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Published By: DATAVERSITY     Published Date: Nov 05, 2014
Ask any CEO if they want to better leverage their data assets to drive growth, revenues, and productivity, their answer will most likely be “yes, of course.” Ask many of them what that means or how they will do it and their answers will be as disparate as most enterprise’s data strategies. To successfully control, utilize, analyze, and store the vast amounts of data flowing through organization’s today, an enterprise-wide approach is necessary. The Chief Data Officer (CDO) is the newest member of the executive suite in many organizations worldwide. Their task is to develop and implement the strategies needed to harness the value of an enterprise’s data, while working alongside the CEO, CIO, CTO, and other executives. They are the vital “data” bridge between business and IT. This paper is sponsored by: Paxata and CA Technologies
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chief data officer, cdo, data, data management, research paper, dataversity
    
DATAVERSITY
Published By: Stardog Union     Published Date: Mar 13, 2019
Enterprises must transition to contextualizing their data instead of just collecting it in order to fully leverage their data as a strategic asset. Existing data management solutions such as databases and data lakes encourage data sprawl and duplication. However, true data unification can be achieved with a Knowledge Graph, which seamlessly layers on top of your existing data infrastructure to reveal the interrelationships in your data, no matter its source or format. The Knowledge Graph is also a highly scalable solution since it retains every analysis performed as a reusable asset -- drastically reducing the need for data wrangling over time. Download Knowledge Graphs 101 to learn how this technology differs from a graph database, how it compares to MDM and data lake solutions, and how to leverage artificial intelligence and machine learning within a Knowledge Graphs.
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Stardog Union
Published By: Tamr, Inc.     Published Date: Feb 08, 2019
Traditional data management practices, such as master data management (MDM), have been around for decades – as have the approaches vendors take in developing these capabilities. And they were well-equipped for the problem at hand: managing data at modest size and complexity. However, as enterprises mature and start to view their data assets as a source of competitive advantage, new methods to managing enterprise data become desirable. Enterprises now need approaches to data management that can solve critical issues around speed and scale in an increasingly complex data environment. This paper explores how data curation technology can be used to solve data mastering challenges at scale.
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Tamr, Inc.
Published By: DATAVERSITY     Published Date: Jun 14, 2013
This report analyzes many challenges faced when beginning a new Data Governance program, and outlines many crucial elements in successfully executing such a program. “Data Governance” is a term fraught with nuance, misunderstanding, myriad opinions, and fear. It is often enough to keep Data Stewards and senior executives awake late into the night. The modern enterprise needs reliable and sustainable control over its technological systems, business processes, and data assets. Such control is tantamount to competitive success in an ever-changing marketplace driven by the exponential growth of data, mobile computing, social networking, the need for real-time analytics and reporting mechanisms, and increasing regulatory compliance requirements. Data Governance can enhance and buttress (or resuscitate, if needed) the strategic and tactical business drivers every enterprise needs for market success. This paper is sponsored by: ASG, DGPO and DebTech International.
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data, data management, data governance, data steward, dataversity, research paper
    
DATAVERSITY
Published By: Adlib Software     Published Date: Feb 09, 2018
In financial services, contracts often exist in highly dispersed formats, with jurisdictional complexities and risk aspects that change over time—resulting in high levels of risk. Learn how to transform contracts into actionable, defensible assets with a robust contract intelligence solution.
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Adlib Software
Published By: ASG     Published Date: Apr 02, 2014
This Case Study focuses on a highly successful data lineage project between ASG Software Solutions and a major global financial institution. The initial project which began in 2011 with the primary goal of achieving greater control, awareness, and ownership over the institution’s data assets due to new regulatory and federal audit controls. As the project progressed and the positive relationship between ASG and the Bank deepened, all stakeholders involved began to see much broader potential for the entire project than originally envisioned.
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metadata, data, data management, white paper, case study
    
