it governance

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Published By: DATAVERSITY     Published Date: Jul 06, 2015
The growth of NoSQL data storage solutions have revolutionized the way enterprises are dealing with their data. The older, relational platforms are still being utilized by most organizations, while the implementation of varying NoSQL platforms including Key-Value, Wide Column, Document, Graph, and Hybrid data stores are increasing at faster rates than ever seen before. Such implementations are causing enterprises to revise their Data Management procedures across-the-board from governance to analytics, metadata management to software development, data modeling to regulation and compliance. The time-honored techniques for data modeling are being rewritten, reworked, and modified in a multitude of different ways, often wholly dependent on the NoSQL platform under development. The research report analyzes a 2015 DATAVERSITY® survey titled “Modeling NoSQL.” The survey examined a number of crucial issues within the NoSQL world today, with focus on data modeling in particular.
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DATAVERSITY
Published By: IDERA     Published Date: Nov 07, 2017
Data modeling is all about data definition but has a much wider impact on the data of your organization. Quality data definition impacts how data is produced and directly impacts how the data is or will be used throughout an organization. That means that we must proactively govern the process of how we define data, to establish a common understanding across the team. In this whitepaper, Robert Seiner describes how data modeling is a form of data governance and provides insights on the three actions of governing data.
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IDERA
Published By: CA Technologies     Published Date: Oct 22, 2015
As the interest in managing information and enforcing corporate data management policies increases, data governance programs to manage data sets are becoming more and more vital to the business operation. However, in this rush for data governance programs, sometimes the true utility and importance of metadata can be missed. In this white paper, David Loshin of Knowledge Integrity, Inc. discusses the importance of data governance and the role of metadata management as a way to empower data governance and enforce data policies.
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white paper, metadata, data management, data modeling, david loshin, data governance, data governance strategy
    
CA Technologies
Published By: First San Francisco Partners     Published Date: Sep 03, 2014
This white paper discusses how awareness, ownership and accountability are key to activating and engaging people to adopt new data governance initiatives and the related policies and processes. It provides you with information on how your organization can ensure that the data stewards adopt the new data governance policies and processes.
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data, data management, data governance, data governance program
    
First San Francisco Partners
Published By: First San Francisco Partners     Published Date: Mar 13, 2015
A Data Governance Organization and its structure should be defined to align with your company’s organizational hierarchy and resources. Finding the right people to assign to data governance requires an understanding of both the functional and the political role of governance within your organization. This paper highlights some best practices in putting the right resources behind the required roles.
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data governance, data governance resources, data governance organization
    
First San Francisco Partners
Published By: First San Francisco Partners     Published Date: Jun 01, 2016
A Data Governance Organization and its structure should be defined to align with your company’s organizational hierarchy and resources. Finding the right people to assign to data governance requires an understanding of both the functional and the political role of governance within your organization. This paper highlights some best practices in putting the right resources behind the required roles.
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First San Francisco Partners
Published By: TopQuadrant     Published Date: Mar 21, 2015
Data management is becoming more and more central to the business model of enterprises. The time when data was looked at as little more than the byproduct of automation is long gone, and today we see enterprises vigorously engaged in trying to unlock maximum value from their data, even to the extent of directly monetizing it. Yet, many of these efforts are hampered by immature data governance and management practices stemming from a legacy that did not pay much attention to data. Part of this problem is a failure to understand that 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. It is essentially codes whose basic job is to turn other data into meaningful business information and to provide an informational context for the wider world in which the enterprise functions. This paper discusses the challenges associated with implementing a reference data management solution and the essential components of any vision for the governance and management of reference data. It covers the following topics in some detail: · What is reference data? · Why is reference data management important? · What are the challenges of reference data management? · What are some best practices for the governance and management of reference data? · What capabilities should you look for in a reference data solution?
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data management, data, reference data, reference data management, top quadrant, malcolm chisholm
    
