Data Management comprises all the disciplines related to managing data as a valuable resource. Data Resource Management is the development and execution of architectures, policies, practices and procedures that properly manage the full data lifecycle needs of an enterprise.
When meeting the needs of a growing infrastructure, do you build or buy? This video illustrates how using the Oracle Database Appliance, which contains the server, software, networking and storage, is easy to deploy and maintain compared to a traditional build scenario, while also being an affordable high availability database solution.
Today, the need for self-service data discovery is making data governance a charged topic. As business-driven data discovery emerges as a fundamental need, the ability to ensure that data and analytics are trustworthy and protected becomes both more difficult and more imperative. This research explains how to manage the barriers and risks of self-service and enable agile data discovery across the organization by extending existing data governance framework concepts to the data-driven and discovery-oriented business.
- The implications of the "freedom vs. control" paradox
- How to design for iterative, "frictionless" discovery
- Critical checkpoints in data discovery process where governance should be in place
As data warehouses (DWs) and requirements for them continue to evolve, having a strategy to catch up and continuously modernize DWs is vital. DWs continue to be relevant, since as they support operationalized analytics, and enable business value from machine data and other new forms of big data. This TDWI Best Practices report covers how to modernize a DW environment, to keep it competitive and aligned with business goals, in the new age of big data analytics.
This report covers:
• The many options – both old and new – for modernizing a data warehouse
• New technologies, products, and practices to real-world use cases
• How to extend the lifespan, range of uses, and value of existing data warehouses
Although the phrase “next-generation platforms and analytics” can evoke images of machine learning, big data, Hadoop, and the Internet of things, most organizations are somewhere in between the technology vision and today’s reality of BI and dashboards. Next-generation platforms and analytics often mean simply pushing past reports and dashboards to more advanced forms of analytics, such as predictive analytics. Next-generation analytics might move your organization from visualization to big data visualization; from slicing and dicing data to predictive analytics; or to using more than just structured data for analysis.
The University of Rochester Medical Center (URMC) is of the nation’s top academic medical centers. Their InfoSec team wanted to implement a program that could withstand constant shifts in regulatory requirements while protecting the sanctity of patient health information. Learn why the selected Rsam and the results of their successful implementation.
Healthcare enterprises are under more scrutiny than ever to demonstrate compliance. With more than 5,000 suppliers, this FORTUNE 50 company needed to automate and streamline their vendor risk management program. Learn why they selected Rsam and the results of their successful implementation.
The rise of the web drove the need for applications in the browser. JSON is natively supported and easy to understand, with facile methods to learn our technical details. JSON sums new web technologies with client side practices to achieve an interactive web application.
This white paper explains the value of putting data and insight
to work to accelerate an organization’s journey to becoming a
Cognitive Business, by enhancing their investments in data and
analytics and realizing new opportunities from cognitive systems
that understand, reason and learn.
Today, in the age of information, people are paid for their ideas: to create original knowledge products or add value to existing products. Given their self-reliance, it is not surprising that workers take pride in their outputs—up to half of employees take a portfolio of files with them when they leave.
When employees move on, many feel entitled to the work they’ve created.They presume it is acceptable to transfer work documents to personal computers, removable media, tablets, smartphones or online file sharing apps. Some pilfered data is innocuous and already in the public realm. But some of it is classified. Read this paper to find out how to collect and secure data to protect operations, reputation and continuity when employees leave.
Data loss, theft or breach is inevitable, but backup assures recovery, continuity and rapid response.
Endpoint data backup is at the core of an enterprise data security strategy. Modern endpoint backup goes well beyond backup and restore, delivering risk reduction across the enterprise and addressing perennial IT and business problems. Discover more by reading this whitepaper.
When the right approach is applied, analytics can drive more effective marketing strategies. While marketers understand the role analytics plays within the organization, most are not leveraging analytics to really drive enterprise performance. We surveyed 100+ business leaders to understand the state of analytics maturity across today’s leading organizations, uncovering common challenges teams are facing in their quest to use data and analytics to deliver a competitive advantage.
What We Uncovered:
- 73% of analytic professionals claim to work for an analytically-driven company
- Only 42% of companies have a strategy for using analytics across the enterprise
- Just 38% of companies share results of their analytic insights outside their department
- 81% of organizations rely on 3rd parties for at least some portion of their analysis
Download the report to learn how marketers, like yourself, view themselves in light of using analytics to drive their business.
This white paper describes how IBM’s Pure Data System for Analytics delivers speed and simplicity to help organizations become more responsive and agile in today’s increasingly mobile and data-driven market.
Big Data has generated much interest and attention in the media of late. Indeed, several authors have recently raised the question of whether Big Data approaches, such as Hadoop, will pronounce the death sentence on the conventional data warehouse.
In this survey we investigate the current state of the data warehouse and examine its recent challenger in the form of Big Data solutions as an alternative. Is the new technology really complementary or is the reign of the data warehouse nearing an end?
The use of analytics will define the successful midmarket business of the next decade, just as it will define the successful larger organization in this information age.
The past decade has seen rapid change in the business climates with an explosion of global competition and economic uncertainty. Despite this, many midmarket companies have thrived by using their size and agility as a competitive advantage. Certainly they have fewer resources than large corporations who are able to invest large sums in IT. However, small yachts can turn and adjust their course much quicker than large cruise ships. The same came be said for midmarket companies.
The most successful organizations are ones that can react the quickest to changes in the market place. Midmarket companies can take advantage of their smaller size by being more agile and quicker to change, or even reinvent themselves, to respond to the market.
To help enterprises create trusted insight as the volume, velocity and variety of data continue to explode, IBM offers several solutions designed to help organizations uncover previously unavailable insights and use them to support and inform decisions across the business. Combining the power of IBM® InfoSphere® Master Data Management (MDM) with the IBM big data portfolio creates a valuable connection: big data technology can supply insights to MDM, and MDM can supply master data definitions to big data.
"Five high-value uses for big data: IBM has conducted surveys, studied analysts’ findings, talked with more than 300 customers and prospects and implemented hundreds of big data solutions. As a result, it has identified five high-value use cases that enable organizations to gain new value from big data."
With the advent of big data, organizations worldwide are attempting to use data and analytics to solve problems previously out of their reach. Many are applying big data and analytics to create competitive advantage within their markets, often focusing on building a thorough understanding of their customer base.
Today data volumes are exploding in every facet of our lives. Business leaders are eager to harness the power of big data but before setting out into the big data world it is important to understand that as opportunities increase ensuring that source information is trustworthy and protected becomes exponentially more difficult. This paper provides a detailed review of the best practices clients should consider before embarking on their big data integration projects.
"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."
By using InfoSphere Information Server “flexible integration” capabilities, the information that drives business and strategic initiatives—from big data and point-of-impact analytics to master data management and data warehousing—is trusted, consistent and governed in real time.
IBM commissioned Forrester Consulting to conduct a Total Economic Impact (TEI) study and examine the potential return on investment (ROI) enterprises may realize by leveraging IBM InfoSphere Information Integration and Governance (IIG) solutions.
While the term 'big data' has only recently come into vogue, IBM has designed solutions capable of handling very large quantities of data for decades. IBM InfoSphere Information Server is designed to help organizations understand, cleanse, monitor, transform and deliver data.