Digital transformation as a data-centric service
Is digital transformation something you can just buy into? No, but it is a data-centric process, and having the right products and people in place makes all the difference, according to Stratio.
Read More →Googling prescriptive analytics: YouTube recommendations and the analytics continuum
Understanding prescriptive analytics is complicated, let alone implementation. Would turning to Google help? Seeing how it works for Google raises questions, but may also lead to some answers.
Read More →Big Data, Crystal Balls and Looking Glasses: Reviewing 2016, predicting 2017
End-of-year reviews are boring — and everyone does them. Predictions are boring — and they are hard. Of course, this is different — because big data.
Read More →Error – data not found: Precarious data and architectures of the future
Can data from organizations as prominent as NASA vanish into thin air? A grass-roots initiative lead by scientists and researchers believes it just might, and is doing everything in its power to prevent this.
Read More →Why finance craves big data: A perfect storm of disruption and opportunity
The financial services domain, where real-time is measured in milliseconds, holds particular interest for big data applications and vendors for a number of reasons: architecture, regulation, transparency, decision-making, and the need for speed.
Read More →Predictive analytics and machine learning: A dynamic duo
Predictive analytics and machine learning are seen as the pair of tools to save the day for most organizations currently. We try to de-mystify both, taking a look at what they are, how they work, and what they are good for.
Read More →Birst-ing into mainstream: Machine Learning meets Semantics in a networked world
Birst is one of the poster children of self-service analytics. Convergence and democratization are the key themes underlying Birst's new release out today, as Birst is trying to balance self-service with enterprise requirements, and making a case for some of the industry's defining trends while at it.
Read More →Data to analytics to AI: From descriptive to predictive analytics
Artificial Intelligence (AI) seems to be the buzzword du jour for organizations, but this is not an obvious or straightforward transition even for those building advanced products and platforms. Many organizations are still struggling with digital transformation to become data-driven, so how should they approach this new challenge?
Read More →Out of the Hadoop box: SQL everywhere and AtScale
AtScale has made a name for itself by providing an access layer on top of Hadoop that enables it to be used directly as a data warehouse. AtScale is now announcing support for Teradata DW and Google Dataproc and BigQuery, offering what it calls a Unified Analytics Platform. Why this move now, how does it work and what does it mean?
Read More →From Brexit to Trump: How organizations can use data to prepare for and respond to political events
Politics and data do mix, apparently, so we try to count the ways.
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