White Papers and Research

Data Quality Essentials: A Project Manager's Guide to Data Quality

Any data intensive project offers an opportunity to improve data quality. Initiatives ranging from CRM, MDM, ERP, BI, data warehouse, data governance, and any data migration, consolidation, or harmonization endeavor warrant a closer look at the quality of the data that will populate the target application or system. To provide a roadmap for optimal effectiveness, we offer a detailed guide that identifies the data quality- related tasks that should be incorporated into a project plan.

7 Accelerators for Putting Analytics to Work with Business Rules

While predictive analytics can dramatically improve business results, bringing that power to bear on transactional systems has historically been difficult. If analytic models must be hand-coded or hand-integrated with transactional systems, companies may be slow to realize value from its models. In addition, the resulting system would likely be hard to change, decreasing agility. The use of a business rules management system to implement analytic models can generate more value sooner and ensure that the resulting system remains agile. Learn seven “accelerators” – or best practices – for using business rules to put analytics to work more effectively and rapidly.

Data Requirements for Advanced Analytics

According to a recent survey from TDWI Research, 38 percent of organizations surveyed are practicing advanced analytics today. This number is rising dramatically as organizations search for a way to understand the constantly changing business environment and to discover opportunities for cost reductions and new sales targets. Download the TDWI Checklist Report, Data Requirements for Advanced Analytics, to discover how your organization can use advanced analytics to discover relationships, predict the future and adapt to change. This report will help you: · Understand the difference between reporting and analysis, and the unique data requirements involved in each practice. · Move past the simple design of a data warehouse for online analytic processing, and expand to fulfill the data requirements for advanced analytics. · Adjust your data management practices, including data integration and data quality, to fit the needs of an analytic database. This TDWI Checklist Report seeks to clear the confusion many organizations have about data requirements for analytics and explains how to ensure the right data is available in the right condition.

Business Drivers and Enabling Technologies for Clickstream Data Warehouse Initiatives

Dramatically reducing the time, cost and effort required for integrating large amounts of web data can radically simplify an organization’s ability to analyze online visitor behavior via a clickstream data warehouse (CDW). Learn how to optimize your CDW and: · Gain greater insight into online customer behavior · Make more strategic decisions based on actionable data · Increase margins, lower costs and improve bottom line · Increase staff productivity

Addressing the Destructive Business Impact of Data Performance Problems

Accelerate your current data integration environment and eliminate the processing bottlenecks and data latencies caused by exponentially growing data and shortened operational timeframes. This white paper offers up a cost-effective, scalable and efficient alternative to traditional and costly Band-aids like adding expensive hardware, custom-coding, and the “rip-and-replace” approach. Learn how to optimize your DI environment and: · Increase revenue opportunities · Decrease costs · Improve decision-making · Secure customer retention

Agile Corporate Management

Find out how companies have leveraged Business Intelligence to become more agile.

Experience has shown that while information is often available, it has not been placed in the context of the businesses processes and has therefore been of only limited use to corporate management. Performance Management is a business model that allows a company to continuously align corporate goals with business processes and keep them consistent. When used as a comprehensive solution with BI, it can facilitate agile corporate management.

AsterData nCluster: A New Architecture of Data Analytics

Aster Data (www.asterdata.com) has developed and brought to market Aster Data nCluster, a new platform for data analytics. Based on the groundbreaking research of three Stanford doctoral students in computer science, nCluster features an innovative shared nothing, massively parallel architecture while running on low priced, commodity hardware. Read this Richard Winter whitepaper that details what you need to consider when evaluating a data warehouse architecture to enable advanced analytics on massive data sets.

Common Models in SOA: Tackling the Data Integration Problem

Creating an SOA without a data integration strategy to address semantic challenges will limit an SOA project at best, and doom it to failure at worst. Creating a common model ensures that all the data exchanged between the various systems and services within the SOA is consistent. This approach can significantly reduce SOA project timescales, lower development costs and avoid problems associated with poor data quality. This paper, written by Dave Hollander, Mile High XML, presents ideas on how you can improve operational responsiveness by tackling data integration problems head on.