ASG
Published By: Embarcadero     Published Date: Jul 23, 2015
Whether you’re working with relational data, schema-less (NoSQL) data, or model metadata, you need a data architecture that can actively leverage information assets for business value. The most valuable data has high quality, business context, and visibility across the organization. Check out this must-read eBook for essential insights on important data architecture topics.
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Embarcadero
Published By: TopQuadrant     Published Date: Jun 11, 2018
Data governance is a lifecycle-centric asset management activity. To understand and realize the value of data assets, it is necessary to capture information about them (their metadata) in the connected way. Capturing the meaning and context of diverse enterprise data in connection to all assets in the enterprise ecosystem is foundational to effective data governance. Therefore, a data governance environment must represent assets and their role in the enterprise using an open, extensible and “smart” approach. Knowledge graphs are the most viable and powerful way to do this. This short paper outlines how knowledge graphs are flexible, evolvable, semantic and intelligent. It is these characteristics that enable them to: • capture the description of data as an interconnected set of information that meaningfully bridges enterprise metadata silos. • deliver integrated data governance by addressing all three aspects of data governance — Executive Governance, Representative Governance, and App
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TopQuadrant
Published By: CA Technologies     Published Date: Feb 25, 2016
As combinations of both internal and externally-imposed business policies imply dependencies on managed data artifacts, organizations are increasingly instituting data governance programs to implement processes for ensuring compliance with business expectations. One fundamental aspect of data governance involves practical application of business rules to data assets based on data elements and their assigned values. Yet despite the intent of harmonizing data element definitions and resolution of data semantics and valid reference values, most organizations rarely have complete visibility into the metadata associated with enterprise data assets.
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CA Technologies
Published By: Information Asset, LLC     Published Date: Feb 11, 2014
An In-Depth Review of Data Governance Software Tools: Reference Architecture, Evaluation Criteria, and Vendor Landscape
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white paper, data governance, data, data management, data management white paper, data governance white paper
    
Information Asset, LLC
Published By: CMMI Institute     Published Date: Sep 03, 2014
To drive strategic insights that lead to competitive advantage, businesses must make the best and smartest use of today’s vast amount of data. To accomplish this, organizations need to apply a collaborative approach to optimizing their data assets. For organizations that seek to evaluate and improve their data management practices, CMMI® Institute has developed the Data Management Maturity (DMM)? model to bridge the perspective gap between business and IT. Download the white paper Why is Measurement of Data Management Maturity Important? to enable you to: - Empower your executives to make better and faster decisions using a strategic view of their data. - Achieve the elusive alignment and agreement between the business and IT - Create a clear path to increasing capabilities
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white paper, enterprise data management, data model, data modeling, data maturity model, cmmi institute
    
CMMI Institute
Published By: Access Sciences     Published Date: Sep 07, 2014
Few organizations have fully integrated the role of the Data Steward due to concerns about additional project complexity, time away from other responsibilities or insufficient value in return. The principles of the Agile methodology (whether or not Agile is followed for projects) can offer guidance in making the commitment to designating and empowering the Data Steward role. By placing insightful people in a position to connect innovators, respond to change and spur development aligned with business activities, organizations can expect to see a more efficient and effective use of their information assets.
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Access Sciences
Published By: iCEDQ     Published Date: Feb 05, 2015
The demand for using data as an asset has grown to a level where data-centric applications are now the norm in enterprises. Yet data-centric applications fall short of user expectations at a high rate. Part of this is due to inadequate quality assurance. This in turn arises from trying to develop data-centric projects using the old paradigm of the SDLC, which came into existence during an age of process automation. SDLC does not fit with data-centric projects and cannot address the QA needs of these projects. Instead, a new approach is needed where analysts develop business rules to test atomic items of data quality. These rules have to be run in an automated fashion in a business rules engine. Additionally, QA has to be carried past the point of application implementation and support the running of the production environment.
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data, data management, data warehousing, data quality, etl testing, malcolm chisholm
    
