La vida es corta – life is short. A
very simple, yet powerful saying. So, life is short for what? Short to waste it
away? Short not to do what you want to do? Short to be unhappy? Short to not
give everything a go?La vida es corta can be adaptable
and applied to everyone, in each individual way. As Steve Jobs once said to‘live everyday, as if it was your last’. Too many times have I looked back with
regret, not having done something that I wanted to do, not pushing for an idea
to be heard, not trying out new things or new ways/approaches. So how can this be applied to Campus Recruitment? The world we live in is dynamic, constantly changing – so does the way we go about recruitment. How we source, the platforms we use, how we reach out to various communities, the various mediums we use. As these variables keep changing, so do we when recruiting – so when we have a new idea, a new approach a new way of thinking, lets not hold back and regret not voicing it, as we do not know if it will have worked. Because life is short not to.
Mustafa Kaya is a Senior Consultant in Oracle Fusion Middleware Team, living in Istanbul. 
The growth of Data Science as a discipline is attributable to the availability of new sources of data and to the increased focus on incorporating analytical outputs into every day decision-making within enterprises. Data Science brings together the hitherto disparate worlds of programming, statistical learning and data management. In my team, we have long recognized that financial institutions need a single analytics platform that brings together data management, statistical modeling and business application development capabilities, not because we foresaw the emergence of data science, but because our approach was developed in response to real-world challenges faced by Financial Institutions. A few enterprise-level modeling related challenges we observed at financial institutions are listed below.
functionally rich modeling platform. At the enterprise-level, however, an analytics platform should be more than a statistical package. The R platform, by itself does not provide the full data management and governance capability desired by banks and required by regulators – data lineage, auditability and security are not what the R platform is architected for. The R platform also does not provide model management and model deployment capabilities, nor does it enable model outputs to be integrated into applications easily. The solution must bring together the IT, app developer, data architecture and modeling worlds using a unified, metadata-driven, toolset. 
As today’s financial institutions seek to become more analytics driven, modeling cannot survive as an island within the institution. While a plethora of point solutions have been introduced into the market to meet specific analytical needs – from big data processing to visualization – financial institutions need a more strategic approach to modeling; one that is built, developed and deployed on a unified platform. Models have been and will continue to be core assets that are essential to managing financial performance, risk & capital, and customer relationships in the financial services sector. Firms can gain control of their modeling environment and integrate modeling with the fabric of enterprise-wide business intelligence with the right enterprise analytics approach. 
A Guest Post by Rick Jewell, Senior Vice President of Applications Development, Oracle (pictured left)
Benefits: