data quality

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By: Adobe     Published Date: Apr 03, 2015
A lack of executive support and poor data quality are just some reasons why analytics programs fail. The guide by Adam Greco, Reenergize Your Web Analytics, identifies the key reasons for program failures and provides ten ways to make your analytics program successful. Read the guide to discover key ways to improve your analytics program, including: • How to deal with your stakeholders • How to set your analytics priorities • How to reap the rewards of change
Tags : analytics program, stakeholders, adobe, marketing, personalization
     Adobe
By: IBM     Published Date: May 28, 2014
Read the whitepaper to find out how one client improved business value of their data by implementing InfoSphere Optim processes and technologies.
Tags : ibm, data lifecycle management, infosphere optim, integrating big data, governing big data, integration, best practices, big data, ibm infosphere, it agility, performance requirements, hadoop, scalability, data integration, big data projects, high-quality data, leverage data replication, data persistence, virtualize data, lifecycle management
     IBM
By: IBM     Published Date: May 28, 2014
Different types of data have different data retention requirements. In establishing information governance and database archiving policies, take a holistic approach by understanding where the data exists, classifying the data, and archiving the data. IBM InfoSphere Optim™ Archive solution can help enterprises manage and support data retention policies by archiving historical data and storing that data in its original business context, all while controlling growing data volumes and improving application performance. This approach helps support long-term data retention by archiving data in a way that allows it to be accessed independently of the original application.
Tags : ibm, data retention, information governance, archiving, historical data, integrating big data, governing big data, integration, best practices, big data, ibm infosphere, it agility, performance requirements, hadoop, scalability, data integration, big data projects, high-quality data, leverage data replication, data persistence
     IBM
By: IBM     Published Date: Jul 22, 2016
"Increasingly, brands are looking to differentiate based on an exceptional customer experience. The key to improving the customer experience is being able to effectively measure what’s working and what you need to improve. IBM host a webinar presenting tips on how to measure the customer experience for your brand and how to use that data to build better journeys. Please join IBM and guest speaker Andrew Hogan from Forrester Research as we share tips on how to best measure the digital experiences customers have with your brand and how to use that information to build better journeys. The webinar will provide attendees with: • Best practices to measure the quality of digital customer experiences • Guidance on the kinds of tools to use to capture the right CX metrics • Tips for integrating metrics, including the role of customer journeys • Techniques to drive action and improve digital experiences"
Tags : ibm, commerce, customer analytics, marketing, customer experience, customer insight, forrester, digital experience, knowledge management, enterprise applications
     IBM
By: IBM     Published Date: Feb 24, 2015
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
Tags : big data, ibm, big data outcomes, information governance, big data analytics, it management, data management, data center
     IBM
By: IBM     Published Date: Apr 06, 2016
As big data environments ingest more data, organizations will face significant risks and threats to the repositories containing this data. Failure to balance data security and quality reduces confidence in decision making. Read this e-Book for tips on securing big data environments
Tags : ibm, big data, data security, risk management, security
     IBM
By: IBM     Published Date: Apr 18, 2016
"Built using the IBM® InfoSphere® Information Server, IBM BigInsights® BigIntegrate and BigInsights BigQuality provide the end-to-end information integration and governance capabilities that organizations need."
Tags : ibm, big data, ibm infosphere, ibm biginsights, ibm bigintegrate, ibm bigquality, data management, data quality, data integration
     IBM
By: IBM     Published Date: Jul 15, 2016
As big data environments ingest more data, organizations will face significant risks and threats to the repositories containing this data. Failure to balance data security and quality reduces confidence in decision making. Read this e-Book for tips on securing big data environments.
Tags : ibm, data, security, big data, data management
     IBM
By: IBM     Published Date: Oct 18, 2016
Big data analytics offer organizations an unprecedented opportunity to derive new business insights and drive smarter decisions. The outcome of any big data analytics project, however, is only as good as the quality of the data being used. Although organizations may have their structured data under fairly good control, this is often not the case with the unstructured content that accounts for the vast majority of enterprise information. Good information governance is essential to the success of big data analytics projects. Good information governance also pays big dividends by reducing the costs and risks associated with the management of unstructured information. This paper explores the link between good information governance and the outcomes of big data analytics projects and takes a look at IBM's StoredIQ solution.