iCEDQ
Published By: Experian     Published Date: Mar 12, 2018
Data is quickly becoming the currency of the emerging digital economy. As digital transformation efforts proliferate and become commonplace, data will take center stage as a critical driver of these initiatives. Organizations that are able to mobilize their data assets to power critical business initiatives will see a distinct advantage in the years to come. In fact, a majority of C-level executives (87%) believe that data has greatly disrupted their organization’s operations over the last 12 months. As the reliance on data deepens, the need for trustworthy and reliable data assets will become increasingly important. This year’s global study highlights several important issues and opportunities throughout the data management and data quality spaces. By discussing the latest advancements and challenges within our industry, we hope to empower all organizations to better leverage their data and to thrive in the digital economy.
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Experian
Published By: Experian     Published Date: Mar 12, 2018
Data is quickly becoming the currency of the emerging digital economy. As digital transformation efforts proliferate and become commonplace, data will take center stage as a critical driver of these initiatives. Organizations that are able to mobilize their data assets to power critical business initiatives will see a distinct advantage in the years to come. In fact, a majority of C-level executives (87%) believe that data has greatly disrupted their organization’s operations over the last 12 months. As the reliance on data deepens, the need for trustworthy and reliable data assets will become increasingly important. This year’s global study highlights several important issues and opportunities throughout the data management and data quality spaces. By discussing the latest advancements and challenges within our industry, we hope to empower all organizations to better leverage their data and to thrive in the digital economy.
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Experian
Published By: Ontotext     Published Date: Dec 21, 2015
Learn how semantic technologies make any content intelligent and turn it into revenue for your publishing business There is a smarter, cost-effective way for publishers to create, maintain and reuse content assets with higher accuracy. It is called dynamic semantic publishing. Putting Semantic Technologies at Work for the Publishing Industry An efficient blend of semantic technologies, dynamic semantic publishing enables powerful experiences when it comes to publishers’ main stock of trade: processing and representing information.
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Ontotext
Published By: Semarchy     Published Date: Feb 12, 2018
This whitepaper covers a recently completed a ground-breaking industry wide survey of executives, architects, and business stakeholders from data-driven organizations by Enterprise Management Research in order to explore the growing role of the CDO, and to explore the various data management maturity levels of enterprise companies. This whitepaper explains how industry visionaries use data as an asset, and discusses the growing importance of data governance leadership. Additionally, it creates a data management maturity index to show how various companies match-up in their data management vision and capabilities. Finally, the whitepaper covers the top data-focused applications used and their average implementation timelines.
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Semarchy
Published By: IDERA     Published Date: Feb 06, 2017
Data modeling can provide tangible economic benefits, which are best shown by quantifying the traditional benefits of data modeling. In this whitepaper, Tom Haughey discusses how to calculate the return on investment (ROI) of data modeling by assessing the economic value of real data modeling benefits, such as improved requirements definition, reduced maintenance, accelerated development, improved data quality and reuse of existing data assets. Download this whitepaper to learn how to: - Describe the value proposition of data modeling - Assess the economic value of data modeling benefits - Learn three methods to calculate data modeling ROI
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IDERA
Published By: R2C     Published Date: Jan 05, 2018
Consistent sharing of data across organizational boundaries is often hampered by a lack of transparency, visibility, and trust in the agreements made between parties who seek to share data assets. How does an organization with cultural barriers to sharing data assets engender trust in the process? Leveraging blockchain technology that “oraclizes” data sharing agreements may provide an answer.
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R2C
Published By: BackOffice     Published Date: Apr 22, 2018
The success of a business is increasingly influenced by how effectively it utilizes data within strategic decision making & operations. But when a business views its critical data simply as a byproduct of business processes, and doesn’t value it as a business asset, it increases the risk of not being able to achieve its desired outcomes.
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BackOffice
Published By: Deloitte Process Robotics     Published Date: May 04, 2018
Deloitte Process Robotics (DPR) solutions use a lightweight approach to train ‘bots’ that automate repetitive tasks of medium complexity without changes to existing process or IT infrastructure. With ever-growing repositories of unanalyzed and underutilized data, organizations require highly flexible and scalable enterprise data management (EDM) processes, DPR makes it possible for organizations to adopt comprehensive yet affordable EDM processes in the Big Data Era, helping organizations transform data into strategic assets through the automation of capabilities.
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Deloitte Process Robotics
Published By: graphgrid     Published Date: Oct 19, 2018
Graph databases are about to catapult across the famous technology adoption chasm and land in start-ups, enterprises and government agencies across the globe. The adoption antibodies are subsiding as the power of natively connected data becomes fundamental to any organization looking for data-driven insights across operations, suppliers, and customers. Moore’s Law increases in storage capacity and processing power can no longer keep up with the pace of data expansion, yet how companies structure and analyze their data ultimately will impact their ability to compete. Unstructured, disconnected data is useless. Graph databases will rapidly jump from niche use cases to a transformative IT technology as they enable turning the data you collect into actionable insights. Data will become the single most differentiating asset for your organization.
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graphgrid
Published By: Experian     Published Date: Nov 03, 2015
It is critically important for businesses today to leverage data as a strategic asset, instead of allowing it to become an obstacle. As more and more organizations are trying to make smarter, data-driven decisions, they are discovering that their data management strategy may not be as mature as it needs to be. One major shift we are seeing is the implementation of a Chief Data Officer. Experian Data Quality recently conducted a research study of more than 250 Chief Information Officers (CIOs) and Chief Data Officers (CDOs) in the US about their data management practices, and the explosion of the CDO role. Key insights in the report include: - Tips for overcoming typical data challenges within your organization - The changing data management landscape and its affect on the CIO - The new and growing need for the CDO - And more!
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Experian
Published By: Adaptive     Published Date: Jan 24, 2013
Data Governance and Metadata Management are two closely associated Data Management Capabilities which organizations must address in order to truly turn their data into Information Assets.
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data, data management, data governance, metadata, adaptive, dataversity, white paper
    
Adaptive
Published By: MarkLogic     Published Date: Apr 01, 2013
Data virtualization solves the problem of consolidating critical data scattered across silos, providing a comprehensive, actionable view of data assets. Learn how MarkLogic presents a unified view of multi-structured data across organizational silos.
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data management
    
MarkLogic
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