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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: SAP     Published Date: May 19, 2016
SAP® solutions for enterprise information management (EIM) support the critical abilities to architect, integrate, improve, manage, associate, and archive all information. By effectively managing enterprise information, your organization can improve its business outcomes. You can better understand and retain customers, work better with suppliers, achieve compliance while controlling risk, and provide internal transparency to drive operational and strategic decisions.
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SAP
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: MemSQL     Published Date: Jun 25, 2014
Emerging business innovations focused on realizing quick business value on new and growing data sources require “hybrid transactional and analytical processing” (HTAP), the notion of performing analysis on data directly in an operational data store. While this is not a new idea, Gartner reports that the potential for HTAP has not been fully realized due to technology limitations and inertia in IT departments. MemSQL offers a unique combination of performance, flexibility, and ease of use that allows companies to implement HTAP to power their business applications.
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MemSQL
Published By: AT&T     Published Date: Sep 11, 2014
The age of Big Data is upon us. Storage costs are going down, and data analytics is becoming more capable and more user-friendly. Even your auto mechanic will be storing a petabyte of data soon. Big Data will give businesses new insights and help improve operations. With these new tools come questions about how to use them. But your mechanic knows more about fixing a transmission than developing a Hadoop cluster, and similar concerns hold true for larger enterprises. Businesses everywhere are looking for guidance.
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AT&T
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: 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: VoltDB     Published Date: Jul 09, 2015
What is fast data? It's data in motion, and it creates Big Data. But handling it requires a radically different approach. Download the Fast Data Stack white paper from VoltDB. Learn how to build fast data applications with an in-memory solution that’s powerful enough for real-time stateful operations.
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data, data management, data stack, bug data, voltdb, database, nosql
    
VoltDB
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: Alteryx     Published Date: May 24, 2017
Spreadsheets are a mainstay in almost every organization. They are a great way to calculate and manipulate numeric data to make decisions. Unfortunately, as organizations grow, so does the data, and relying on spreadsheet-based tools like Excel for heavy data preparation, blending and analysis can be cumbersome and unreliable. Alteryx, Inc. is a leader in self-service data analytics and provides analysts with the ability to easily prep, blend, and analyze all data using a repeatable workflow, then deploy and share analytics at scale for deeper insights in hours, not weeks. This paper highlights how transitioning from a spreadsheet-based environment to an Alteryx workflow approach can help analyst better understand their data, improve consistency, and operationalize analytics through a flexible deployment and consumption environment.
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Alteryx
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: 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: graphgrid     Published Date: Oct 02, 2018
Whether it’s for a specific application, optimizing your existing operations, or innovating new customer services, graph databases are a powerful technology that turn accessing and analyzing your data into a competitive advantage. Graph databases resolve the Big Data limitations and free up data architects and developers to build amazing solutions that predict behaviors, enable data driven decisions and make insightful recommendations. Yet just as cars aren’t functional with only engines, graph databases require surrounding capabilities including ingesting multi-source data, building data models that are unique to your business needs, ease of data interaction and visualization, seamless co-existence with legacy systems, high performance search capabilities, and integration of data analysis applications. Collectively, this comprehensive data platform turns graph capabilities into tangible insights that drive your business forward.
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graphgrid
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: graphgrid     Published Date: Oct 02, 2018
Whether it’s for a specific application, optimizing your existing operations, or innovating new customer services, graph databases are a powerful technology that turn accessing and analyzing your data into a competitive advantage. Graph databases resolve the Big Data limitations and free up data architects and developers to build amazing solutions that predict behaviors, enable data driven decisions and make insightful recommendations. Yet just as cars aren’t functional with only engines, graph databases require surrounding capabilities including ingesting multi-source data, building data models that are unique to your business needs, ease of data interaction and visualization, seamless co-existence with legacy systems, high performance search capabilities, and integration of data analysis applications. Collectively, this comprehensive data platform turns graph capabilities into tangible insights that drive your business forward.
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graphgrid
Published By: Basho     Published Date: Nov 25, 2015
The landscape of Scalable Operational and Analytical Systems is changing and disrupting the norm of using relational databases for all workloads. With the growing need to process and analyze Big Data at Scale, the demand for alternative strategies has grown and has given rise to the emergence of NoSQL databases for scalable processing. Mike Ferguson, Managing Director of Intelligent Business Strategies, is an independent IT Analyst who specializes in Big Data, BI/Analytics, Data Management and Enterprise Business Integration. In this whitepaper he will discuss the movement towards NoSQL databases for scalable operational and analytical systems, what’s driving Big Data analytics from Hadoop to the emergence of Apache Spark, the value of operational analytics and the importance of in-memory processing, and why use Apache Spark as your in-memory analytical platform for operational analytics.
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Basho
Published By: First San Francisco Partners     Published Date: Jan 23, 2013
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.
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white paper, data, data management, master data management, first san francisco partners, dataversity
    
