Recommendations for Simplifying OS Migration

OS migration is typically time-consuming and expensive. The average enterprise migration project takes 18-32 months from conception to completion and involves significant IT time and end-user downtime. An endpoint backup solution can simplify OS migration by giving IT the ability to migrate data, as well as system and application settings, effectively and seamlessly. To make your next migration easy, follow these six recommendations when planning your project.
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7 Elements Radically Simple OS Migration

OS migration is typically time-consuming and expensive. The average enterprise migration project takes 18-32 months from conception to completion and involves significant IT time and end-user downtime.1 An endpoint backup solution can drastically simplify the process by enabling IT to migrate data and personal settings effectively and seamlessly.
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5 Things You Didn’t Know About Cloud Backup

Only customers have access to the key and, subsequently, their data, once authenticated. To perform enterprise restores quickly, a backup solution must also use multi-threaded restores in order to allow parallel transfers of multiple files. Using multi-threaded restoration significantly accelerates the time it takes to restore data to a user’s computer.

This Whitepaper provides real-time visibility, enabling organizations to support their governance and compliance needs.

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Information Governance Best Practice: Adopt a Use Case Approach

When should you implement integration governance? According to this Gartner research note, you should start now if you are an IT professional considering an initiative like MDM, e-discovery, information archiving or cloud migration. The need for governance is prompted by a combination of massive data growth, litigation, new data types, scrutiny from a regulatory perspective and new information privacy risks.
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Improve the Confidence in Your Big Data with IBM InfoSphere

This white paper discusses how IBM InfoSphere can improve the confidence in Big Data across nine common use cases including Single Customer View, Application Development, Application Efficiency, Security & Compliance, Application Consolidation & Retirement, Data Warehouse Augmentation, Operations Analysis, Security/Intelligence Extension and Big Data Exploration.
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Big Data Needs Agile Information And Integration Governance: Forrester Consulting Report

Organizations are no longer asking what big data is, they are looking at what big data can do. Information and integration governance (IIG) is playing a significant role to move big data from the realm of possibility to the reality of business outcomes. Rather than bypassing IIG with the goal of agility and fast results, organizations are embracing it and recognizing that IIG is a critical factor in the success of big data initiatives.
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Data protection for big data environments

As organizations plan big data projects, business and IT leaders must recognize that data security cannot simply be added later - it must be built into big data implementations from the start. This ebook walks through the requirements of big data security.
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Data masking everywhere – Design standard and repeatable data privacy policies across the enterprise

The new era of computing has arrived: organizations are now able to process, analyze and derive maximum value from structured, unstructured and streaming data in real time. However, in the rush to achieve new insights, are privacy concerns being neglected? With the average cost of security-related incidents in the era of big data estimated to be over USD40 million, you can’t afford to ignore data privacy as a top requirement.
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Preserving quality: Fundamentals of reference data management

Most enterprise applications contain reference data, built into code tables, to classify and categorize product information, customer information or even internal transaction codes. This data changes relatively infrequently—but it does change over time, and given its ubiquity, synchronizing reference data values and managing changes across the enterprise is a major challenge. This e-book introduces five tenets of a solid reference data management strategy and how you can use them to build a consistent foundation for your business’s information management needs.
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Magic Quadrant for Master Data Management of Customer Data Solutions

This Gartner's Magic Quadrant offer insight into the part of the packaged MDM solution market that focuses on how organizations master and share a "single version" of customer data with multiple views of it across their organizations - achieving a single version of master data is a key initiative for many organizations. Learn about IBM's leadership position with regard to both its Master Data Management vision and its ability to execute on that vision.
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Making the case for data lifecycle management – A must-have element for business transformation in a data-driven world

The ability to turn large volumes of data into insights is a competitive advantage in today's economy. To manage the data, the best CIOs are applying data lifecycle management strategies so they can:

-Fully understand what data they have, where it resides and what policies and standards exist for managing it
-Create efficient test and development environments to support application delivery schedules
-Take advantage of peak application performance
-Effectively manage data retention and access according to business compliance requirements
- Protect sensitive data

Read the whitepaper to find out how one client improved business value of their data by implementing InfoSphere Optim processes and technologies.

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Business Driven Governance: Managing Policies for Data Retention

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.
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