TopQuadrant
Published By: CA Technologies     Published Date: Dec 03, 2015
This 2nd paper in a 3-part series by David Loshin explores some challenges in bootstrapping a data governance program, and then considers key methods for using metadata to establish the starting point for data governance. The paper will focus on how metadata management facilitates progress along three facets of the data governance program including assessment, collaboration and operationalization.
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CA Technologies
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: Cambridge Semantics     Published Date: Mar 13, 2015
As the quantity and diversity of relevant data grows within and outside the enterprise, how can IT easily deploy secure governed solutions that allow business users to identify, extract, link together and derive value from the right data at the right time, at big data scale, while keeping up with ever changing business needs? Smart Enterprise Data Management (Smart EDM) is new, sensible paradigm for managing enterprise data. Anzo Smart Data solutions allow IT departments and their business users to quickly and flexibly access all of their diverse data. Based upon graph data models and Semantic data standards, Anzo enables users to easily perform advanced data management and analytics through the lens of their business at a fraction of the time and cost of traditional approaches, while adhering to the governance and security required by enterprise IT groups. Download this whitepaper to learn more.
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enterprise data management, data governance, data integration, cambridge semantics
    
Cambridge Semantics
Published By: Cambridge Semantics     Published Date: Aug 17, 2015
As the quantity and diversity of relevant data grows within and outside of the enterprise, business users and IT are struggling to extract maximum value from this data. Current approaches, including the rigid relational data warehouse and the unwieldy Hadoop-only Data Lake, are limited in their ability to provide users and IT with the answers they need with the proper governance and security required. Read this whitepaper to learn how The Anzo Smart Data Lake from Cambridge Semantics solves these problems by disrupting the way IT and business alike manage and analyze data at enterprise scale with unprecedented flexibility, insight and speed.
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Cambridge Semantics
Published By: AnalytixDS     Published Date: Feb 28, 2015
With future business intelligence solutions clearly evolving from data that comes from highly efficient and well behaved systems, to data that comes from the extended enterprise where data is not necessarily so well structured and behaved - Organizations are forced into a more collaborative mode of operation with their core infrastructure being adapted from the consumer space, and to the extent possible, conformed to their existing repositories. This whitepaper attempts to address various challenges consumers face while managing enormous data sets within the context of this complex scenario. Further, we’ll try to answer the question: Is Big Data Governance really that different from traditional data governance initiatives? Finally, we’ll see how AnalytiX™ Mapping Manager™ can help organizations accelerate the development and deployment of a successful Big Data/ Business Intelligence platform and accelerate delivery of all sorts of data – structured, semi-structured as well as unstruc
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big data, big data governance, data governance, analytixds
    