Tags : ibm, idc, big data, data, analytics, information governance, knowledge management, enterprise applications, data management, data center
     IBM
By: IBM     Published Date: Apr 14, 2017
Cloud-based data presents a wealth of potential information for organizations seeking to build and maintain a competitive advantage in their industry. However, most organizations will be confronted with the challenging task of reconciling their legacy on-premises data with new, third-party cloud-based data. It is within these “hybrid” environments that people will look for insights to make critical decisions.
Tags : cloud-based data, data quality, data management, hybrid environment, decision making
     IBM
By: IBM     Published Date: Oct 03, 2017
Many new regulations are spurring banks to rethink how data from across the enterprise flows into the aggregated risk and capital reports required by regulatory agencies. Data must be complete, correct and consistent to maintain confidence in risk reports, capital reports and analytical analyses. At the same time, banks need ways to monetize, grant access to and generate insight from data. To keep pace with regulatory changes, many banks will need to reapportion their budgets to support the development of new systems and processes. Regulators continually indicate that the banks must be able to provide, secure and deliver high-quality information that is consistent and mature.
Tags : data aggregation, risk reporting, bank regulation, enterprise, reapportion budgets
     IBM
By: Mentor Graphics     Published Date: Apr 03, 2009
A powerful signal integrity analysis tool must be flexibility, easy to use and integrated into an existing EDA framework and design flow. In addition, it is important for the tool to be accurate enough. This report reviews a validation study for the Mentor Graphics HyperLynx 8.0 PI tool to establish confidence in using it for power integrity analysis.
Tags : mentor graphics, pdn simulation, eda framework, mentor hyperlynx 8.0 pi, integrity analysis, virtual prototypes, esr, capacitor, power distribution network, vrm, voltage regulator module, signal, smas, analog models, backward crosstalk, capacitive crosstalk, controlling crosstalk, correct emc problems, correct emi problems, cross talk
     Mentor Graphics
By: Mentor Graphics     Published Date: Apr 03, 2009
For advanced signaling over high-loss channels, designs today are using equalization and several new measurement methods to evaluate the performance of the link. Both simulation and measurement tools support equalization and the new measurement methods, but correlation of results throughout the design flow is unclear. In this paper a high performance equalizing serial data link is measured and the performance is compared to that predicted by simulation. Then, the differences between simulation and measurements are discussed as well as methods to correlate the two.
Tags : mentor graphics, equalized serial data links, design flow, high loss channels, tektronix, pcb, bit error rate, ber, ieee, serdes, simulation, system configuration, mentor graphics hyperlynx, simplified symmetric trapezoidal input, duty cycle distortion, ber contours, electronics, analog models, backward crosstalk, capacitive crosstalk
     Mentor Graphics
By: Unitrends     Published Date: Jun 29, 2010
Get past the hype and focus on simply, reliably, and cost-effectively protecting your systems and data.
Tags : unitrends, backup, data protection, data quality, server, replication
     Unitrends
By: Unitrends     Published Date: Jun 15, 2010
In this document we're first going to explore the use of the insurance metaphor in terms of its most fundamental element: the broad consequences of data loss. We'll also discuss industry and regulatory consequences of data loss.
Tags : unitrends, backup, data protection, data quality, server, replication, data loss, sox
     Unitrends
By: Unitrends     Published Date: Apr 12, 2010
The purpose of deduplication is to provide more storage, particularly backup storage, for less money, right? Then wouldn't it be ridiculous if deduplication vendors were demanding that their customers pay more per terabyte of storage? Or if they were simply pushing the task of integrating, monitoring, and managing deduplication back onto their users?
