A Message from Doug Henschen, Executive Editor, InformationWeek
The big deal with big data isn't in collecting huge amounts of information. It's in putting that information to use in ad hoc analyses, what-if scenario planning, customer sentiment analysis, and accurate predictive models, and machine-learning-derived indicators of fraud, risk, and customer behavior. New platforms, tools, and technologies for big data are opening up incredible possibilities, but data- and business-analysis teams must be prepared to make use of that information.
Which technologies and infrastructure choices for big data are gaining adoption and why, with options ranging from frameworks like Hadoop and NoSQL databases to cloud-based services based on these platforms.
How use of big data is changing approaches to model building and algorithm development, with data-driven machine learning methods gaining influence.
Which new types of analyses and algorithms are emerging based on big data, such as the use of unstructured data (think social networks) and machine data (think sensor data, log files, and clickstreams).
What companies are doing about the shortage of expertise, with details on analytics and data-science degree programs and promising internal-development programs.
How information-management and data-analysis teams are working together to incorporate new platforms and gather big data insights.
What's ahead in big data platforms and big data analysis -- from real-time analysis to large-scale visual analysis and what-if scenario planning.