To manage complex data and do data analytics, during the 1990s, the use of Relational Database Management systems (RDBMS) was very popular. However, as the volume data increased and storage became cheaper, there was an increase need for reporting in the 2000s. Therefore, in addition to RDBMS, Data-Warehousing (OLAP/BI) became another component of the data management systems. In 2010, we began to see an increasing number of data transactions that resulted in the introduction of big data to the current data management system. The data transactions were becoming error prone and to reduce error, increase flexibility and reduce costs, Big data systems were added to the RDBMS, and Data-warehousing. Now some companies may only have one the the 3 technologies in place.
In Healthcare, the use of big data systems has partially been introduced, so the use of pre-2010 technology to run reports and queries based on the stored data is very popular. To get to the big data stage, organizations need to have to do risk-analysis to measure the benefits of the system compared to its costs. The cost of big data system outweighs the its benefits for RDBMS but as machine learning is introduced, the equation will change. Big data system offers less error processing when the data processing becomes impossible for RDBMS and OLAP/BI engines.