Enterprise Management Associates (EMA) has recognized that big data implementers and consumers rely on a variety of platforms to meet their big data requirements. These platforms include new data management technologies such as Hadoop, MongoDB, and Cassandra, but the collection also includes traditional SQL-based data management technologies supporting data warehouses and data marts; operational support systems such as customer relationship management (CRM) and enterprise resource planning (ERP); and cloud-based platforms. EMA refers to this collection of platforms as the Hybrid Data Ecosystem (HDE):
When I started out in security, only very large organizations with a mature set of business processes dared to talk about implementing some form of governance, risk, and compliance (GRC) or enterprise program (e-GRC). They generally did it in an attempt to get ISO or similar certification, or to “move their programs to the next level,” and some, I think, attempted it just to prove they did it. Many of those efforts were monumental, costing millions of dollars and taking years to complete. However, a significant number seemed to end in compromise, yielding a smaller end result or totally failing after thousands of man hours and millions of dollars for software, systems, and consulting had been spent.
Waaay back in the day (say 2002), organizations would ask themselves:
As I review my series of #100linesOnBIDW blogs over the last couple of weeks, I found myself looking at the Data Management posting. I covered when to apply schemas, Big Data, and data governance. What I left out was technical implementation concepts for data management systems like row vs. column orientation; in-memory vs. spinning disk primary storage; and symmetric multiprocessing (SMP) vs. massively parallel processing (MPP). Processing and storage were the “developments” of 2012. I left 2013 for the “how to use” Data Management platforms.