AnalytixDS
Published By: Reltio     Published Date: Aug 11, 2017
"Forrester's research uncovered a market in which Reltio [and other companies] lead the pack,” the Forrester Wave Master Data Management, 2016 states. "Leaders demonstrated extensive and MDM capabilities for sophisticated master data scenarios, large complex ecosystems, and data governance to deliver enterprise-scale business value.”
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Reltio
Published By: ROKITT     Published Date: Apr 11, 2016
Few things benefit an organization as much as information governance. Data is now one of the most valuable holdings for any business, but unfortunately in many environments much of the data is ignored and its potential value lost. Ignored data is also inherently less secure than data that’s tracked. Businesses need a way to bring hidden data out of the shadows and make it safe and useful again. Data discovery facilitates unearthing previously unknown data relationships. Mapping data flow and data lineage helps make data safe, compliant, and auditable. Good metadata makes a system more navigable. All these tools make data more accessible to staff and more useful for capitalizing on business opportunities.
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ROKITT
Published By: Infogix     Published Date: Apr 21, 2017
Data Governance and GDPR go Together Like Peanut Butter and Jelly May 2018 might sound far away, but any organization that does business in the EU must be prepared on day one to comply with the new General Data Protection Regulation (GDPR). This regulation carries stiff penalties for non-compliance – first time violators should expect to pay up to the greater of 4% of global annual revenue or 20 million EUR in fines – so it behooves organizations to cross their i's and dot their t's in regards to their GDPR plan. One integral component that is vital is instituting data governance to understand the organization’s data from a business perspective. Learn more about "What is considered Personally Identifiable Information?”, “What are the GDPR compliance obligations?”, and “Why data governance is vital?” in an easy to read white paper titled: General Data Protection Regulation (GDPR) and the Vital Role of Data Governance.
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Infogix
Published By: TopQuadrant     Published Date: Jul 18, 2016
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 becoming too slow and limited.   Semantic information management makes it easier to reconcile data from different sources by compiling and organizing information about that data, its metadata. By connecting all kinds of data and metadata in a more accessible way, semantic information systems empower users, data stewards and analysts to unlock and use the true meaning and value of their organization’s data.     Learn more about the challenges in the evolving data landscape and how a semantic approach can help.
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TopQuadrant
Published By: TopQuadrant     Published Date: Aug 01, 2016
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 becoming too slow and limited. Semantic information management makes it easier to reconcile data from different sources by compiling and organizing information about that data, its metadata. By connecting all kinds of data and metadata in a more accessible way, semantic information systems empower users, data stewards and analysts to unlock and use the true meaning and value of their organization’s data. Learn more about the challenges in the evolving data landscape and how a semantic approach can help.
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TopQuadrant
Published By: SAS     Published Date: Oct 27, 2014
Done correctly, data governance can transform the way an organization manages – and capitalizes on – its data. However, because it spans a variety of people, policies and technologies, data governance is a daunting effort. The SAS Data Governance Framework is designed to provide the organizational and technological structures needed to overcome common data governance failure points.
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data, data management, data governance, sas, white paper
    
SAS
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: SAP     Published Date: Jan 23, 2013
This paper examines the root causes of data centralization failure and then reviews straightforward best practices that can help avoid such failures but are typically ignored when systems are designed in an ad hoc, organic manner.
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data, data management, data governance, big data, cloud, business intelligence, semantic technology, nosql
    
SAP
Published By: First San Francisco Partners     Published Date: May 19, 2014
Master data management (MDM) is about people and process; it is not about technology. Implementing MDM technology alone will not address operational and business process challenges. Rather, “mastering” data involves people taking action through established data policies and processes. Data Governance ensures that data in the MDM hub is of high quality and can be trusted by business users. Lacking Data Governance, organizations do not have consistent data definitions or know what constitutes a data problem, who is accountable, what decisions need to be made, or how to escalate and resolve issues.
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white paper, master data management, first san francisco partners, kelle o'neal, dataversity, data governance, mdm, master data management white paper
    
First San Francisco Partners
Published By: Embarcadero     Published Date: Feb 28, 2013
A recent Data Governance Benchmarking Survey reported that only 23% of organizations have a reasonable command of data compliance. A shockingly low number. We as data professionals cannot solve the problem alone.
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white paper, dataversity, data governance, karen lopez, embarcadero, data management
    
Embarcadero
Published By: Cambridge Semantics     Published Date: Apr 03, 2014
A new approach to rapid, high-quality data integration, data services, and data governance.
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white paper, data, data mangement, enterprise information management, cambridge semantics, semantic technology
    
Cambridge Semantics
Published By: Semarchy     Published Date: Aug 18, 2016
David Loshin reexamines the way we ingest, manage, consume, and transform data into actionable information and intelligence. Read how this industry expert makes the case for data governance with an unconventional business-first focus. The conventional wisdom on data governance proposes hierarchies, operating models, and processes for data policy definition and implementation. Unfortunately, poorly-designed and minimally-planned data governance processes are ineffective because they are bureaucratic and overwhelming. This is especially true when processes are imposed by fiat, take a long time, and don't result in any short-term improvement in information value. But proper data governance is a critical success factor for master data management! In this paper, we examine the motivations for coupling data governance with master data management and consider how to evolve data policies and processes to position master data management for success.
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Semarchy
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