First San Francisco Partners
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, data governance white paper
    
First San Francisco Partners
Published By: Data Blueprint     Published Date: Apr 02, 2014
Organizations maintain data-based assets in hopes of successfully employing them in support of strategy. In an attempt to provide valued products and/or services, a customer relationship management (CRM) strategy should attempt to improve what is known about the wants and needs of existing customers. An organization may desire to transfer its inventory to its suppliers and to only play the role of transaction broker. A third strategy might be to use data to obtain significant efficiencies from productions/operations, ensuring a low cost advantage.
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data, data management, data value, return on investment, white paper
    
Data Blueprint
Published By: Hewlett Packard Enterprise     Published Date: Jan 31, 2019
"Extracting value from data is central to the digital transformation required for businesses to succeed in the decades to come. Buried in data are insights that reveals what your customers need and how they want to receive it, how sales, manufacturing, distribution, and other aspects of business operations are functioning, what risks are arising to threaten the business, and more. That insight empowers your businesses to reach new customers, develop and deliver new products, to operate more efficiently and more effectively, and even to develop new business models. "
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Hewlett Packard Enterprise
Published By: Hewlett Packard Enterprise     Published Date: Jan 31, 2019
The bar for success is rising in higher education.  University leaders and IT administrators are aware of the compelling benefits of digital transformation overall—and artificial intelligence (AI) in particular. AI can amplify human capabilities by using machine learning, or deep learning, to convert the fast-growing and plentiful sources of data about all aspects of a university into actionable insights that drive better decisions. But when planning a transformational strategy, these leaders must prioritize operational continuity. It’s critical to protect the everyday activities of learning, research, and administration that rely on the IT infrastructure to consistently deliver data to its applications.
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Hewlett Packard Enterprise
Published By: Sage EMEA     Published Date: Jan 29, 2019
Transform your finance operations into a strategic, data-driven engine Data inundation and information overload have burdened practically every largescale enterprise today, providing great amounts of detail but often very little context on which executives can act. According to the Harvard Business Review,1 less than half of an organisation’s structured data is actively used in making decisions. The burden is felt profoundly among finance executives, who increasingly require fast and easy access to real-time data in order to make smart, timely, strategic decisions. In fact, 80% of analysts’ time is spent simply discovering and preparing data, and the average CFO receives information too late to make decisions 24% of the time.2
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Sage EMEA
Published By: Rackspace     Published Date: Feb 01, 2019
Whether you’re already a Google customer or simply getting started with the public cloud, Google Cloud Platform (GCP) is an aordable, reliable, innovative and intuitive cloud solution. Rackspace can help you accelerate innovation and cost savings by taking over the intensive dayto-day operations of GCP — letting you focus on achieving your core business objectives while optimizing the performance of your applications. Rackspace works with customers to identify the scope and criticality of their applications and determine the service level that best addresses their needs. To discover how, download this whitepaper today.
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Rackspace
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