Staying agile: data-driven IT operations
Would you like to have an end-to-end picture of your IT operations, but are lost in translation among a myriad monitoring solutions and metrics? Your monitoring should be as agile as your operations, and OpsDataStore says it can help you get there.
Read More →Streaming hot: Real-time big data architecture matters
What is streaming in big data processing, why you should care, and what are your options to make this work for you?
Read More →Mix and match analytics: data, metadata, and machine learning for the win
Creating winning analytics solutions means combining and making the most of different approaches and techniques. Taking a look at how Google does this for YouTube can provide inspiration and set a framework for analytics solutions.
Read More →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.
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