The data lake may be a fashionable concept, but it is also one born of necessity. The traditional data warehouse approach cannot handle the vast quantities of data now being produced.So, rather than trying to transform all data before loading (like the data warehouse), the principle of the data lake is to reverse these two steps.
With the vast amounts of health data available today, health analytics can provide insights into what has happened (reporting), what will happen (predictive analytics) and what should be done right now (prescriptive analytics).
Big data goes beyond precisely defined data sets. Instead of purely employee-entered data, for example, big data extends to or constituent comments on social media, third-party economic forecasts, machine sensor outputs and any other data source that has a bearing on questions being asked or issues to be resolved. Analyzing big data to reveal patterns, trends and likely outcomes can take organizations far beyond the limits of “normal” data processing to improve decision making and efficiencies.