Tags : unitrends, backup, data protection, data quality, server, replication, lossless, lossy, data compression
     Unitrends
By: Unitrends     Published Date: Dec 07, 2009
Backup, despite the breathless hyperbole of many in the industry, is simply a form of insurance. What's important about your backup strategy is getting your systems and data protected in the most effective and affordable manner possible. Speaking plainly - you must be careful that you are in control of your backup strategy rather than your backup strategy (and vendor) being in control of you.
Tags : unitrends, backup, data protection, data quality, server, replication, strategy, virtualization, recovery
     Unitrends
By: Unitrends     Published Date: Mar 30, 2010
Clouds are all the rage right now. And the truth is that cloud-based computing can be a tremendous value. Cloud-based computing is not just an opportunity for a bunch of venture capitalists hanging out at Sand Hill Road, it's a wonderful chance for small and medium businesses to take a step toward aligning their information technology spend to increase their focus on revenue-generating opportunities.
Tags : unitrends, backup, data protection, data quality, server, replication, strategy, virtualization, cloud, business continuity, cloud-based, revenue, recovery
     Unitrends
By: Unitrends     Published Date: May 18, 2010
This tongue in cheek white paper explores data loss from a contrarian point of view - exploring the top 7 shortcuts you can take to ensure that you lose your data. And since a fundamental responsibility of any information technology professional, as well as any C-level executive, is to ensure that the data upon which any company is created is protected - scrupulously following these shortcuts should also ensure that you lose not only your data but your job as well.
Tags : unitrends, backup, data protection, data quality, server, replication, data loss, sox
     Unitrends
By: SAS     Published Date: Aug 28, 2018
“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.
Tags : 
     SAS
By: SAS     Published Date: Mar 20, 2019
In today’s crowded analytics marketplace, who can you trust? What’s needed to deliver on the promise of transforming data into real value? And what do CIOs need to cost-effectively and successfully lead their organizations through changing technologies? For an organization to experiment with (and ultimately deploy) analytics, the responsibility falls squarely on the shoulders of IT. IT must provide secure access to lots of high-quality data, a friendly environment for experimentation and discovery, and a method for rapidly deploying and governing models. SAS can support an organization's journey toward becoming a data- and analytics-driven organization. We can help unlock the value by enabling with choices that make sense. Plus, we can show organizations how to get the most out of technology investments.
Tags : 
     SAS
By: StrikeIron     Published Date: Oct 03, 2013
Learn how to increase your landing page conversion rate by 10-30% using data validation, which will help you to get the most out of your landing page optimization.
Tags : strikeiron, data quality, data validation, data, daas, data-as-a-service, landing page optimizations, cloud-based, cloud, landing page conversion rates, software development, it management, telecom
     StrikeIron
By: IBM     Published Date: Apr 17, 2013
When it comes to storing data, the concepts of cost-effectiveness and efficiency are often at odds with each other. What we all seek is that elusive deal where we don’t affect the quality of service, yet are able to store a lot more (without spending a lot more). Read this white paper to learn how IBM Real-time Compression provides the data center with the flexibility to increase capacity and maintain performance for all kinds of data, including your most active data.
Tags : storwize, v7000, real-time, compression, features, data, cost-effective, efficiency, quality, service
     IBM
By: Oracle     Published Date: Nov 21, 2013
If you've neglected the advantages of marketing automation software because you think implementation will be complex, read this.
Tags : zenithoptimedia, oracle, eloqua, software implementation, marketing automation, aligning sales and marketing processes, system integration, crm system, lead management, communication issues, automation software, data quality, intelligent integration, employee training
     Oracle
By: Oracle     Published Date: Nov 21, 2013
Modern enterprise businesses need the right IT tools to engage customers online and keep them engaged until they buy. In short, they need marketing automation software.
Tags : zenithoptimedia, oracle, eloqua, software implementation, marketing automation, aligning sales and marketing processes, system integration, crm system, lead management, communication issues, automation software, data quality, intelligent integration, employee training, customer engagement, customer relationships, lead nurturing, performance reporting
     Oracle
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