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data analysis environment without spreading the problem out across a team of servers . In-database analytics yield a host of benefits over traditional analytics including : Faster analytics , on a scale of 10- 100x faster than traditional analytics Better analytic models as data scientists can now use full datasets versus sampling No data duplication errors caused by moving data between servers Stricter security policy enforcement , particularly for industries that regulate the movement of sensitive business data Near real time insights , as opposed to analytic insights that may be days or weeks old Capex reduction by eliminating the need for additional hardware servers to process the analytics Pervasive analytics that can flow freely to reporting tools and applications throughout the enterprise
It ’ s data science , not rocket science As data has grown , so has the role of the ‘ data scientist ’ to that of an Atlas of analytics . The data scientist , according to legend , is intimate with machine learning and statistics , can program in low-level languages ( often with one hand ), is a data domain expert and is an artist where data visualisation is concerned . He or she can tease insights from otherwise unrecognisable patterns and , in some cases literally , can predict the future . Not surprisingly , this mythical unicorn comes with a commensurate price tag .
The problem with this model , beyond cost and scarcity , is a tendency to isolate innovation . In-database analytics solves this problem by reducing the complexity of analytics so that teams of ‘ regular ’ data analysts can access and analyse data using familiar SQL queries . This essentially democratises data led discoveries in the enterprise and , to the relief of HR departments everywhere , eliminates the need to slather themselves in unicorn perfume just to attract the right talent .
That ’ s nice , but who cares ? In-database analytics isn ’ t ideal for everyone . For example , some of the newer business cases for Big Data that require analysis of large sets of unstructured data are better served by tools designed specifically for those scenarios . But any business where large amounts of structured data need to be analysed quickly can benefit from in-database analytics . Good candidates for in-database analytics include : Healthcare organisations that need to analyse large amounts of patient data securely Financial services companies that can benefit from real time decision making in their investment strategies Retail corporations that need to improve supply chain logistics or analyse product performance in a dynamic environment
Over the next 18 months , we expect to see more enterprise analytics forego the data server farms of the past and move into the data warehouse . Higher performance and lower cost are the most important drivers for the move home , but there are other benefits
All signs point to in-database analytics becoming the next ‘ in ’ thing for Big Data .
to consider . As already cited , indatabase analytics allow enterprises to use what they have today in terms of IT skills and infrastructure while dramatically improving their analytic capabilities . Also , the relative simplicity of in-database analytics allows enterprises to experiment more with their data analysis and test hypotheses that would have been impractical or excessively expensive in a traditional setting .
At least from our perspective , all signs point to in-database analytics becoming the next ‘ in ’ thing for Big Data .
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