#10
Preparing Your Organization for Master Data Management (MDM)
There is evidence that the master data management (MDM) movement is maturing, as organizations consider the incorporation of data quality and data governance processes to supplement the emergence of master data systems. Yet, there is room for improvement and for those intrigued by the potential benefits of MDM, it is worthwhile to understand the potential pitfalls and potential “stalls” in a successful deployment.
Some of the barriers to success are “people related”—a lack of understanding who the consumers of master data are or their expectations, and failing to ensure that the MDM framework meets their needs. Others are “data related,” where data consolidation greatly magnifies the impacts of minor differences across data subsystems. Some barriers are systemic issues related to modeling, standards for information sharing, or general data governance and oversight.
This TDWI Checklist Report reviews opportunities where anticipating potential roadblocks can help improve planning, identify issues early in the program, and generally eliminate barriers to MDM success.
#9
Real-Time Data Quality
Data management continues to move closer and closer to real-time operation. This includes data management disciplines for data quality, data integration, master data management, and event processing. Among these, real-time data quality (RT DQ) is the second-fastest growing discipline, after master data management and just before real-time data integration.
The gradual migration toward real-time operation is the strongest trend in data management today, and the growing adoption of RT DQ is part of that trend. But why the rush to real time?
There are many good technology and business reasons to deploy real-time data quality functions today. This TDWI Checklist drills into the details and desirable use cases for RT DQ to help user organizations understand what’s available and when to use it.
#8
Hadoop: Revealing Its True Value for BI
Hadoop and related technologies have been with us for over five years now, but BI/DW professionals have only recently started exploring them, motivated by the rise of big data analytics. The business advantages of big data analytics are the leading reasons why BI/DW professionals need to know more about Hadoop now. Despite the short-term confusion, TDWI anticipates that Hadoop techniques will soon become a common complement to older BI/DW approaches.
To help BI/DW professionals and other people prepare for the eventual widespread use of Hadoop and its extended ecosystem, this Checklist Report drills into common points of confusion. It clarifies these points and reveals the true value of Hadoop for BI, DW, big data, and analytics.
TDWI Checklist Reports provide a succinct description of the best practices required to succeed in a particular area of BI/DW. Here are three of the most popular checklist reports of 2011.
#7
Dashboards & Visual Discovery Tools; an Evaluation Framework
Dashboards and visual discovery are now must-have components of the BI tool portfolio. What’s driving their popularity? Capabilities vary widely among BI platform vendors and specialty solutions. This Webinar highlights key evaluation criteria, industry trends, and differences in leading products.
You Will Learn:
- The business value and use cases for dashboards and visual discovery
- How these modules complement and compete with each other
- Key criteria in evaluating solutions
- Differentiators of leading products
TDWI Webinars cover the whole gamut of topics in BI/DW. The following two Webinars had a great impact with technology professionals for their timeliness and subject matter.
#6
Hadoop Bursting its Myths to Reveal its True Value for BI
Despite all the hubbub and hype around Hadoop, few business intelligence (BI) professionals know much about what Hadoop is, how it works, or in which situations they should deploy it. In fact, numerous myths about Hadoop persist among BI professionals and their business counterparts. Here are some myths that need busting:
- It's a distributed file system, not a database management system
- It's also an analytic processor called MapReduce, not just the Hadoop distributed file system
- It depends on MapReduce, Pig, and Hive to express queries and analyses, not standard SQL (although this will change)
- It's for diverse document and data types, not just for big volumes of structured data
- It can handle real-time data, not just history-oriented data
- And more...
This Webinar presents a panel discussion on how to help BI professionals prepare for the eventuality of Hadoop technologies becoming common components of BI and data warehouse technology and discuss the myths of Hadoop to reveal its true value for BI.
TDWI Webinars cover the whole gamut of topics in BI/DW. The following two Webinars had a great impact with technology professionals for their timeliness and subject matter.
#5
What works in Emerging Technologies: Volume 32 – White Papers
The white papers published in this edition of What Works in Emerging Technologies offer a fresh, topically focused collection of expert perspectives to enhance your understanding of the tools and methods that are central to today’s emerging technologies in data integration, data warehousing, and business intelligence (BI). The white papers cover the following categories: analytic databases, collaborative/agile/lean data integration, columnar databases, data governance, data warehousing appliances, mobile business intelligence, real-time data integration, software-as-a-service, and unified data integration platforms.
#4
Mobile BI and Analytics: Extending Insight to a Mobile Workforce
Excitement is growing among business users about how they could use mobile devices to transform the role of information in customer interactions, performance management, planning and forecasting, and their daily operational decisions while on the go. This report examines the impact of mobile computing on the development, management, and implementation of applications and services for business intelligence and analytics.
#3
Big Data Analytics
Big data analytics is the intersection of two technical entities that have come together. First, there’s big data for massive amounts of detailed information. Second, there’s advanced analytics, which can include predictive analytics, data mining, statistics, artificial intelligence, natural language processing, and so on. Put them together and you get big data analytics, the hottest new practice in BI.
#2
Self-Service Business Intelligence: Empowering Users
to Generate Insights
In today's economic environment, organizations must use business intelligence (BI) to make smarter, faster decisions. Yet, in too many organizations, decisions are still not based on business intelligence because of the inability to keep up with demand for information and analytics. To satisfy this demand, one approach involves setting up a self-service BI (SS BI) environment.
#1
Next Generation Data Integration
Data integration (DI) has undergone an impressive evolution in recent years. Today, DI is a rich set of powerful techniques, including ETL (extract, transform, and load), data federation, replication, synchronization, changed data capture, data quality, master data management, natural language processing, business-to-business data exchange, and more. This report brings readers up to date on all that's happening in this exciting arena of data management.