Featured White Papers

How Data Warehousing Challenges Can Be Resolved Through Modelling

Data warehouse solutions are viewed differently today. The emphasis is less on the technological problems, and more on the day-to-day issues of living and working with a data warehouse. Issues include: • Configuration/Change Management • Managing and Improving Data Quality • Engagement with the Enterprise Architecture • Enhancing Return on Investment David M. Walker, data warehouse industry expert identifies the challenges that are affecting today’s data warehouse environment and how modeling can address those challenges.

Sybase® IQ: The Economics of

The demand for business intelligence is growing. Decision-makers need better access to information to make smarter, faster decisions in response to changing circumstances. Reporting is one form of business intelligence that has become business-critical.

Selecting a Visual Analytics Application

Visual analytics is becoming the fastest way for people to explore and understand data of any size. Read our guide for selecting a visual analytics, or data visualization, software application. Based on the 7 defining characteristics of visual analytics, this white paper will tell you exactly what to look for and how to evaluate applications. You’ll learn exactly the questions to ask application vendors about their visual analytics capabilities.

Gaining the Performance Edge Using a Column-Oriented Database Management System

As organizations continue to employ larger data warehouses, the need for performance will continue to outpace the capabilities of traditional relational databases. This paper explores the data explosion and why column-oriented database systems are able to provide the boost needed to gain the performance edge.

Forrester Report: Best Practices for Scaling the Next-Generation Enterprise Data Warehouse

Data warehousing professionals continue to expand the scale, scope, and deployment roles for their enterprise data warehouse investments. Today’s most demanding EDW environments support petabytes of aggregated data, trillions of records, thousands of concurrent users and queries, complex mixed-query workloads, subsecond latencies, and continuous high volume data loading. Information managers are adopting EDW best practices that push the scalability and performance envelope without sacrificing the agility to optimize this critical infrastructure to ever-changing analytic workloads.

Reaping the Benefits of Next Generation Dashboards

Traditional BI solutions face many obstacles and limitations. However, the need for timely and succinct business intelligence (BI) continues to grow. As a result, BI has continued to evolve - creating a next generation of BI. This white paper explores how organizations are demanding more integrated and flexible business intelligence solutions that facilitate more timely and informed decisions, as well as how next generation of dashboards solve these challenges.

Overall Approach to Data Quality ROI

Data quality is an elusive subject that can defy measurement and yet be critical enough to derail any single IT project, strategic initiative, or even a company as a whole. Authored by William McKnight, this white paper outlines six key steps that organizations can put into process to help realize tangible ROI on data quality initiatives, ensuring that corporate data assets can be leveraged as a competitive advantage.

Enterprise Information Management: Strategy, Best Practices & Technologies on Your Path to Success

Authored by Frank Dravis, Baseline Consulting, this paper discusses: (1) EIM strategy development and (2) enabling information management technology. Understanding these two areas is crucial to starting, planning and executing an EIM initiative. And while there are other important technology concepts in EIM—such as data warehousing and data security—only a corporate-wide data management vision can bind disparate, heterogeneous data sources together in a framework for access and sharing of data. As such, the paper also discusses metadata management, master data management, data quality, and data migration—all of which play important roles in integrating and managing data.

Business Intelligence for the Small to Medium Sized Business

As information flows more freely in the business world, decisions need to be made quicker and based on sturdier data. The analytical capability that was once reserved for large enterprises has now permeated the world of Small to Medium Businesses (SMBs) and provided a solid foundation of visibility into what really matters to these companies. Aberdeen's research shows a marked uptake in the use of Business Intelligence (BI) solutions in the SMB market over the past year. Based on feedback from almost 650 end-user organizations, this report serves as a roadmap for SMBs looking to improve their analytical capability and achieve Best-in-Class performance through the deployment of BI.

Why Midsize Companies Need Business Intelligence Solutions in this uncertain global economy

Learn how midsize companies are putting in place a business intelligence information infrastructure - making them more likely to adhere to their budgets and deadlines, concentrate on improving the overall business rather than just one department, and reward employees accordingly.

7 Ways to Reduce IT Costs with Master Data Management

Faced with budget cuts In today's economic environment, how can IT maintain service levels and continue to provide value to business? Smart IT decision-makers are investing in master data management

Using Master Data in Business Intelligence

Improve planning, building and deploying a master data management environment. Consider an evolutionary – rather than revolutionary – approach to MDM, and examine how to use MDM with business intelligence applications and an underlying data warehousing environment to improve decision-making.

Data Management for Business Intelligence

As the sheer amount of information available for business intelligence applications has grown, and the sophistication of BI capabilities has accelerated, organizations are striving to improve time-to-information for business users. Data storage and retrieval decisions have started to be made based on cost rather than fulfilling a business need, and this has led to compromises that impact business performance. In January and February of 2008, Aberdeen Group conducted a research program among 333 respondents from several industry sectors, geographies, company sizes and job roles. The program included an online survey and several one-on-one interviews.

How Banks Can Enhance Online Customer Relationships

Banks have traditionally organized their customer service efforts by product or channel, which places the burden of navigating the bank’s various organizations on their customers, leaving them confused and frustrated. As more institutions move to simplify and integrate their channel services using online customer portals, banks must make organizational and technological changes to attract and retain their customers.

Open Source Paves the Way for the Next Generation of Enterprise IT

In October 2008, Bull commissioned Forrester Consulting to evaluate the upcoming paradigm change of open source software enterprise adoption. In conducting in-depth interviews with 132 senior business and IT executives from large companies that are using open source products, Forrester found that these companies are embracing a fundamentally different understanding of software.

