Data warehouse management strategies for CIOs

CIOs use data warehousing tools and technologies to capture and analyze data from various sources within the enterprise. This guide offers advice to manage your data warehouse.

Data warehousing, performance management and business intelligence (BI) are top priorities for enterprise CIOs looking to improve business operations, access timely data and meet compliance regulations. This month's Executive Guide offers CIOs information and advice on the various tools and strategies necessary to implement and manage a data warehouse in the enterprise.

This guide is part of the Executive Guide series, which is designed to give IT leaders strategic guidance and advice that addresses the management and decision-making aspects of timely topics. For a complete list of topics covered to date, visit the Executive Guide section.

Data warehouse disaster recovery: What's the ?

[James M. Connolly, Contributor]

It used to be that when you had a data warehouse, an analyst would run a bunch of queries against an offline copy of two years' worth of data, analyze it, and if disaster struck the data warehouse, it would simply be repopulated on a system.

Not anymore. With data warehousing and BI increasingly tied to mission-critical applications, it's not always certain that a disaster recovery (DR) accounts for your data warehouse.

"Data warehousing and business intelligence did start out as being not mission critical," said Claudia Imhoff, president and founder of Intelligent Solutions Inc., a consultancy in Boulder, Colo. "People would extract data out of the operational system and run away into their own environment, completely separated from operations. But in the past 10 years, analytics have become quite critical."

Imhoff said businesses today directly tie data warehouses to key applications, such as stock price analysis, fraud detection and inventory . Not only are companies more dependent on BI, but the systems are also serving , less technology-savvy audiences than when BI was used largely by analysts, Imhoff said. "If something goes wrong, they don't know how to fix it, and it really does cripple them in terms of making good business decisions.''

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Data warehousing: ETL vs. ELT

[Info-Tech Research Group, Special to]

Extract, transform and load (ETL) was considered the most effective way to load information into a data warehouse. Early data warehouses were not viewed as being capable of handling the extensive processing required to perform the complex transformations involved in the warehouse load process. Instead, third-party tools like IBM's WebSphere DataStage and Informatica Corp. were leveraged to orchestrate data movement between source systems and the data warehouse. With the advancement in both hardware and data warehouse software technology, warehouse designers can now consider extract, load and transform (ELT) a viable option.

Loading a data warehouse

Loading a data warehouse can be extremely intensive from a system resource perspective. In companies with data sets greater than 5 terabytes, load time can take as much as eight hours depending on the complexity of the transformation rules. Most data warehousing teams schedule load jobs to start after working hours so as not to affect performance when a user query is being executed. However, as data volumes and warehouse subject areas increase, load times can increase even further and spill over into regular working hours.

Find out more in "Data Warehousing: ETL vs. ELT." Also:

Data warehouse mergers: Best practices for CIOs


Data warehouse mergers and acquisitions, whether through vendor consolidation or other company mergers, require a solid, long-term . Forrester Research Inc.'s James Kobielus offers advice and guidelines for CIOs experiencing a data warehouse merger or acquisition.

Learn more about "Data warehouse mergers: Best practices for CIOs (A podcast)."

Gartner data warehouse DBMS Magic Quadrant 2007

[Hannah Smalltree, Site Editor,]

Gartner's 2007 data warehouse DBMS Magic Quadrant found that the market is returning to tried-and-true IT mantras.

This year is notably different from a few years ago, when the data warehousing market was experiencing an unusual trend for the IT industry, according to Mark Beyer, research director with the Stamford, Conn.-based analyst firm and co-author of the study. Companies were willing to spend significant money, time and effort on data warehousing in order to achieve ideal implementations, Beyer said. This year, he found that companies are going back to IT basics, wanting to "do better with less." That is, they want to spend less money and time but achieve better results.

That's due in part to a maturing market, he explained. Data warehousing has been around for about 18 years now, and customers expect that software vendors have successfully and cost-effectively solved problems with data warehouse physics, mixed workloads and hardware platforms -- issues Beyer discussed in last year's study. There's another reason customers are less forgiving than in years past.

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BI projects fail without C-level ownership

[Shamus McGillicuddy, News Writer]

When BI software projects fail, IT is often blamed. But the failure can usually be traced to lack of leadership, not technology.

In fact, a new survey finds that a lack of ownership by the right executive often leads to a disconnect between the vision of senior management and the way a project gets done.

"The core issue with business intelligence [not succeeding] isn't a technical issue," said Betsy Burton, vice president and distinguished analyst at Stamford, Conn.-based Gartner Inc. Rather, she said, it's the failure on the part of business leaders to make sure the organization gets the information it needs and leverages it in a way that makes sense with the business objectives.

"It's interesting," Burton said. "The symptom that people see is a lack of vision, a lack of strategy, a lack of linking supportive business intelligence back to systems. It's very easy for managers to say, 'Hey the data is wrong,' rather than take an introspective look. They should ask 'Have I given the organization a clear sense of what we're trying to get out of business intelligence? Am I really arming my people within my organization with a sense of the importance and the metrics so that they can deliver valuable information?' It's easier to point at the numbers and say, 'The numbers are wrong. Fix them.'"

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