Navigating the Predictive Analytics Market
Predictive analytics is on the verge of widespread adoption. Enterprises are extremely interested in deploying predictive capabilities. In a recent TDWI survey about data science, about 35 percent of respondents said they had already implemented predictive analytics in some way. In a 2017 TDWI education survey, predictive analytics was the top analytics-related topic respondents wanted to learn more about.
August 1, 2017
Use Big Data Analytics and Geoenrichment to Drive Better Business Outcomes
The volumes of data and speed at which data is produced continually increases on an exponential scale. Consumer transaction data, client records and data in motion from mobile devices, IoT sensors and other sources usually contains associated geographic coordinates that require geospatial processing to extract value. With the volume and variety of this data, organizations need to have a location strategy that includes big data technology that can join disparate data sets (geoenrichment) and perform location analytics to reveal actionable business and operational insights.
August 15, 2017
Get More Business Value from a Data Lake via Data-as-a-Service (DaaS)
Data lakes are coming on strong as a modern and practical way of managing the large volumes and broad range of data types and sources that enterprises are facing today. TDWI sees data lakes managing diverse data successfully for business-driven use cases, such as omni-channel marketing, multi-module ERP, the digital supply chain, and data warehouses extended for business analytics. Yet, even in business-driven examples like these, user organizations still haven’t achieved full business value and return on investment from their data lakes.
August 16, 2017
Making Multiplatform Data Architectures Work for You: Common Use Cases and Reference Architectures
To leverage the new wave of advanced data sources available, users and architects are turning to a multiplatform data architecture (MDA), where numerous diverse data platforms and tools are integrated in a multiplatform, distributed architecture. An MDA is typified by an extreme diversity of platform types that may include multiple brands of relational databases, NoSQL platforms, in-memory functions, and tools for data integration, analytics, and stream processing. Any of these may be on premises, in the cloud, or in hybrid combinations of the two.
August 17, 2017