CRM Metrics That Really Work

Like most other three-letter management programs that have delivered less than they promised, CRM has failed to result in stronger relationships with customers and increased profits in more organizations than it has succeeded in. Part of the reasons for this failure are the faulty assumptions that building relationships is as simple as having the right data on your customers, and following a systematic process of behaviors to build and sustain relationships.

Practical Open Source Data Integration: Case Studies & Implementation Examples

Over the past few years, open source has established itself as a key component of the overall data integration market. Many organizations have adopted this model for their data integration projects. These organizations span all industries, all continents and all company sizes. More importantly, their projects range from ETL for data warehousing or business intelligence to operational data integration, data migration, data synchronization, etc. This reference book presents selected case studies, illustrating real-life implementations of open source data integration and its associated benefits.

The BI Survey 8

The BI Survey provides a detailed quantitative analysis of why customers buy BI tools, what they use them for, how successful they are and why they eventually abandon them

The Three Key Phases for Data Migration - and Beyond

Companies who have already done major data migration or consolidation work often struggle with incompatible system designs and technologies, limited knowledge of what data exists, where it came from, and a lack of standards on what constitutes “good” or “valuable” data. All of these issues have the same root cause: inconsistent, unreliable and inaccurate data in the source systems that is capable of polluting the new application. This white paper examines how data management technology can solve these issues through a three phase process of analyzing, improving and controlling data.

Improving the Accuracy of Spend Analysis through Data Quality

Organizations can spend as much as 60 percent of revenue to acquire the goods and services necessary to conduct business. Procurement professionals are being asked to reduce the organization’s overall spend, while simultaneously improving supplier collaboration. Companies now realize more than ever the effect of procurement strategies on their profitability and viability. Poor-quality product data creates difficulties in controlling the costs of production, promoting the productivity of the company, and the delivering finished goods. This white paper offers practical advice on how data quality technology can be used to address these issues.

Data Quality Remediation

When errors in data are identified, the data stewards responsible for the data must take action. This white paper by David Loshin from Knowledge Integrity reviews the pieces of that immediate action plan: the triage and analysis tasks performed by data quality analysts or data stewards when an issue is identified and logged in the data quality incident tracking system.

Business Intelligence: Putting Information to Work

A recent survey by 'The Economist' on business intelligence/analytics revealed the following IT priorities: - Platform standardization - process embedded analytics - performance management - speed (in-memory processing) - information quality You will notice a strong match between customers' needs, and SAP's offerings and strategy for BI and Analytics - confirming SAP's vision and direction. This (independent) study was sponsored jointly by SAP and Intel, further demonstrating SAP's commitment and thought leadership for BI/analytics. An integral element of a larger BI/Analytics campaign by SAP and Intel, this work will also further the market's awareness and recognition of SAP's leadership in the BI/Analytics space.

Trust and Competitive Advantage: An Integrated Approach

Learn how a comprehensive governance, risk and compliance strategy can help your organization rebuild and strengthen stakeholder trust through honesty, accountability, transparency and consideration of others' business interests. SAP can help.

Master Data Management: Extracting Value from Your Most Important Intangible Asset

Explore the importance of sound master data and the benefits of how achieving effective master data management can offer your business, including improved operational efficiencies, increased revenue opportunities, better business insights, and tighter compliance with regulatory requirements.

Business Intelligence for the Health Care Industry: Actionable Insights for Business Decision Makers

Read this white paper by Don Tapscott and explore the criteria best-in-class hospitals use for selecting business intelligence solutions. Companies that learn to effectively harness the information generated by their IT systems will enjoy substantial productivity improvements.

Seeing the Big Picture: A Corporate Guide to Better Decisions Through IT

Get timely, accurate information and use it wisely to allocate your small or midsize company’s resources for maximum effect. Assess your business and IT systems to ensure you have the right information at the right time – to stay ahead of competitors by making better decisions faster.

Tightening the Alignment Between Business and IT through Modeling

Achieving alignment between an organization’s core goals and the IT infrastructure that supports an enterprise’s strategic direction is an on-going challenge. Through this paper, we’ll look at the ways that business processes and the data for information systems can be used to align business needs and IT. This paper will also discuss the value of aligning business and IT using a model-driven approach which streamlines communication and increases productivity throughout the enterprise.

Combining Data Profiling and Data Modeling for Better Data Quality

For most organizations, data-source discovery, analysis and comparison are major resource drains during data-intensive projects. Data profiling integrated with data modeling can help you overcome traditional data discovery challenges by: finding hidden inconsistencies in data, providing metrics to correct errors in your database environment, inferring key structures for undocumented legacy systems, cross-system analysis to identify a 'single source of record,' and comparing live instance data with model design to ensure that design matches reality.

State of the Data Integration Market

The growth of the data integration segment has exceeded market expectations as companies recognize the fundamental importance of unified enterprise data. The market is now expected to exceed $3 billion by 2012. Because data integration includes such compelling business drivers as costs savings and improved efficiencies, it remains an investment area even in times of economic stress. To cut through the noise of this vibrant market, Oracle completed a thorough analysis of the Data Integration market, drawing on leading analyst reports, articles and a global survey with over 350 top companies. In this report, you’ll discover how data integration is helping companies: reduce development costs by 30 percent, improve business process execution times by 70 percent, rapidly increase speed the handling of data by 50 percent.

Lower Mainframe Total Cost of Ownership by Exploiting zIIP Specialty Engines

Exploit your mainframe zIIP specialty engines for dramatically lower mainframe TCO. DataDirect Shadow helps you unlock the zIIP to handle processing intensive Web services involving CICS, IMS, Natural and more! Read the latest Shadow zIIP Benchmark Report to find out how to offload up to 80 percent of your integration processing.

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