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Barry Devlin

Faster Analytics and Better Business Intelligence

Today’s business and analytics users place high value on timeliness. Reaching decisions quickly—at the speed of thought—depends on two factors: the response speed of the data exploration tooling and the delivery speed of data into the exploration environment. Modern tools deliver these features through in-memory operation on locally stored data and direct access to operational data, respectively. The result is faster turnaround time of decisions based on more timely information.

Barry Devlin


Wayne Eckerson

Revolutionary BI: Changing the Rules of the Game

New technologies often change market dynamics, making it possible for organizations to address business needs in new and creative ways. Today, open source software, analytic databases, and other new technologies are enabling BI teams to deliver new applications that previously weren't possible in a cost-effective way.

Wayne Eckerson


Wayne Eckerson

How Pervasive BI is Good for Your Business and How to Get There

Usage rates for BI tools have nudged up from 18 percent three years ago to 24 percent today, according to TDWI Research. This abysmally low percentage accounts for most of an organization’s power users and a handful of very determined casual users. What can you do to make BI more pervasive?

Wayne Eckerson


Wayne Eckerson

Agile Analytics: The Convergence of the Cloud, Open Source and Specialized Analytic Databases

New technologies often change the rules of the game, making it possible for BI teams to address business needs in new and creative ways. BI teams that understand how to harness the power of the cloud, open source, virtualization, and high-performance analytical databases can create new opportunities to serve the business while saving money and time.

Wayne Eckerson


Philip Russom

Next Generation BI: Advancing the State of the Art in an Affordable and Manageable Way

We’re blessed in the fields of business intelligence (BI) and data warehousing (DW), in that new technologies and best practices continue to emerge, thereby advancing the state of the art. According to a recent report from TDWI Research, a long list of innovations have arrived recently, and many user organizations are considering how to incorporate these into their next generation of BI solutions and DW platforms. One of the challenges these organizations face is how to adopt technologies and practices that are new to them, while managing the complexity of these and keeping the cost down during the current recession.

Philip Russom


Philip Russom

Cost-Effective Strategies for Mainframe Modernization: Maximizing Your Information Assets With Real-Time Data and SOA

There is no question that mainframes continue to serve a wide range of organizations by providing a secure, high-performance, and scalable computing platform that’s hard to match on other systems. The issue comes when you attempt to extend mainframe data or applications to participate in new business applications on so-called open systems. Non-relational data, mainframe COBOL programs, and 3270 screen-based applications are difficult to access from open systems, and this inhibits modern data-driven business practices, like 360-degree views, on demand performance management, just-in-time inventory, business intelligence, and so on.

Philip Russom


Mark Madsen

The New Analytic Imperative: Faster Time-to-Insight

As business conditions change, enterprises need faster time-to-value and shorter deployment cycles for their decision making platforms. Hence the question in IT is shifting from how to build a data warehouse to how to speed delivery of insight and how to meet new requirements without breaking the bank. Although many organizations have collected terabytes of information, most still don’t have a cost-effective infrastructure for transforming this data into actionable insights.

Mark Madsen


Philip Russom

Best Practices in Data Profiling, Integration, and Quality

Data profiling, data integration, and data quality go together like bread, peanut butter, and jam, because all three address related issues in data assessment, acquisition, and improvement. Because they overlap and complement each other, the three are progressively practiced in tandem, often by the same team within the same data-driven initiative. Hence, there are good reasons and ample precedence for bringing the three related practices together. The result is an integrated practice for data profiling, integration, and quality (dPIQ).

Philip Russom


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