November 12, 2013
By Mark Madsen
Data strategy focuses on how data can be used as a resource to further the goals of a business strategy. This means building capabilities: treating data as an asset, organizing to make better use of it, and building the necessary management and technology infrastructure. There are many ways to build capabilities. Choices impose constraints and trade-offs, which are the essence of crafting a set of policies, procedures, and plans that make up a data strategy. This Ten Mistakes to Avoid focuses on many common mistakes we make when crafting a data strategy.
October 1, 2013
This Best Practices Report examines how organizations are leveraging their big data assets, the challenges they face, future trends in user practices and vendor tools, and 10 priorities for the years ahead.
Sponsored By Cloudera, Dell Software, Oracle, Pentaho, SAP, SAS
September 17, 2013
This TDWI Checklist Report takes a look at text analytics and how to get started with this new technology that can help you improve and gain new insight.
Sponsored By Angoss, Lexalytics, SAS
September 16, 2013
In this issue, you'll find out why big data is the fourth generation of data management and learn how to maximize insight from unstructured data. Plus, our experts look at real-time data warehousing, the future of master data management, data governance, and other timely, trending topics. In addition, the TDWI Best Practices Awards winners are announced.
July 22, 2013
This TDWI Checklist Report drills into the many technologies and capabilities needed to make operational intelligence possible for a technology team and successful for a business.
Sponsored By Splunk
July 22, 2013
This TDWI Checklist Report discusses adjustments to DW architectures that real-world organizations are making today, so that Hadoop can help the DW environment satisfy new business requirements for big data management and big data analytics.
Sponsored By Teradata
June 17, 2013
This TDWI Checklist Report discusses how organizations can achieve greater agility with data quality projects through adjustments to data stewardship, business processes, and technical development methods. The report also looks at critical success factors for agile
data quality, such as tool features, team structures, self service, data-driven documentation, and data services.
Sponsored By Trillium Software