What Works in Big Data: Volume 37, June 2014

What Works 36Download What Works in Big Data, plus a selection of bonus white papers, for a comprehensive collection of case studies, best practices, and expert insight focused on business intelligence, data warehousing, and big data.

You'll get insider tips from all three TDWI Research analysts—David Stodder, Philip Russom, and Fern Halper. Together they discuss how you can attain the most value from big data—including how to experiment with real-time analytics in your Hadoop environment, and advances in visualization that can make immediate impacts on the business and BI users.

Also, find recommendations for modifying your data warehouse to accommodate new and old data requirements; how to make analytics work in your big data effort; and why big data and BI work so well together.


  • Case studies highlighting the most innovative BI and DW implementations in the industry today
  • Lessons from leading experts in the services, software, and hardware vendor communities
  • Featured article: "Attaining Value from Big Data: Recommendations, Tips, and Best Practices” by all three TDWI Research directors: David Stodder, Philip Russom, and Fern Halper
  • Selections from the most recent TDWI Best Practices Reports


To download the latest edition of What Works and the bonus white papers, click here. To download a selection of white papers, select or deselect the individual checkboxes and click the Submit button at the bottom of the page. White papers are provided by our sponsors.

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The Omnichannel Superstore

It’s no secret that today’s retail environment poses significant challenges for retailers with brick-and-mortar presence. Many retailers face eroding margins and the fading effectiveness of their traditional tools for influencing consumer behavior. There is no shortage of proposed systems to cope with the challenges retailers face, or of big claims about the impact those systems can make, so finding the way forward to a real solution can be confusing. It requires a thoughtful assessment of how the environment shapes requirements, and then of how those requirements can best be met.

Big Data Must Become a First-Class Citizen in the Enterprise

The most direct path to making big data—and Hadoop—a first-class citizen will be through an "embrace and extend" approach that not only maps to existing skill sets, data center policies and practices, and business use cases, but also extends them.

How Big Data Can Lie: Ensuring Accuracy in Predictive Analytics

Big data is about more than just storing massive volumes of information. It also creates tremendous opportunities by leveraging predictive analytics. This paper highlights how combining advanced analytics solutions—such as predictive analytics—with data integration and integrity technologies can facilitate successful analytics and decision management in big data scenarios.

Putting the Data Lake to Work: A Guide to Best Practices

The concept of a data lake is emerging as a popular way to organize and build the next generation of systems to master new big data challenges of managing and exploiting new forms of data. Before creating data lakes, however, an understanding is needed.

Data Stewardship in Complex and Big Data Environments

This paper explores the concepts of data stewardship, which is the practice of monitoring, measuring, managing, and reporting on data integrity, data policies, and data performance. It highlights three comprehensive suites—Data Quality, Master Data, and Integration—to facilitate effective data stewardship in any traditional, complex, and big data scenario.

Enterprise Data Hub: The Key to the Information-Driven Enterprise

Cloudera is pushing the boundaries of what’s possible in order to solve the next generation of business problems by delivering an enterprise data hub powered by Apache Hadoop.

Big Data: What Does It Really Cost?

There are two major platform architectures for implementing big data analytics: the data warehouse and Hadoop. A key challenge is choosing which of these big data solutions to use for a specific analytic application. In this WinterCorp Report, a framework for determining the total cost of data (TCOD) is presented, factoring in not only all system-related costs but also the software-related cost of developing the analytic business solution, including the cost of developing the applications, queries, and analytics that use the data.

Big Data and Enterprise Data: Bridging Two Worlds with Oracle Data Integrator 12c

Volume, velocity, variety—big data is in vogue. Why? Enterprises know that there is a treasure trove of information they should be tapping into. This information is predominantly less structured data consisting of weblogs, social media, e-mail, sensors, and photographs that can be mined for useful information. Whether it is healthcare, financial, manufacturing, government, or retail, big data presents a big opportunity. What are the best ways to bridge enterprise data with big data through a common set of unified data integration tools? And what are the best strategies for dealing with the complexities of these two unique worlds? This dilemma is what today’s IT leaders are up against.

To download the latest edition of What Works and the bonus white papers, click the "Submit" button. White papers are provided by our sponsors.