EDG can help with progressing “RDM maturity” as it is presented in terms of formal RDM practices in the companion white paper to this one: Maturing Reference Data Management

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How Can Semantic Information Management Help to Preserve Meaning in a Dynamic Data Environment?

Abstract: With information streaming in from more varied sources and at a faster pace than ever before, organizations are having an increasingly difficult time deriving accurate meaning from their data. Data governance systems that were once able to organize and process enterprise information are now too slow and siloed to handle what has become a rapidly-evolving and heterogeneous data landscape. As a result, companies may mischaracterize otherwise accurate data, putting their reliability and competitiveness at stake. By connecting all kinds of data and its metadata in a more accessible way than ever, semantic data management systems empower users, data stewards and analysts to make more accurate use of, gain precise and timely insights from, and unlock the true meaning behind their business’s data.

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MDM Institute Field Report: TopBraid Reference Data Manager

BOTTOM LINE: TopQuadrant’s TopBraid RDM solution is a new entrant into the reference data marketplace. It is a self-service reference data governance hub for subject matter experts that provides “full spectrum” reference data to comprehensively support an enterprise’s IT portfolio. Due to its agile-style approach to business data modelling, TopBraid RDM appears to be an excellent choice as a flexible and low cost (yet fast time to value) web-based solution for reference data governance. Additionally, its strong semantic querying features (based on open standards), taxonomy support, and mappings/crosswalks promote business user and data steward self-service that requires only modest initial IT support. Moreover, TopBraid RDM is a purpose-built reference data management solution rather than providing capabilities derived from an operational or consolidation MDM hub. During 2015-16, organizations evaluating reference data solutions where user-directed, agile governance of reference data is the key use case should consider the TopBraid RDM solution – independent of other MDM investments.

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The Foundations of Successful Reference Data Management

Abstract: Data management is becoming more and more central to the business model of enterprises. There are different types of data, and each type of data has its own special characteristics, challenges and concerns. Reference data is a special type of data that is found in practically every enterprise application including back-end systems, front-end commerce applications, and in data warehouses. It turns other data into meaningful business information and provides an information context for the wider world in which the enterprise functions. Yet it is rarely managed well — data duplication and inconsistent mappings of reference data across applications frequently lead to costly business errors. This paper discusses the challenges associated with implementing a reference data management solution. It outlines best practices for the governance and management of reference data and describes fundamental capabilities that should be provided in any modern reference data solution.

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Using TopBraid to Work with Spreadsheet Data

Abstract: Much of the data today is held in spreadsheets. Not surprisingly, there is a lot interest in transforming spreadsheet data into RDF. To address the need to capture this type of information and the semantic relationships encoded in the spreadsheet structure, the TopBraid product family offers different capabilities for transforming spreadsheets into the semantic standards representation RDF. This technical whitepaper provides a description of the set of TopBraid capabilities for importing and transforming different types of spreadsheet data into meaningful, structured information in RDF, along with guidance for when and how to use each specific capability.

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Linked Data in the Enterprise

Abstract: An informative whitepaper by Jan Voskuil, CEO of Taxonic, a TopQuadrant services partner and TopBraid distributor in the Netherlands, explains that Linked Data is a method of storing and sharing data that fundamentally differs from the classic methods: the relational database. It highlights the benefits of Linked Data by outlining how it will reduce the cost of modifying IT-systems and data integration. The paper explains just enough of the technology to understand the answer at the conceptual level.

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Controlled vocabularies, taxonomies, and thesauruses (and ontologies)

Abstract: When people maintain a vocabulary of terms—and sometimes, metadata about these terms—they often use different words to refer to this vocabulary. One man's taxonomy may be another woman's thesaurus, and what is an ontology, anyway? The paper describes definitions of each that are not official, standard definitions, but have worked well for us in practical situations and will help you to understand their relationships.

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Towards Executable Enterprise Models: Building Semantic Enterprise Architecture Solutions with TopBraid Suite™

Abstract: This whitepaper presents an overview of the need for and value of dynamic and executable EA models for enterprises. It makes made a case that semantic web technologies and TopBraid Suite are a promising way to accomplish this. The benefits of using Semantic Web technologies for EA solutions include:

  • Every entity is ‘first class’ – no re-engineering data to promote entities as requirements change

  • Flexible schemas – add new kinds of data (metadata) as systems change

  • It is easy to link external information – refer to reference models, standards, any external information as a ‘web’ connection (URI)

TopBraid Suite encompasses the full spectrum of Semantic Web tools and technologies necessary to build and deploy Semantic Enterprise Architecture solutions. Benefits include:

  • Standards-based capture and management of enterprise information models and metadata based natively on RDF, RDFS and OWL

  • Information aggregation across diverse enterprise data sources using TopBraid Composer’s multiple import modes – information from other EA systems, spreadsheets, databases, etc.

  • Support for workflows and alerts

  • Fully customizable and extendable taxonomies for organizing IT standards across the enterprise

  • Support and compliance with the Federal Enterprise Architecture (FEA) including Federal Transition Framework (FTF) and the Data Reference Model (DRM)

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Semantic Web Solutions at Work in the Enterprise

Abstract: The amount of digitized information is growing at unprecedented rate. By 2007 the size of individual databases at many organizations reached up to hundreds and in some cases thousands of terabytes. For example, in 2004 AT&T had 11 exabytes (107 TB) of wireline, wireless and Internet data. This is an equivalent amount of data to that held by 1 million Libraries of Congress. Wal-Mart had 500 terabytes of transactional data and was adding 107 transactions per day. On average, the size of transactional databases doubles every five years with core databases doubling every two years. Data reporting and analysis warehouses (OLAP stores) triple in size every three years. On the web, by 2007 there were 29.7 billion pages, roughly five pages for every man, woman, and child on the planet. In 2006 alone, the size of the information created or replicated worldwide was 161 exabytes (108 TB).

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FEA Ontology Modeling White Paper

Abstract: This white paper describes the design of the Federal Enterprise Architecture Reference Ontology Models (FEA RMO). Five models have been encoded in OWL (W3C standard Web Ontology Language). The paper is organized in the following sections:

  • FEA RMO Ontology Models – this section describes FEA RMO architecture and identifies business value and potential of the models

  • Assessment of the Semantics of the FEA – this section describes some inconsistencies, conflicts and omissions discovered in the process of formalizing FEA framework as ontology models

  • Use Cases of the FEA RMO – this section describes representative use cases

  • FEA RMO Development Approach – this section describes the design patterns used by FEA RMO; it is intended for people interested in using and extending the models

  • Tooling Issues – this section describes technical issues with the state of the art ontology tools discovered during FEA RMO development

  • Recommendations and Future Plans – this section describes our “wish list”, defining where and how we would like to see the work progressing; it also identifies FEA RMO deployment and governance options.

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Dictionary of Search Terminology

Abstract: The problem of Information Retrieval continues to attract increasing attention as the oceans of unstructured data organizations have already captured, and are continuing to capture, keep on growing. At the same time accurate and speedy access to the information is becoming ever more difficult.

In selecting the right search tool many factors need to be taken in to account, including:

  • Nature of typical search queries (studies have found important differences between e-commerce product search, customer service / tech support search and other types of searches)

  • Form, format and location of the information and knowledge sources

  • Organization’s information publishing processes

  • Availability of the metadata, custom dictionaries and taxonomies

  • Readiness of an organization to engage in a continuous process necessary to achieve search precision

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