Big Data Analytics

A typical Big Data cycle is to Acquire, Ingest/Clean, Store/Manage, Featurize/Wrangle, Visual Analytics/Interactive Queries, Modeling and finally, Story Telling.


Essentially, it is the use of cloud computing to provide a powerful computing platform to allow large quantities of data to be analyzed, reducing it to actionable information that can be used to improve decision making process. Together, cloud, mobility, advanced analytics and new data visualization techniques helps unlock the power of data to drive more informed decision making and, in the case of Real Estate investments, identifying alpha return prospects within the available spectrum of opportunities. It provides real-time visibility of critical indicators, that when mapped across the risk-return dynamics, can facilitate instant actionable/investable decisions.


Big Data is also about building in the experience, and know-how, of the user into the various analytics model to create true knowledge and wisdom based upon their experience. It is a fundamentally different model than Data Warehousing – which assumed you knew what questions you wanted answered so you could define a set of data items that could be extracted, typically nightly, weekly or monthly, from all available operational databases on which to run reports. Big data expands the model to include all operational data and potentially additional data from sensors, remote devices, audio and video streams. Hence the use of Big Data analytics covers not just an organization’s data but often requires additional context data to help to analyze patterns in the data.


The other key characteristic of Big Data is that it is open to everyone. Gone are the days when the organizations had to run their entire business on a few management reports designed for higher management, with the rest of the organization staff working on data that “trickled down” from these reports and captured in Excel spreadsheets. Big Data and Cloud Computing support the premise that everyone should have access to the specific data they need to do their jobs, not just the traditional financial reports produced by legacy ERP systems, but actual information across platforms from disparate sources that they need for their informed decision making.


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