The leading benefits of implementing real-time technologies for data, BI, and analytics include better actions based on BI and analytic information, improved customer service and customer experience, and automated decisions made by software, not people.
Seattle, WA (PRWEB) October 11, 2014
TDWI Research announces the release of its newest Best Practices Report, Real-Time Data, BI, and Analytics: Accelerating Business to Leverage Customer Relations, Competitiveness, and Insights. This original, survey-based research introduces readers to real-time data, business intelligence, and analytics terminology and technology, how (and by whom) they are used, and the benefits and barriers to their use. The report drills down into real-time and right-time BI, including best practices, business drivers, and how applications make use of real-time and near-real-time data.
“The leading benefits of implementing real-time technologies for data, BI, and analytics include better actions based on BI and analytic information, improved customer service and customer experience, and automated decisions made by software, not people,” the report explains, noting that barriers to achieving these benefits “include cost, difficulties in designing real-time systems, the state of data management infrastructure, and inadequate staffing or skills.”
The report was written by three directors from TDWI Research: Philip Russom, David Stodder, and Fern Halper. It includes the results of a TDWI survey of 365 respondents (IT professionals, consultants, business sponsors, and business users), plus telephone interviews with technical users, business sponsors, and recognized data management experts.
“This term real time has become an umbrella concept encompassing multiple time frames, speeds, and execution frequencies,” the authors point out. They examine the diversity of real-time operations, including the enabling technologies enterprises use.
The report looks at:
- Three broad layers of real-time technology and how they interoperate: real-time data, real-time BI, and real-time analytics
- The fuzzy terminology used to describe real-time analytics
- Common use cases for real-time technologies and practices
- Benefits and barriers for real-time data, BI, and analytics
- Benefits of effective real-time practices and barriers to success
- Real-time data, including data refresh rates, available technologies (such as data replication, data federation, and data virtualization), columnar DBMSs, Hadoop, complex event processing, in-database analytics, and in-memory databases
- Best practices for real-time data, including streaming data
- The important distinctions between real-time and right-time BI, plus real-time BI business drivers and the variety of refresh rates
- The business case and status of real-time analytics use, including stream computing and mining
The authors examine the many sides of real-time data, including data refresh rates, available technologies (such as data replication, data federation, and data virtualization), columnar DBMSs, Hadoop, complex event processing, in-database analytics, and in-memory databases, massively parallel processing, and solid state drives. They also provide insights into future trends in real-time data, BI, and analytics use.
This research was sponsored by Actian Corporation, Cloudera, Inc., Datawatch Corporation, HP Vertica, IBM, Tableau Software, Treasure Data, Inc., and WebAction, Inc.
About the Authors
Philip Russom is a well-known figure in data warehousing and business intelligence, having published more than 500 research reports, magazine articles, opinion columns, speeches, Webinars, and more. Today, he’s director of TDWI Research for data management at The Data Warehousing Institute (TDWI), where he oversees many of the company’s research-oriented publications, services, and events. Before joining TDWI in 2005, Russom was an industry analyst covering BI at Forrester Research and Giga Information Group. He also ran his own business as an independent industry analyst and BI consultant and was a contributing editor with leading IT magazines. Before that, Russom worked in technical and marketing positions for various database vendors. You can reach him at @prussom on Twitter and on LinkedIn at linkedin.com/in/philiprussom.
David Stodder is director of TDWI Research for business intelligence at The Data Warehousing Institute (TDWI). He focuses on providing research-based insight and best practices for organizations implementing BI, analytics, performance management, data discovery, data visualization, and related technologies and methods. He is the author of TDWI Best Practices Reports and Checklist Reports on data discovery, data visualization, customer analytics in the age of social media, BI/DW agility, mobile BI, and information management. He has chaired TDWI conferences on BI agility and big data analytics. Stodder has provided thought leadership on BI, information management, and IT management for over two decades. He has served as vice president and research director with Ventana Research, and he was the founding chief editor of Intelligent Enterprise, where he served as editorial director for nine years. You can follow him on Twitter at @dbstodder.
Fern Halper is director of TDWI Research for advanced analytics, focusing on predictive analytics, social media analysis, text analytics, cloud computing, and other big data analytics approaches. She has more than 20 years of experience in data and business analysis, and has published numerous articles on data mining and information technology. Halper is co-author of "Dummies" books on cloud computing, hybrid cloud, service-oriented architecture, service management, and big data. She has been a partner at industry analyst firm Hurwitz & Associates and a lead analyst for AT&T Bell Labs. Her Ph.D. is from Texas A&M University. You can follow her on Twitter: @fhalper.
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