The wider, more generally used, definition of big data does not have to rely on the newer, non-relational database technologies. As the term big data becomes more commonly used, it seems that the meaning of big data morphs beyond the stricter technical definition to include any large scale data analytics effort. Those that are communicating from this perspective are not necessarily worrying about whether newer technologies such as Hadoop and NoSQL databases are being used, but are rather focused on leveraging large amounts of data, or real-time access and response to these data.
Many media articles do not discuss the use of these newer technologies. This is not a criticism but rather meant to reinforce the notion that the general media does not typically delve into the nuances of big data. Instead it focuses on the realization that there is tremendous value in the volumes of data that are available today.
Consider an organization with a 10 million patient relational database; perhaps they are storing these data in a traditional relational database to mine the terabytes of data by applying analytical tools on fields in the database to predict readmissions. The technical solution does not rely on anything other than conventional techniques but this still fits the generalized notion of a big data problem. Similarly, as point of care monitoring devices come on line in more healthcare delivery settings, the transmission, processing, storage, and access to the data also fall into the concept of a big data problem. In this case, it’s the near-real-time analysis of the data that might provide early warning of adverse outcomes.
For some HIMSS members, the wider definition of big data is nothing new and they have had data intensive efforts in place for decades that rely on sophisticated data warehouses and related technology. For these organizations, “big data” is already part of the fabric of their organizational infrastructure.
For other, smaller HIMSS organizations, the journey towards leveraging big data has just begun. These organizations can still benefit from the wider view of big data by instituting data focused efforts. Some of those efforts might rely on the time tested relational and star schema database (i.e., data warehouses) approaches while others might necessitate the use of newer technologies like Hadoop.