Traditional data warehousing has failed to keep pace with the ever-changing information needs of the business, according to Rosslyn Analytics CEO Charles Clark.
He contends that the greatest data challenge facing all organisations is delivering relevant and high-quality data to decision-makers.
To address this challenge, Rosslyn Analytics has developed a dynamic big data cloud analytics platform which is powered by Azure.
The RAPid Big Data Cloud Analytics Platform offers a combination of self-service data integration, cleansing and enrichment tools, and machine learning and visualisation technologies, to enable business and IT users to directly interact with, change and analyse data.
RAPid is supported by SQL and Hadoop, and can leverage data services and applications available on the Azure Marketplace such as Hadoop Insights, Python and R.
The platform’s architecture makes it possible for business users to search for and create new sources of information and views, while also allowing IT teams to develop, test and deliver new analytics apps rapidly. These apps can then be enhanced by collaboration with business users.
The platform can also process structured and unstructured data in one layer, allowing users to query, cleanse, enrich and analyse their data with what Rosslyn Analytics says is an unprecedented level of accuracy and sophistication.
The RAPid Platform is built to process both structured and unstructured data in one layer, allowing users to query, cleanse, enrich and analyze their data with the level of accuracy and sophistication that has not been possible to date. This technical feat enables IT teams to support both operational and analytical apps on one platform, significantly reducing costs while accelerating value creation from data.
“Though valuable, simply having a visualisation tool to analyse data isn’t enough today,” said Garth Fort, general manager of the Cloud and Enterprise Partner Group, Microsoft. “Rosslyn Analytics is utilising Azure to disrupt the business intelligence market built on traditional data warehousing architecture.”