data practices

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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: 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: Apr 24, 2013
Using ERwin Data Modeler & Microsoft SQL Azure to Move Data to the Cloud within the DaaS Lifecycle by Nuccio Piscopo Cloud computing is one of the major growth areas in the world of IT. This article provides an analysis of how to apply the DaaS (Database as a Service) lifecycle working with ERwin and the SQL Azure platform. It should help enterprises to obtain the benefits of DaaS and take advantage of its potential for improvement and transformation of data models in the Cloud. The use case introduced identifies key actions, requirements and practices that can support activities to help formulate a plan for successfully moving data to the Cloud.
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CA Technologies
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: CMMI Institute     Published Date: Mar 21, 2016
CMMI Institute®’s Data Management Maturity (DMM)SM framework enables organizations to improve data management practices across the full spectrum of their business. It is a unique, comprehensive reference model that provides organizations with a standard set of best practices to assess their capabilities, strengthen their data management program, and develop a custom roadmap for improvements that align with their business goals.
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CMMI Institute
Published By: Experian     Published Date: May 17, 2016
Every year, Experian Data Quality conducts a study to look at the global trends in data quality. This year, research findings reveal how data practitioners are leveraging and managing data to generate actionable insight, and how proper data management is becoming an organization-wide imperative. This study polled more than 1,400 people across eight countries globally from a variety of roles and departments. Respondents were chosen based on their visibility into their orgazation's customer data management practices. Read through our research report to learn: - The changes in channel usage over the last 12 months - Expected changes in big data and data management initiatives - Multi-industry benchmarks, comparisons, and challenges in data quality - And more! Our annual global benchmark report takes a close look at the data quality and data management initiatives driving today's businesses. See where you line up and where you can improve.
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Experian
Published By: Experian     Published Date: Mar 30, 2017
Businesses today recognize the importance of the data they hold, but a general lack of trust in the quality of their data prevents them from achieving strategic business objectives. Nearly half of organizations globally say that a lack of trust in their data contributes to increased risk of non-compliance and regulatory penalties (52%) and a downturn in customer loyalty (51%). To be of value to organizations, data needs to be trustworthy. In this report, you will read about the findings from this unique study, including: · How data powers business opportunities · Why trusted data is essential for performance · Challenges that affect data quality · The current state of data management practices · Upcoming data-related projects in 2017
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Experian
Published By: Alation     Published Date: Mar 15, 2016
curation (noun): The act of organizing and maintaining a collection (such as artworks, artifacts, or data). Data curation is emerging as a technique to support data governance, especially in data-driven organizations. As self-service data visualization tools have taken off, sharing the nuances and best practices of how to use data becomes ever more critical. Analysts at companies from eBay to Safeway and Square are scaling their data knowledge through curation techniques. What are the 4 steps to successful data curation? Find out here:
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data stewardship, self-service analytics, data curation, data governance
    
Alation
Published By: Alation     Published Date: Mar 26, 2018
When analysts and data scientists have access to data governance policies and best practices directly within the flow of their analysis, the result is both more consistent compliance and more broadly adopted best practices. With the combination of Tableau and Alation, organizations can not only balance the demands of agility and governance, they can actually optimize for both at the same time. We call this Governance for Insight. Learn more about how Tableau users can have the best of worlds - agility and governance. Read Enabling Governance for Insight: Trust in Data with Tableau and Alation’s Data Catalogs. Register on the right to access a complimentary copy of this joint white paper from Tableau and Alation.
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Alation
Published By: CloverETL     Published Date: Nov 24, 2017
The volume of data is increasing by 40% per year (Source: IDC). In addition, the structure and quality of data differs vastly with a growing number of data sources. More agile ways of working with data are required. This whitepaper discusses the vast options available for managing and storing data using data architectures, and offers use cases for each architecture. Furthermore, the whitepaper explores the benefits, drawbacks and challenges of each data architecture and commonly used practices for building these architectures.
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CloverETL
Published By: Converseon     Published Date: Apr 02, 2018
Separating signals from noisy social listening data has long been a problem for data scientists. Poor precision due to slag, sarcasm and implicit meaning has often made it too challenging to effectively model. Today, however, new approaches that leverage active machine learning are rapidly over taking aging rules-based techniques and opening up use of this data in new and important ways. This paper provides some detail on the evolution of text analysis including current best practices and how AI can be used by data scientists to use this data for meaningful analysis.
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Converseon
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: 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, information quality, data quality, metadata, enterprise information management, master data management, mdm, analytics, database
    
