New TDWI Research Report Explores How Organizations Can Accelerate Insights from Data
Report explores how organizations can accelerate speed to insight for competitive advantage.
SEATTLE, WA, January 14, 2020— TDWI Research has released its newest Best Practices Report:Faster Insights from Faster Data.This original, survey-based report looks at the many competitive advantages enterprises can derive by analyzing a growing volume and variety of data sooner to meet daily business requirements. It explores why organizations need to modernize technologies and practices to eliminate bottlenecks that exist between data’s initial creation and collection and its consumption by humans, applications, and AI programs.
The author of the report, David Stodder, is senior director of TDWI Research for business intelligence. He explains that fact-based, data-driven decision making is key in today’s market, which drives the need for speed in analytics. “If an insurer or government healthcare agency can use predictive models to detect a fraud scheme before it has a chance to do significant damage, it can save costs and avoid public embarrassment.” A retailer, he explains, “can analyze data to uncover a trend in customer buying preferences before other retailers and gain an edge, potentially delivering higher market share and profitability.”
The report examines experiences, practices, and technology trends that focus on identifying and removing latencies in data’s life cycle to extract more timely insights. Stodderexplains how organizations are using innovative technologies such as AI (including AI-driven augmentation), data streaming, and real-time analytics as well as newer ways of integrating, processing, preparing, and transforming data. The report also delves into the role of agile, DataOps, and other methods for organizing analytics projects and moving faster to create value.
This comprehensive report reveals:
- 10 recommendations and best practices for developing a strategy to reduce delays in data integration and management and increase business value through faster analytics
- The role of an analytics culture in nurturing good practices in using data and analytics to support decisions and increasing data literacy throughout an enterprise
- About half of organizations surveyed (52%) either currently or plan to use AI to augment BI/analytics and support data integration and management so they can automate discovery of actionable insights
- About two-thirds of organizations (67%) are interested in improving actionable information by investing in technologies for “fast batch” updates and delivery of true real-time data, including data streams
- Although not yet used by the majority of organizations, data streaming and real-time analytics are becoming more mainstream, especially through implementation of open source Apache Kafka and Spark
- Over three-quarters of respondents (78%) say agile is the most popular methodology for BI, analytics, AI, and/or data integration and management projects; 40% are using DevOps practices, which extend agile methods to increase automation for faster and more repeatable software development
- Organizations are intent on applying new technologies to improve data preparation, transformation, virtualization, and pipeline development so they can eliminate delays in providing complete and trusted views of and access to data
- Increasing self-service BI and analytics capabilities continues to be highly important to nearly all organizations, but user satisfaction shows room for improvement; many organizations are also concerned about how to balance self-service with governance demands
- One-third of respondents (34%) say their organizations are somewhat successful with adaptability -- important in analytics workflows because users may want to add new data sources easily and quickly
The report includes a survey of technologies and cloud services in use (including data catalogs and glossaries; real-time processing; in-memory analytics; automated data classification; datavirtualization; data transformation; pipelines; machine learning; on-demand, self-service, and embedded analytics; and data streams). It explores how, in a data-driven world, enterprises can reduce the latency between when data is created and collected and when it can be used for analytics.
This research was sponsored byAscend.io, Denodo, Wyn Enterprise by GrapeCity, Matillion, and SAS.
About the Author
David Stodder is senior director of TDWI Research for business intelligence. 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 AI for BI, cloud analytics, data visualization, customer analytics, big data analytics, design thinking, and data governance. He has chaired TDWI conferences on visual BI and analytics, business agility, and analytics. Stodder has provided thought leadership on BI, information management, and IT management for over two decades. He is an industry analyst, having served as vice president and research director with Ventana Research, and he was the founding chief editor of Intelligent Enterprise and Database Programming & Design. You can reach him at firstname.lastname@example.org, @dbstodder on Twitter, and on LinkedIn at linkedin.com/in/davidstodder.
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