Published By: Aberdeen
Published Date: Jun 17, 2011
Download this paper to learn the top strategies leading executives are using to take full advantage of the insight they receive from their business intelligence (BI) systems - and turn that insight into a competitive weapon.
Published By: Lookout
Published Date: Apr 18, 2018
The world has changed. Yesterday everyone had a managed PC for work and all enterprise data was behind a firewall. Today, mobile devices are the control panel for our personal and professional lives. This change has contributed to the single largest technology-driven lifestyle change of the last 10 years.
As productivity tools, mobile devices now access significantly more data than in years past. This has made mobile the new frontier for a wide spectrum of risk that includes cyber attacks, a range of malware families, non-compliant apps that leak data, and vulnerabilities in device operating systems or apps. A secure digital business ecosystem demands technologies that enable organizations to continuously monitor for threats and provide enterprise-wide visibility into threat intelligence.
Watch the webinar to learn more about:
What makes up the full spectrum of mobile risks
Lookout's Mobile Risk Matrix covering the key components of risk
How to evolve beyond mobile device management
The financial services industry has unique challenges that often prevent it from achieving its strategic goals. The keys to solving these issues are hidden in machine data—the largest category of big data—which is both untapped and full of potential.
Download this white paper to learn:
*How organizations can answer critical questions that have been impeding business success
*How the financial services industry can make great strides in security, compliance and IT
*Common machine data sources in financial services firms
This spotlight report examines:
• How Manufacturing Operations Management (MOM) or Manufacturing Execution Systems (MES) are key enablers of data management and Digital Transformation. Companies can combine many other opportunities with manufacturing operations in a digital journey.
• Product lifecycle management (PLM) as a high-value discipline to pair with MOM in discrete manufacturing, and the value of digital continuity across engineering, manufacturing operations, and supply chain.
• A robust integration of MOM and PLM technologies and the advent of the Digital Twin (a virtual copy of the product and how it's made) to demonstrate maturity in Smart Manufacturing and the ability to make smart products in smart factories.
The IIoT has opened up a world of opportunity for manufacturers. Take advantage of it.
We are offering this second edition resource as a business oriented, working guide to core data management practices. In this ebook you will find easy to digest resources on the value and importance of data preparation, data governance, data integration, data quality, data federation, streaming data, and master data management.
Don’t settle. Forward-thinking finance professionals know that there is a better way. Though BI tools have provided visibility to data, they have in fact failed to provide direct access to enterprise information. However, evolution does not stop there: Next-gen corporate performance management solutions are among us and promise to deliver interconnected, flexible, and realtime data access. Agility achieved.
Published By: Panduit
Published Date: Aug 28, 2018
Interested in learning how the right physical and network infrastructure approach in your colocation data center facility can help you stabilize costs, provide better security, and help promote growth for you and your tenants? Download the Panduit white paper Optimizing Colocation Infrastructure Strategies to learn how to overcome the challenges of aging colocation data center infrastructure and onboard new tenants quickly.
Published By: Panduit
Published Date: Oct 09, 2018
Interested in learning how to stabilize costs and promote growth for you and your tenants? Download the Panduit white paper Colocation Provider Strategies for Success to learn how you can enable ongoing monitoring and maximize your colo data center’s efficiency.
Published By: StreamSets
Published Date: Sep 24, 2018
The advent of Apache Hadoop™ has led many organizations to replatform their existing architectures to reduce data management costs and find new ways to unlock the value of their data. One area that benefits from replatforming is the data warehouse. According to research firm Gartner, “starting in 2018, data warehouse managers will benefit from hybrid architectures that eliminate data silos by blending current best practices with ‘big data’ and other emerging technology types.” There’s undoubtedly a lot to ain by modernizing data warehouse architectures to leverage new technologies, however the replatforming process itself can be harder than it would at first appear. Hadoop projects are often taking longer than they need to create the promised benefits, and often times problems can be avoided if you know what to avoid from the onset.
Published By: Datastax
Published Date: Aug 03, 2018
"Part of the “new normal” where data and cloud applications are concerned is the ability to handle multiple types of data models that exist in the application and persist each in a single datastore. This data management capability is called a “multi-model” database.
Download this free white paper and explore the multi-model concept, its rationale, and how DataStax Enterprise (DSE) is the only database that can help accelerate building and powering distributed, responsive and intelligent cloud applications across multiple data models"
Veritas' NetBackup software has long been a favorite for data protection in the enterprise, and is now fully integrated with the market-leading all-flash data storage platform: Pure Storage. NetBackup leverages the FlashArray API for fast and simple snapshot management, and protection copies can be stored on FlashBlade for rapid restores and consolidation of file and object storage tiers. This webinar features architecture overviews as well as 2 live demo's on the aforementioned integration points.