High Value Business Intelligence on a Budget

Whether you’re an IT leader or finance executive seeking high ROI business process improvement, you can deploy a cost-efficient Business Intelligence solution that will improve the way your organization uses information, collaborates and make decisions.

Learn how Blastrac built a collaborative dashboard and reporting environment for less than 20% the cost and time of traditional BI.

In this whitepaper you'll learn:

* The 7 proven principles of implementing and deploying low-cost, high-ROI BI * How Blastrac delivered results within days and a full-scale solution within weeks * Why your data infrastructure and software toolset drive user satisfaction

Author: Elissa Fink, VP Marketing, Tableau Software

Guided and Open-ended Analytics: Serving the Real Users of BI

Measuring performance, investigating variances, looking over the horizon and making plans accordingly are fundamental activities in a knowledge-based economy. The starting point is acquiring a deeper understanding of the causes and effects of things, both internal and external, and having it spread throughout the organization. To do this requires, among other things, tools that provide the promise of self service to evaluate, investigate and share. However, not everyone is capable or is interested in building models or maintaining analytical applications they’ve developed.

Getting the job done, then, requires a mix of tools and approaches. For those who have an analytical perspective but not a technical one, some sort of guided analytics through data and models is called for. What are currently referred to as dashboards represent a good compromise of function and aesthetics. A smaller constituency desires to not only create analyses, but to share them with those who are not so inclined. This sort of open-ended analytics allows an analyst (or really anyone with the understanding of the data and relationships in the organization) to author their own analytical scenarios for themselves or for sharing with others.

The focus of this paper is to:

* describe the use of these tools from a cognitive point of view, * examine the differences between guided analytics and open-ended analytics, * and propose some best practices for deployment and use.

Author: Neil Raden, Founder, Hired Brains

Data Migration for Project Leaders

The project leader can play a critical role in ensuring a successful data migration. This guidebook by Dylan Jones of Data Quality Pro examines exactly what responsibilities and activities project leaders should fulfill and supplies details for these responsibilities, including sections on essential activities such as planning, forecasting, risk management, team selection, communication and collaboration.

De-Risking Data Migration: The Case for Data Quality Technology

This white paper by Dylan Jones provides practical advice that will help the reader understand the pivotal role data quality technology must play in a data migration. Jones describes five distinct implementations of data quality technology in detail, each one providing clear evidence for the need to - and the benefits of - adopting the right data quality approach in data migration projects.

Data Quality and Cost Reduction

This paper by David Loshin of Knowledge Integrity reviews aspects of cost reduction and examines some typical financial accounting expense categories. Selecting some specific examples and assessing their reliance on high-quality data, Loshin looks at how data quality services can be applied in those examples to reduce expenses, and examines the potential for applying data quality management as a way to manage and reduce organizational expenses.

Value Proposition for IBM DB2 9.7: A Cost/Benefit Case for SAP Migration

For many organizations, the immediate focus is on cost cutting. But business needs must still be met. Trends toward greater volatility of markets, erosion of traditional forms of competitive differentiation,industry concentration, and the effects of globalization have not diminished. Migration from Oracle 10g to IBM DB2 9.7 can significantly reduce costs not only for database software, but also for server and storage infrastructures and for the personnel who administer and maintain these resources. This white paper discusses the cost/benefits for migrating to IBM DB2 9.7.

Web Self-Service and the Multi-Channel Customer Experience

In today's highly competitive economic climate, there is no margin for error when it comes to attracting and retaining customers. Businesses must now offer more channels of communication, more customer options, and faster responses than ever before. This white paper examines the best ways to use exceptional customer service to differentiate your organization in the rapidly evolving multi-channel customer service environment. Beginning with your contact center and web properties, and extending out to include assisted channels, mobile and new social media, this paper examines the multi-channel approach necessary to successfully manage the customer experience for an organization.