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Published By: Syncsort     Published Date: Jul 17, 2018
In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does its work and how the data is used. Today, in a world of AI and machine learning, data has a new role – becoming essentially the source code for machine-driven insight. With AI and machine learning, the data is the core of what fuels the algorithm and drives results. Without a significant quantity of good quality data related to the problem, it’s impossible to create a useful model. Download this Whitepaper to learn why the process of identifying biases present in the data is an essential step towards debugging the data that underlies machine learning predictions and improves data quality.
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Syncsort
Published By: TD Bank Group     Published Date: Aug 10, 2018
This paper examines whether blockchain distributed ledger technology could improve the management of trusted information, specifically considering data quality. Improvement was determined by considering the impact of a distributed ledger as an authoritative source in TD Bank Group's Enterprise Data Quality Management Process versus the use of standard authoritative sources such as databases and files. Distributed ledger technology is not expected, or proven, to result in a change in the Data Quality Management process. Our analysis focused on execution advantages possible due to distributed ledger properties that make it an attractive resource for data quality management (DQM).
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TD Bank Group
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: First San Francisco Partners     Published Date: Oct 29, 2015
One of the biggest challenges in a data management initiative is aligning different and sometimes competing organizations to work towards the same long-term vision. That is why a proactive approach to aligning the organization around a common goal and plan is critical when launching a data management program.
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First San Francisco Partners
Published By: Melissa Data     Published Date: Mar 23, 2017
In this eBook published by Melissa, author David Loshin explores the challenges of determining when data values are or are not valid and correct, how these values can be corrected, and how data cleansing services can be integrated throughout the enterprise. This Data Quality Primer eBook gives an overview of the five key aspects of data quality management (data cleansing, address data quality, address standardization, data enhancement, and record linkage/matching), as well as provides practical aspects to introduce proactive data quality management into your organization.
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Melissa Data
Published By: Melissa Data     Published Date: Jan 18, 2018
Maintaining high quality data is essential for operational efficiency, meaningful analytics and good long-term customer relationships. But, when dealing with multiple sources of data, data quality becomes complex, so you need to know when you should build a custom data quality tools over canned solutions. To answer this question, it is important to understand the difference between rules-based data quality, where internal subject matter expertise is necessary – and active data quality, where different domain expertise and resources are required.
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Melissa Data
Published By: Ted Hills     Published Date: Jul 02, 2015
Entity-relationship (E-R) modeling is a tried and true notation for use in designing Structured Query Language (SQL) databases, but the new data structures that Not-Only SQL (NOSQL) DBMSs make possible can’t be represented in E-R notation. Furthermore, E-R notation has some limitations even for SQL database design. This article shows how a new notation, the Conceptual and Objective Modeling (COM) notation, is able to represent NOSQL designs that are beyond the reach of E-R notation. At the end, it gives a peek into the tutorial workshop to be given at the 2015 NOSQL Conference in San Jose, CA, US, in August, which will provide opportunities to apply COM notation to practical problems.
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nosql, sql, data modeling, data model, er modeling, entity relationship, database, relational, dbms, schema-less, xml, conceptual, logical, physical
    
Ted Hills
Published By: Trillium Software     Published Date: Apr 10, 2017
For the 11th consecutive year, the Gartner Magic Quadrant for Data Quality Tools1 research report positions Trillium Software as a leader in the Data Quality Software industry. Data Quality is vital to ensuring trust in your data-driven, decision making business processes. Confidence is the result of a well thought out and executed data quality management strategy and is critical to remaining competitive in a rapidly and ever-changing business world. The 2016 Gartner Magic Quadrant for Data Quality Tools report is a valuable reference, providing the latest insights into the strengths and cautions of leading vendors. Access the report to learn how a leading data quality solution can help you achieve your long-term strategic objectives.
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Trillium Software
Published By: FairCom     Published Date: May 25, 2016
As companies embrace NoSQL as the “next big thing,” they are rightly cautious of abandoning their investment in SQL. The question a responsible developer or IT manager must investigate is “in which cases are each of these technologies, SQL and NoSQL, the appropriate solution?” For example, cloud provider BigStep offered this assessment: “NoSQL is not the best model for OLTP, ad hoc queries, complicated relationships among the data, and situations when stability and reliability outweigh the importance of speed.” While that statement may be true of many NoSQL databases, c-treeACE is the exception. Its unique, No+SQL architecture offers the advantages of SQL on top of a robust, high-performance NoSQL core engine. In this white paper, you'll read five ways c-treeACE breaks the NoSQL mold in terms of: • Data Integrity • Availability and Reliability • Complex Data Relationships • Flexible Queries • Performance
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FairCom
Published By: Infogix     Published Date: May 04, 2018
Over the last few years, the term “data governance” has evolved to more prominence, concurrently with big data. While organizations understand the need for governance around big data, the implementation of a successful data governance solution continues to remain elusive as organizations grapple with what exactly data governance is. This whitepaper provides a concise definition of data governance and offers some key considerations for a successful data governance solution.
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Infogix
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: Syncsort     Published Date: Jan 04, 2018
The term Big Data doesn’t seem quite “big enough” anymore to properly describe the vast over-abundance of data available to organizations today. As the volume and variety of Big Data sources continue to grow, the level of trust in that data remains troublingly low. Read on and discover how a strong focus on data quality spanning the people, processes and technology of your organization will help keep your data lake pristine.
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Syncsort
Published By: Datawatch     Published Date: Apr 06, 2018
Enterprises are focusing on becoming ever more data-driven, meaning that it is simply unacceptable to allow data to go to waste. Yet, as the amount of data businesses collect and control continues to increase exponentially, many organizations are failing to derive enough business value from their data. Companies are feeling the pressure to extract maximum value from all of their data, both defensive and offensive. Defensive analytics are the “plumbing aspects” of data management that must be captured to mitigate risk and establish a basic understanding of business performance. Offensive analytics build on defensive analytics and support overarching business objectives, strategic initiatives and long-term goals using predictive models. In this whitepaper, you will learn how to address many challenges, including streamlining operational reporting, delivering insight and providing a single, unified platform for everyone.
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Datawatch
Published By: Zoomdata     Published Date: Apr 11, 2018
How do you imagine data? If you’re thinking about it in terms of uniform records and databases, it’s time to make a brain update. Back in the day, analytical engines were limited, so our perception of what could be considered data was, too. Today, the big data renaissance has begun, and actually, more of the data exists outside of databases than inside them, plus, EVERYTHING is data. We’re going to help you discover how business intelligence and data sources of today have changed, and as a result, so has our approach to analyzing data. An eBook on this very topic is waiting for you—all it takes is the click of a button.
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Zoomdata
Published By: birst     Published Date: Jan 21, 2013
This Dive Deep analyst report looks at the process of building an environment for what can be aptly termed Agile Business Analytics.
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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
    
