I recently attended SAS Institute’s analyst relations conference. There the company provided updates on its financial performance and its Viya platform and a glimpse into some of its future plans.
Topics: Big Data, data science, Mobile Technology, cloud computing, business intelligence, Data Governance, Data Integration, Data Preparation, Internet of Things, Information Optimization, analytics, Machine Learning and Cognitive Computing, Mobile Big Data, Machine Learning Digital Technology, Collaboration for Business
Cloudera provides database and enabling technology for the big data market and overall for data and information management. As my colleague David Menninger has written, the big data and information management technology markets are changing rapidly and require vendors to adapt to them. Cloudera has grown significantly over the last decade and now has approximately 1,000 customers and provides support and services in countries around the world. Its product and technology strategy is to provide a unified data management platform, Cloudera Enterprise, that can meet the data engineering and science needs for a range of analytic and operational database applications. Its primary focus is its Enterprise Data Hub, which as a data lake can handle organizations’ big data and analytical needs. As David Menninger asserts, the data lake is a safe way to invest in big data. It also helps shift the focus away from the V’s (volume, velocity and variety) of big data to the A’s, which are analytics, awareness, anticipation and action.
Topics: Big Data, data science, Machine Learning, cloud computing, business intelligence, Data Governance, Data Integration, Data Preparation, Internet of Things, Cognitive Computing, Information Optimization, analytics, digital technology
Big data initially was characterized in terms of “the three V’s,” volume, velocity and variety. Nearly five years ago I wrote about the three V’s as a way to explain why new and different technologies were needed to deal with big data. Since then the industry has tackled many of the technical challenges associated with the three V’s. In 2017 I propose that we focus instead on a different letter, which includes these A’s: analytics, awareness, anticipation and action. I’ll explain why each is important at this stage of big data evolution.
Big data has become an integral part of information management. Nearly all organizations have some need to access big data sources and produce actionable information for decision-makers. Recognizing this connection, we merged these two topics when we put together our recently published research agendas for 2017. As we plan our research, we focus on current technologies and how they can be used to improve an organization’s performance. We then share those results with our readers.
Topics: Big Data, data science, Data Governance, Data Integration, Data Preparation, Information Management, Internet of Things, analytics, Machine Learning and Cognitive Computing, Machine Learning Digital Technology
The business intelligence market is bounded on one side by big data and on the other side by data preparation. That is, to maximize their performance in using information, organizations have to collect and analyze ever increasing volumes of data while the tools available are constantly evolving in the big data ecosystem that I have written about. In our benchmark research on big data analytics, half (51%) of organizations said they want to access big data using their existing BI tools. At the same time, as I have noted, end users are demanding self-service access to data preparation capabilities to facilitate their analyses.