A study of how University of Washington graduate students integrated an Amazon Kindle DX into their course reading provides the first long-term investigation of e-readers in higher education. While some of the study’s findings were expected – students want improved support for taking notes, checking references and viewing figures – the authors also found that allowing people to switch between reading styles, and providing the reader with physical cues, are two challenges that e-readers will need to address in cracking the college market.
Category: Integration
“I think he who knows the data wins and that transcends MDM and is true for data warehousing, CRM [customer relationship management], business intelligence and other programs,” Dyché says. “We have a major Japanese car company as a client that is embracing MDM. The data warehouse people have been jockeying and everyone wants to pad their resume and own the hub. The one smart guy said, ‘you guys can all own the platforms and technology and I’ll be the data steward.’ Guess who the star of the show is now? It’s the guy who owns the information and manages the data back to the context of its usage. He’s the one with a direct line of communication back to the data governance council. He made the right choice.”
Categories
Spreadmarts and the Ideology of BI
Spreadmarts and the Ideology of BI | The Intelligent Enterprise Blog
In the world of Business Intelligence, Excel is the devil and BI tools are the savoir. Spreadsheets are a satanic element were trying to drive from unrepentant departments. This is because centralized data is good and distributed data is bad.
I *love* the term “spreadmarts”.
Project Management Requirements Unique to Data Warehousing
Once most project managers understand that there is essentially only one process within a data warehouse, they begin to understand the importance of the initial analysis that precedes the design
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The 38 Subsystems of ETL
IntelligentEnterprise : The 38 Subsystems of ETL (printable version)
The extract-transform-load (ETL) system, or more informally, the “back room,” is often estimated to consume 70 percent of the time and effort of building a data warehouse. But there hasn’t been enough careful thinking about just why the ETL system is so complex and resource intensive. Everyone understands the three letters: You get the data out of its original source location (E), you do something to it (T), and then you load it (L) into a final set of tables for the users to query.
Man in the Middle: The Evolving Role of the Data Steward
In his February 2005 DM Review article, William Laurent writes:
Under a stewardship program, everybody shares responsibility and accountability for data excellence; information belongs to everybody, and like other (more empirical) corporate resources, it must be scrupulously managed and cared for. The stewards do not own the data; they define and bestow ownership and accountability accordingly, as they train, guide and mentor others in data quality best practices, rewarding compliance and adherence to quality specifications. Everybody that touches data throughout the organization must understand their role in data quality and be able to provide a feedback loop that will help stop bad data habits from propagating throughout the business.
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First Step to Quality and Consistency
Data stewards are the liaisons between business users and the data warehouse team, and they ensure consistent, accurate, well-documented and timely insight on resources and requirements.
TDAN – Stephens – Another Meta-Data Secret – The MetaCardCraine –
Document Strategy
The MetaCard is based on
the Dublin Core metadata standard and allows traditional search technology
to work without additional layers of integration effort. This article will
review an implementation of the MetaCard technology in a Fortune 500 company
to resolve the problem of universal asset integration.
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Proactive Data Quality
Traditional data quality activities that focus primarily on “detect and cleanse” are merely reactive measures and do very little to actually improve the quality of the data in the company. They only address the symptoms but do not remedy the root cause of the problem. Companies that embark on data quality initiatives of detect and cleanse will be very disappointed by the final outcome of their efforts and ultimately will not correct data quality problems. Effective data quality is based on proactive data quality.
Deming, Data Quality and ETL, Part 1: Point 3 – Cease Dependence on Inspection
Data quality is a continuous problem in the data warehouse industry. The cost of poor data quality has been reckoned in terms of lost productivity, lost opportunities, inefficiencies, reduced profitability and lost customer confidence. These are the same problems addressed by W. Edwards Deming. Deming helped manufacturers achieve world-class excellence by addressing the Quality Problem. Deming codified his principles into “14 Points.” Points 3 and 5 are the focus of this two-part series.