birst
Published By: First San Francisco Partners     Published Date: Nov 20, 2013
One of the biggest challenges in a data management initiative is aligning different and sometimes competing organizations to work towards the same long-term vision. It is very difficult to execute a data management program all at once, or as a “big bang” approach. Rather, the program should be deployed in phases over time, starting in one area and incrementally building out and adding value to the rest of the organization over time.
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data, data management, enterprise information management, enterprise data management, white paper
    
First San Francisco Partners
Published By: MapR Technologies     Published Date: Jul 26, 2013
Enterprises are faced with new requirements for data. We now have big data that is different from the structured, cleansed corporate data repositories of the past.Before, we had to plan out structured queries. In the Hadoop world, we don’t have to sort data according to a predetermined schema when we collect it. We can store data as it arrives and decide what to do with it later. Today, there are different ways to analyze data collected in Hadoop—but which one is the best way forward?
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white paper, hadoop, nosql, mapr, mapr technologies
    
MapR Technologies
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
Published By: DATAVERSITY     Published Date: Jun 17, 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.
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research paper, data, data management, data governance, data steward
    
DATAVERSITY
Published By: SAP     Published Date: Jul 23, 2018
Leading companies are making digital transformation a reality put data and intelligence at the center of their future. They are building new capabilities, skills, and technology, and evolving their culture to transform into an ‘Intelligent Enterprise’ and achieve the aforementioned outcomes. These companies are not only delivering short-term value to shareholders, but are also positioned to thrive and transform their industry. Explore how SAP can help you navigate the journey to the Intelligent Enterprise.
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SAP
Published By: Hewlett Packard Enterprise     Published Date: Jul 18, 2018
Case study briefs for Transguard, BiLFINGER, Aldermore PLC, PinkRoccade, Okinawa Cross Head, Siemens, Sogeti, Norrbottens, Dansk Supermarked Group, ITS Nordic.
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Hewlett Packard Enterprise
Published By: Zendesk     Published Date: Jun 29, 2018
Customers are more technically savvy than ever and have come to prefer the DIY approach to solving their issues and answering their own questions. Years of research by ICMI has confirmed that customers prefer to resolve issues themselves and within their channels of choice. Furthermore, customers only seek direct interactions when they unsuccessfully exhaust their self-service options. This is backed up by data from American Express, which found that 48% of consumers prefer to speak with a customer service rep when dealing with complex issues, but only 16% prefer the same contact for simple issues. The goal of this paper is simple: We want to help you build an all-in-one knowledge base, community, and customer portal. All of which can be accomplished with a help center like Zendesk Guide.
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Zendesk
Published By: Zendesk     Published Date: Jun 29, 2018
Your door is always open, regardless of your business or support hours. Today’s customers ask questions at any time of the day or night and will wait for you to respond (but not for long). This poses a challenge to most businesses: How can you anticipate which channels a customer will need or want, and how can you determine whether these channels will be efficient for your organization? The short answer is: you do. Customers want the ability to contact businesses in more than one way, and they expect each brand interaction to be consistent with the one before and after. Learn how with Zendesk's Omnichannel solution.
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Zendesk
Published By: Akamai Technologies     Published Date: Jun 14, 2018
"Existing security controls are outmatched — at best static and reactive. Current layers likely aren’t protecting you against all attack vectors, like the vulnerable back door that is recursive DNS. And security mechanisms that frustrate, impede, or disallow legitimate users, devices, or applications will have low adoption rates and/or will curtail productivity. Benign users may even circumvent these processes, further undermining your corporate security posture and creating more gaps in your defense-in- depth strategy. One of the many use cases associated with a zero trust security strategy is protecting your network — and most importantly, your data — from malware. "
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dns, rdns, security, zero trust security, malware, data, network security
    
Akamai Technologies
Published By: Amazon Web Services     Published Date: Jul 25, 2018
IDC’s research has shown the movement of most IT workloads to the cloud in the coming years. Yet, with all the talk about enterprises moving to the cloud, some of them still wonder if such a move is really cost effective and what business benefits may result. While the answers to such questions vary from workload to workload, one area attracting particular attention is that of the data warehouse. Many enterprises have substantial investments in data warehousing, with an ongoing cost to managing that resource in terms of software licensing, maintenance fees, operational costs, and hardware. Can it make sense to move to a cloud-based alternative? What are the costs and benefits? How soon can such a move pay itself off? Download now to find out more.
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Amazon Web Services
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