SAP
Published By: HotSchedules     Published Date: Jan 17, 2019
The cost of labor can be managed effectively when restaurateurs are equipped with the right training, tools and processes. Traditional methods for capturing critical forecasting data and formulating labor plans & budgets are incomplete and often inaccurate, rarely accounting for menu pricing changes or current economic conditions. Even when the best practices of forecasting are employed, the information isn’t always available to the store managers who are attempting to manage the cost of labor. This ebook will walk you through the ins and outs of labor forecasting and best practices to build efficiency, grow revenue, and positively impact your restaurant’s bottom line.
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HotSchedules
Published By: TIBCO Software APAC     Published Date: Feb 14, 2019
Tips and best practices for data analytics executives Organizations today understand the value to be derived from arguably their greatest asset—data. When successfully aggregated and analyzed, data can unlock valuable insights, solve problems, improve products and services, and help companies gain a competitive edge. However, analytics executives face significant challenges in collecting, validating and analyzing data to deliver the right analytic insight to the right person at the right time. This e-book is designed to help. First, we'll explore the growing expectations for data analytics and the rise of the analytics executive. Then we'll explore a range of specific challenges those executives face, including those around data blending, analytics, and the organization itself, and offer best practices and strategies for meeting them.
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TIBCO Software APAC
Published By: Google Apigee     Published Date: Mar 06, 2019
Inside the API Product Mindset APIs expose data or functionality for use by applications and developers, which means they allow access to a business’s valuable digital assets—and thus to the heart of the business itself. Vulnerable APIs can expose a business’s core data and services to a variety of threats. Therefore, API security should be of paramount importance to any enterprise that exposes digital assets. Google Cloud’s new eBook, Building and Managing Secure APIs, takes a deeper look at the do’s and don’ts of enterprise API security. Explore how to design secure, easy-to-use APIs with field-tested best practices, real-world use cases and a checklist to help your team get started. What's Inside? ? Field-tested best practices ? Real-world use cases ? API monetization checklist
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Google Apigee
Published By: APC by Schneider Electric     Published Date: Feb 09, 2011
In the broadening data center cost-saving and energy efficiency discussion, data center physical infrastructure preventive maintenance (PM) is sometimes neglected as an important tool for controlling TCO and downtime. PM is performed specifically to prevent faults from occurring. IT and facilities managers can improve systems uptime through a better understanding of PM best practices.
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absolute, data protection, maitenance, ups, physical infrastructure, infrastructure, data loss protection, dlp, ponemon, it security, sensitive data
    
APC by Schneider Electric
Published By: Cisco EMEA     Published Date: Nov 13, 2017
Big data and analytics is a rapidly expanding field of information technology. Big data incorporates technologies and practices designed to support the collection, storage, and management of a wide variety of data types that are produced at ever increasing rates. Analytics combine statistics, machine learning, and data preprocessing in order to extract valuable information and insights from big data.
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big data, analytics, virtualization, cloudera, ibm, sas, sap, splunk
    
Cisco EMEA
Published By: Coupa     Published Date: Dec 14, 2018
Many procurement departments are still using traditional manual processes or outdated technology. The result? Rogue spending, missed discounts from supplier contract pricing, reconciliation headaches, and the list goes on. These business risks are driving more organizations towards the cloud-based, secure, and workflow-friendly world of eProcurement solutions. These solutions are saving money and resources, improving use of budgets and personnel, enabling centralization, and using data to improve and streamline end-to-end purchasing processes. Download this report to learn about: Procurement trends from 400 organizations surveyed Operational and cost-savings benefits of eProcurement Leading features and functionality in eProcurement Adoption best practices and how to get started
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Coupa
Published By: Hewlett Packard Enterprise     Published Date: Oct 24, 2017
Big Data is not just a big buzzword. Government agencies have been collecting large amounts of data for some time and analyzing the data collected to one degree or another. Big data is a term that describes high volume, variety and velocity of information that inundates an organization on a regular basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and better services.
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cloud optimization, cloud efficiency, cloud management, cloud assurance, cloud visibility, enterprise management, data management
    
Hewlett Packard Enterprise
Published By: Broadsoft     Published Date: May 25, 2017
This whitepaper is for contact center management and business executives looking for ways to optimize the business performance of their contact center. • If yours is an existing contact center with infrastructure that has been built up over the years, this whitepaper provides bestpractice steps to break down the resulting data silos, unify them, and optimize your contact center for business performance. • If yours is a new contact center and you have the opportunity to build your infrastructure from the ground up using modern technologies, this whitepaper will provide best practices to prevent building data silos.
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Broadsoft
Published By: SAS     Published Date: Jan 17, 2018
This TDWI Best Practices Report focuses on how organizations can and are operationalizing analytics to derive business value. It provides in-depth survey analysis of current strategies and future trends for embedded analytics across both organizational and technical dimensions, including organizational culture, infrastructure, data and processes. It looks at challenges and how organizations are overcoming them, and offers recommendations and best practices for successfully operationalizing analytics in the organization.
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SAS
Published By: Pentaho     Published Date: Feb 26, 2015
This TDWI Best Practices report explains the benefits that Hadoop and Hadoop-based products can bring to organizations today, both for big data analytics and as complements to existing BI and data warehousing technologies.
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big data, big data analytics, data warehousing technologies, data storage, business intelligence, data integration, enterprise applications, data management
    
Pentaho
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