Improved business productivity often requires more efficient IT and more efficient IT cannot be achieved without a better understanding of the way business services are run and delivered. Configuration Management Databases (CMDBs) have emerged as a central component for Information Technology Infrastructure Library (ITIL) and business service management (BSM).
Published By: Extensis
Published Date: Jun 08, 2010
Metadata Management is the process of ensuring that all metadata associated with a digital asset is captured, organized, stored and made available for use by and within other applications. Metadata Management begins at the moment the digital asset is created by an application or captured by digital imaging.
You may know some data management basics, but are you aware of the transformational results that can result from doing data management right? This paper explains core data management capabilities, then describes how a solid data management foundation can help you get more out of your data.
Fraudsters are only becoming smarter. How is your organization keeping pace and staying ahead of fraud schemes and regulatory mandates to monitor for them? In this e-book, learn the basics in how to prevent fraud, achieve compliance and preserve security.
Despite heavy, long-term investments in data management, data problems at many organizations continue to grow. One reason is that data has traditionally been perceived as just one aspect of a technology project; it has not been treated as a corporate asset. Consequently, the belief was that traditional application and database planning efforts were sufficient to address ongoing data issues.
As our corporate data stores have grown in both size and subject area diversity, it has become clear that a strategy to address data is necessary. Yet some still struggle with the idea that corporate data needs a comprehensive strategy.
There’s no shortage of blue-sky thinking when it comes to organizations’ strategic plans and road maps. To many, such efforts are just a novelty. Indeed, organizations’ strategic plans often generate very few tangible results for organizations – only lots of meetings and documentation. A successful plan, on the other hand, will identify realistic goals along with a r
Data integration (DI) may be an old technology, but it is far from extinct. Today, rather than being done on a batch basis with internal data, DI has evolved to a point where it needs to be implicit in everyday business operations. Big data – of many types, and from vast sources like the Internet of Things – joins with the rapid growth of emerging technologies to extend beyond the reach of traditional data management software. To stay relevant, data integration needs to work with both indigenous and exogenous sources while operating at different latencies, from real time to streaming. This paper examines how data integration has gotten to this point, how it’s continuing to evolve and how SAS can help organizations keep their approach to DI current.
Machine learning systems don’t just extract insights from the data they are fed, as traditional analytics do. They actually change the underlying algorithm based on what they learn from the data. So the “garbage in, garbage out” truism that applies to all analytic pursuits is truer than ever.
Few companies are already using AI, but 72 percent of business leaders responding to a PWC survey say it will be fundamental in the future. Now is the time for executives, particularly the chief data officer, to decide on data management strategy, technology and best practices that will be essential for continued success.
You may know some basics about data management, but do you realize the transformational results data-management-done-right can produce? This paper explains core data management capabilities, then describes how a solid data management foundation can help you get more out of your data. From getting fast, easy access to trustworthy data to making better decisions and becoming a data-driven business, you’ll learn why good data management is essential to success. Multiple real-world examples illustrate how SAS customers have used data management to improve customer experience, boost revenue, remain compliant and become more efficient.
“Unpolluted” data is core to a successful business – particularly one that relies on analytics to survive. But preparing data for analytics is full of challenges. By some reports, most data scientists spend 50 to 80 percent of their model development time on data preparation tasks. SAS adheres to five data management best practices that help you access, cleanse, transform and shape your raw data for any analytic purpose. With a trusted data quality foundation and analytics-ready data, you can gain deeper insights, embed that knowledge into models, share new discoveries and automate decision-making processes to build a data-driven business.
With the amount of information in the digital universe doubling every two years, big data governance issues will continue to inflate. This backdrop calls for organizations to ramp up efforts to establish a broad data governance program that formulates, monitors and enforces policies related to big data. Find out how a comprehensive platform from SAS supports multiple facets of big data governance, management and analytics in this white paper by Sunil Soares of Information Asset.
Risks have intensified as retailers and financial organizations embrace new technologies to meet customer demands for convenience. The rise of mobile and online transactions introduces new risks – and with that, new requirements for fraud mitigation. This paper discusses key steps for fighting back against fraud risk by establishing appropriate and accurate data, analytics and alert management.
Published By: Zynapse
Published Date: Jun 16, 2010
Data Governance has emerged as the point of convergence for people, technology and process in order to manage the crucial data (information) of an enterprise. This is a vital link in the overall ongoing data management process for it maintains the quality of data and makes it available to a wide range of decision making hierarchy across an organization